Patent Application
20140335490
The present invention relates to automated systems and methods for understanding and assisting in human behavior, and more particularly to automated systems and methods for collecting a variety of user-related data and providing feedback to the user.
In the past, many have attempted to collect and gather information to aid users in understanding health, behavior and various conditions. These systems have been limited in scope and capabilities, as they have not, among other things, adequately addressed the need to change the behavior of the users. Many systems have been designed to monitor user data and report on specific details. As a result, these types of systems have met with only limited commercial success.
The most typical form of behavior that consumers seek to understand and control is that of health or weight. Health monitoring devices typically seek to measure caloric expenditure, so that a user may determine an appropriate diet based on typical caloric burn rates. Energy expenditure is typically measured by devices that utilize accelerometers to measure activity levels, then use calculations based of personal biological information such as height, weight, etc. These devices can approximate energy expenditure given these inputs, however, they are unable to determine caloric intake. To do this, a user must manually enter the food they have eaten into a database through a computer, smartphone, or other computing device connected to the internet. However, these entries may be prone to errors if a user forgets a meal or snack, if caloric content for a food is misrepresented, if a user cannot remember portion size, if a user purposefully omits information, or any other type of human error.
Currently known devices that measure activity levels cannot determine body composition on their own. Typically, they require a separate device that measures body composition and uploads that information to the Internet. Perhaps the standard method of measuring body composition is through Bio-impedance Spectroscopy.
Bio-impedance Spectroscopy refers to the complex impedance measurement between two points on the human body measured over a frequency range, typically from 3 kHz to 1 MHz.
These models suffer from a number of shortcomings. For example, they fail to take into consideration daily changes in hydration levels, stress levels, electrolyte levels, and body positioning. These factors can vary the measured FFM and FM by significant amounts, which present inaccurate data to the user.
One typical form of mood or emotional recording and analysis that is done today uses manual inputs wherein a user can tag a picture, article, event, or other online content with a response indicative of the user's mood. Additionally, devices may prompt users to answers surveys that are periodically presented to them by a web page, application on a smartphone, or even by devices that can be carried by a user to allow a user to input a mood or emotional state at various times. However, these devices fail to provide any mechanism for understanding the context of this data. In addition, these devices do not track the activities, physiological state, location, or other pertinent information along with the mood or emotion of the user. This means that the stimuli for a mood or emotion cannot be identified.
Some of the most typical forms of feedback sent by behavior modification systems are automatic messages sent to a user via a typically communication method such as email, text message, or a reminder alarm on a personal computer or cell phone. This automatic feedback is often configured directly by the user to alert the user at certain times. Additionally, data may be presented to a user from time to time such as how much sleep they have gotten, how many calories they have consumed, how much time they have spent with another person, group, or pet, or how much time they have spent performing certain activities. In doing so, these systems seek to modify a user's behavior by presenting pertinent information to the user in hopes that their actions may change. However, these prompts typically do not provide suggested changes in behavior that may result in a positive trend towards a user's goal.
Currently, most behavior tracking and modification devices use a substantial amount of user control to track and modify both desired and non-desired behaviors. For example, users may be required to set their own alarms and notifications, input their own data, and manually transfer information between devices to consolidate data. With complicated user-control systems, most behavior modification devices and systems require too much thought and too much effort by the user. Because of this, users are very conscious of the behaviors they are trying to modify. The more aware a user becomes of how behaviors are being tracked and recorded, the more likely they are to try and get around notifications and essentially try to cheat the data, even when they may be the only ones looking at the data.
In one aspect, the present invention provides a unique behavior modification system. The system generally includes a network of components that interact to collect various data and provide user feedback. In one embodiment, the network includes a personal device that is worn or carried by a user, an Internet-connected storage device and a hub that is capable of receiving communications from the personal device and communicating that data to the storage device. The personal device may be configured to uniquely identify the user and to collect data relating to the activity and body composition of the user. In one embodiment, the personal device includes one or more accelerometers for collecting data relating to physical activity and bio-impedance measurement circuitry for collecting data relating to body composition. In one embodiment, the Internet-connected storage device is coupled with one or more processors capable of interpreting data received from the personal device and providing feedback to the user.
In one embodiment, the behavior modification system is implemented in a network of components capable of collecting data, storing data, processing data, communicating data, receiving user input and providing user feedback. These various function may be implemented individually in single components or in combination in more complex components. The system may include essentially any components capable of collecting relevant data, such as data relating to the user and the user's activities or to environmental factors that might impact the user or otherwise be of use to the system. For example, data collecting components may include stand-alone sensors that function primarily to obtain and communicate data to other components. They may also include more complex devices that combine sensors with other types of system components, such as data storage and data processing components. In addition to sensors, the system may include input devices for entering data into the system. For example, a system component may include a touch screen, a keyboard or a mouse, or it may include one or more buttons, switches and other input devices. As another example, a three-axis accelerometer (and potentially other motion or orientation sensors) may be provided to receive input through user gestures. The system may include one or more storage units, such as local or network-based data storage units. Local storage units may include storage within a particular component, such as flash memory or other onboard storage in a sensor or a more complicated device. Network-based storage units may include a local hard drive or an Internet-connected hard drive (e.g. cloud storage) that receives and stores data from one or more system components. The system may include processors at various levels. For example, some components may include integrated processors for processing data and/or providing user feedback. The system may also include one or more centralized processors capable of collecting and analyzing data from one or more other components. The system may include algorithms capable of evaluating data alone and/or in combination to identify activities and events relevant to health and well-being. User feedback may be provided through visual means, such as lights, indicators and displays, or other types of output devices, such as tactile and audible devices.
In another aspect, the present invention provides a personal device for use in connection with a behavior modification system. In one embodiment, the personal device is a device that is capable of being worn by a user. For example, the personal device may be a wrist-band, a bracelet or an anklet. As another example, it may be device that can be carried in a user's pocket or clipped on the user's belt. In one embodiment, the personal device includes bio-impedance measurement circuitry, at least one accelerometer and a processor for determining energy expenditure based on data from the accelerometer(s). In one embodiment, the bio-impedance measurement circuitry may include an interior sensor configured to engage the user's skin beneath the device and an exposed sensor that can be placed in contact with the user's skin at a location remote from the interior sensor. For example, if the personal device is a wristband, one sensor may be located on the inside of the wristband to engage the user's wrist on one arm and the other sensor may be exposed on the outside of the wristband so that it can be placed in contact with the skin on the user's other wrist to provide an arm-to-arm bio-impedance measurement. In one embodiment, the personal device includes a three-axis accelerometer for collecting acceleration data relating to the physical activities of the user. The three-axis accelerometer may be supplemented or replace by other motion and orientation sensors. The personal device may include data storage for storing collected accelerometer data, such as onboard flash memory. In one embodiment, the processor is configured to determine the user's activity by analyzing data collected from the three-axis accelerometer. In one embodiment, the personal device includes a unique identifier capable of uniquely identifying the personal device to the behavior modification system. The unique identifier may be included with communications transmitted by the personal device.
In another aspect, the behavior modification system includes a unique hub that is capable of routing communications between various components within the system. In one embodiment, the hub includes a plurality of different transceiver that allows the hub to receive communication from components that operate using different communication protocols. For example, the hub may include WiFi, Bluetooth, Near Field Communications, ZigBee and/or other communications transceivers. To permit communications between devices of different protocols, the hub is configured to translate communications from one protocol to another. The hub may also be configured to implement a low-power behavior modification network. In this embodiment, the hub may include an RF transmitter capable of transmitting an RF signal capable of waking other network devices from standby mode. In one embodiment, the transceiver includes a router and protocol controller capable of receiving communications/data from another network component; convert the communication/data to the proper format for the target network component and sending the communication/data to the appropriate transceiver for transmission to the target network component.
In another aspect, the present invention provides a method for measuring bio-resonance. In one embodiment, the method includes the steps of measuring bio-impedance, measuring a factor capable of normalizing bio-impedance and normalizing bio-impedance using the normalization factor. In one embodiment, the method includes two normalizing factors—namely, hydration and user body orientation (e.g. sitting, standing or supine). In this embodiment, the method may include the steps of determining a user's hydration level, for example, using a hydration sensor, and normalizing the bio-impedance measurement to compensate for the determined hydration level. In this embodiment, the method may include the steps of determining the user's orientation, for example, using a three-axis accelerometer located at the hip of the user (and optionally or alternatively a magnetometer and/or other position or orientation sensors), and normalizing the bio-impedance measurement to compensate for the determined body orientation. In one embodiment, the method includes the steps of normalization for both hydration and body orientation, but the type and number of normalization factors may vary from application to application, and potentially from user to user.
In another aspect, the present invention provides a system and method for determining caloric intake. In one embodiment, the method includes the general steps of measuring an initial body composition of a user at a first time, measuring a subsequent body composition of the user at a second time, determining caloric expenditure during the period of time between the first time and the second time, and determining caloric intake as a function of the change in body composition and the caloric expenditure. In one embodiment, the step of determining caloric intake includes determining a number of calories corresponding to the change in body composition between the initial measurement and the subsequent measurement. In one embodiment, the behavior modification includes a personal device capable of inferring a user's caloric intake. In one embodiment, the personal device includes one or more sensors for measuring body composition, one or more sensors for measuring a user's physical activity and a processor configure to determine caloric intake as a function of the change in measured body composition and the measured user's physical activity. In one embodiment, the body composition sensor includes a bio-impedance sensor. In one embodiment, the physical activity sensor includes a three-axis accelerometer.
In another aspect, the present invention includes a network of components that are capable of entering standby mode to reduce power consumption and being woken from standby mode using an RF signal. In one embodiment, the system includes one or more components that are capable of entering a standby mode when inactive, as well as an RF receiver capable of receiving an RF wake-up signal even when in the standby mode. In one embodiment, the RF wake-up signal receiver circuitry is separate from any standby circuitry that may be incorporated into the communication circuitry. This allows the RF wake-up signal to be used to place the circuitry in an even lower power-consumption state than might be possible with just the conventional standby circuitry that is incorporated into some communication microcontrollers. In such embodiments, the RF wake-up signal receiver circuitry may provide an input to enable the communication circuitry. For example, the RF wake-up signal receiver circuitry may provide a high input to the enable input on a communication microcontroller. In one embodiment, the RF wake-up signal receiver circuitry includes an RF antenna and circuitry for determining when the wake-up signal has been received by the RF antenna. In one embodiment, the circuitry generally includes a filter, a peak-detector, an amplifier and a comparator. In this embodiment, the filter is configured to filter the output of the RF antenna and provide it to the peak detector. The peak detector may provide an output representative of the peaks in the filter signal. The output of the peak detector may be passed to the amplifier where it is amplified and output to the comparator. The comparator compares the amplified signal to a reference to determine whether an RF signal of sufficient strength has been received by the RF antenna. If so, the comparator output a wake-up signal, such as a high output. In one embodiment, the RF wake-up signal receiver circuitry may be combined with RF wake-up signal transmitter circuitry to provide an RF wake-up signal transceiver. In such embodiment, the circuitry may include RF wake-up signal receiver circuitry and RF wake-up signal transmitter circuitry that are alternately capable of being coupled to the RF antenna. In one embodiment, the RF wake-up signal transceiver includes an RF switch that can be selectively operated to connect the RF wake-up signal transmitter to the RF antenna to transmit an RF wake-up signal or to connect the RF wake-up signal transmitter to the RF antenna to receive an RF wake-up signal.
In another aspect, the present invention includes a personal device having bio-impedance circuitry that is reconfigurable to function as an alternative type of sensor. For example, in one embodiment, the bio-impedance circuitry may be reconfigurable to function as heart rate sensing circuitry. In this embodiment, the bio-impedance circuitry may include an excitation subcircuit for applying an electrical signal across a pair of sensors and a gain and phase detector subcircuit for extracting bio-impedance feedback across a second pair of sensors. In this embodiment, the bio-impedance circuitry may be configured to allow the excitation subcircuit to be disabled and a pair of the bio-impedance sensors may be used to provide a signal indicative of the electrical impulse of the user's heart to the circuit. The heart rate sensing circuitry may include a bypass subcircuit that allows the signal indicative of the heart rate to be fed directly to an analog-to-digital converter without passing through the gain and phase detection circuitry for the bio-impedance circuitry. As another example, the bio-impedance circuitry may be reconfigurable to function as circuitry for sensing skin salinity. In this embodiment, the bio-impedance circuitry may include an excitation subcircuit for applying an electrical signal across a pair of sensors and a gain and phase detector subcircuit for extracting bio-impedance feedback across a second pair of sensors. In this embodiment, the bio-impedance circuitry may include a bypass switch configured to create an electrical circuit between a single pair of adjacent sensors, whereby the electrical signal passes between the sensors through the user's skin, and a current sensor for sensing the current in the electrical circuit. In use, the magnitude of the current in the electrical circuit will be representative of the user's skin salinity. The salinity sensing circuitry may include a bypass subcircuit that allows the output of the current sensor to be fed directly to an analog-to-digital converter without passing through the gain and phase detection circuitry for the bio-impedance circuitry.
In one embodiment, the present disclosure relates to using a device or devices with an array of sensors and communication methods between devices and networks to track motions, locations, sense other nearby devices, and track various biometric data about a user. These devices work together to understand a user's body composition, activity levels, moods, habits, behaviors, and eventually a lifestyle. Specifically, the measured change in body composition over time when compared to energy expenditure over the same amount of time will allow the device(s) to determine caloric intake. Once these behaviors and lifestyles are identified, a central program can begin prompting the user through the same network of devices to begin changing their behaviors to meet target goals. Goals such as physical health, target levels of stress, time management, and relationship building/maintaining are first measured using empirical measurements, then analyzed either within the sensor devices or in a remote data collection machine or both, then prioritized based on correlation to the desired outcome, and finally an influence is injected to the users lifestyle. These influences may be warnings or reminders, displaying of data or results, or automatic changes to the device(s) within the network.
In one aspect, the present invention can utilize this data in conjunction with various components to systematically enhance or modify behavior in a wide variety of ways. This system combines the ability to monitor, interface, network, control and store data as well as analyze and recognize behaviors to further enhance this systems capability to assist a user in reaching personal goals.
The present disclosure seeks to overcome these and other disadvantages by providing an automated way to track and modify behaviors with very little human interaction and input.
These and other objects, advantages, and features of the invention will be more fully understood and appreciated by reference to the description of the current embodiment and the drawings.
Before the embodiments of the invention are explained in detail, it is to be understood that the invention is not limited to the details of operation or to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention may be implemented in various other embodiments and of being practiced or being carried out in alternative ways not expressly disclosed herein. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. The use of “including” and “comprising” and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items and equivalents thereof. Further, enumeration may be used in the description of various embodiments. Unless otherwise expressly stated, the use of enumeration should not be construed as limiting the invention to any specific order or number of components. Nor should the use of enumeration be construed as excluding from the scope of the invention any additional steps or components that might be combined with or into the enumerated steps or components.
A behavior modification system in accordance with one embodiment of the present invention is configured to assist a user in improving health and well-being, as well as other objectives that may be set by the user. In one embodiment, the system collects a variety of data and provides a user with feedback based on the collected data. Feedback may include simple feedback, such as reports on the tracked data, and it may include more complicated feedback, such as guidance or assistance in improving health and well-being based on determinations made from analysis of the collected data. In use, the system may collect a wide variety of data, including user data (e.g. biometric data, physiological data, physical activity), environmental data (e.g. temperature, location, sunlight, barometric pressure, elevation, noise level) and other data that might represent behavior, impact behavior or otherwise be relevant to one or more of the objectives of the system. The types of data collected may vary from application to application; however, a typical system may collect physiological and biometric data for the users, as well as data representative of physical activity, caloric intake, sleep patterns, human interaction, mood and physical location. The data may be collected, tracked, correlated and otherwise processed as desired to provide assistive feedback to the user. The user feedback may provide any of a wide-variety of type is data may be used to track activities and other factors that might relate to health and well-being. In addition these components can interface with building automation equipment, such as HVAC, lighting, and building security systems.
The behavior modification system of one embodiment of the present invention is implemented in the form of a network of components primarily capable of collecting data, storing data, processing data, communicating and providing user feedback. The behavior modification system of the present invention may include one or more devices with sensors or array of sensors and communication methods between devices and networks to track motions, locations, sense other nearby devices, and track various biometric data about a user. These components work together to understand a user's body composition, activity levels, moods, habits, behaviors, and eventually a lifestyle. For example, the measured change in body composition over time when compared to energy expenditure over the same amount of time will allow the components to determine caloric intake. Once these behaviors and lifestyles are identified, a central program can begin prompting the user through the same network of components to begin changing their behaviors to meet target goals. Goals such as physical health, target levels of stress, time management, and relationship building/maintaining are first measured using empirical measurements, then analyzed either within the sensor devices or in a remote data collection machine or both, then prioritized based on correlation to the desired outcome, and finally an influence is injected to the users lifestyle. These influences may be warnings or reminders, displaying of data or results, or automatic changes to the components within the network.
The system may include essentially any components capable of collecting relevant data, such as data relating to the user and the user's activities or to environmental factors that might impact the user or otherwise be of use to the system. For example, data collecting components may include stand-alone sensors that function primarily to obtain and communicate data to other components. They may also include more complex devices that combine sensors with other types of system components, such as data storage and data processing components. In addition to sensors, the system may include input devices for entering data into the system. For example, a system component may include a touch screen, a keyboard or a mouse, or it may include one or more buttons, switches and other input devices. As another example, a three-axis accelerometer (and potentially other motion or orientation sensors) may be provided to receive input through user gestures.
The system may include one or more storage units, such as local or network-enabled data storage units. Local storage units may include storage within a particular component, such as flash memory or other onboard storage in a sensor or a more complicated device. Network-enabled storage units may include a local hard drive or an Internet-enabled hard drive (e.g. cloud storage) that receives and stores data from one or more system components.
The system may include processors at various levels. For example, some components may include integrated processors for processing data and/or providing user feedback. The system may also include one or more centralized processors capable of collecting and analyzing data from one or more other components. The system may include algorithms capable of evaluating data alone and/or in combination to identify activities and events relevant to health and well-being. User feedback may be provided through visual means, such as lights, indicators and displays, or other types of output devices, such as tactile and audible devices
Further, these system components may use a range of recharging methods to maintain power. An inductive wireless charging using a charging base may be used, the components may be plugged into a wired charger, or the components may be able to recharge themselves through power harvesting.
As can be seen, this system of this embodiment combines the ability to monitor, interface, network, control and store data as well as analyze and recognize behaviors to further enhance the system's capability to assist a user in reaching the user's personal goals. In use, the present invention may systematically help to guide or modify behavior in any number of a variety of different ways to be described below.
In one embodiment, the behavior modification system generally centers around a personal device that is intended to be carried or worn by a user. The personal device creates a unique association between the user and other components of the system. As discussed in more detail below, the personal device may include any combination of sensors, data storage, communication circuitry, user interface, and processing units. For example, the personal device may be capable of collecting one or more types of data, storing data, processing data, communicating with other network components and providing user feedback. In one embodiment, data may be collected using sensors integrated into the personal device, entered into the personal device by a user or may be received through communication with other network devices. The personal device may be provided with an input device to permit the user to enter data into the personal device. The input device may be essentially any type of human input devices, such as a touch screen, buttons, switches, keyboards and other human interface devices. In embodiments that incorporate a hub, the personal device may also be capable of relaying data to and from other network components. For example, the personal device may be capable of collecting data from various network components, storing that data internally and then communicating that data to the hub when in range. Similar, the personal device may be capable of receiving communications from the hub, storing the communications internally and then transmitting those communications to other network components when in range.
In one embodiment, the personal device includes the ability to collect information about caloric expenditure. For example, the personal device may include an accelerometer for measuring user physical activity. As another example, the personal device may have communication circuitry to receive sensor readings representative of a user's physical activity from other components. As still another example, the personal device may include a user interface for accepting information entered by a user regarding physical activity.
In one embodiment, the personal device includes the ability to collect information about current body composition and changes in body composition at various times. For example, the personal device is capable measuring bio-impedance or bio-resonance (as discussed below), or both. A determination regarding Fat Mass and Fat Free Mass may be based on the body composition information. These measurements may be taken periodically or in response to an event.
The personal device, or a component within the system, may include the ability to utilize both body composition information and caloric expenditure to generate a caloric intake prediction. For example, by comparing energy expenditure to changes in Fat Mass and Fat Free Mass since these tissues are used by the body to store energy. By detecting a reduction or increase in stored energy, the system can determine that the user has expended more or less energy than consumed, respectively.
Turning now to the illustrated embodiment of
The personal device 510 in the illustrated embodiment of
A personal device according to one embodiment is shown in
Returning to
User's activities can be monitored to determine and recommend behavior modification opportunities.
The behavior modification method may further include tagging attitude, mood, or user condition in the context of the user's current state. Step 1422. For that state or condition, the method includes recording the difference or delta in each area and the areas of movement, angle, stage, time, and force. Step 1424. The method may also include asking questions to learn and define patterns. Based on the information gathered, the method may recognize a pattern and associate it with a learned tag. The method may include determining a behavior modification opportunity based on the recognized pattern. Step 1428.
The personal device 710 of the illustrated embodiment of
Bio-impedance measurements may be made across the arms and torso of the body when a personal device 5321 is worn on the wrist, as shown, for example, in
As shown in the illustrated embodiment of
The personal device 510, 610, 710 in the illustrated embodiments of
A. Body Composition Capabilities
In embodiments in which the personal device is capable of monitoring body composition, the personal device may include bio-impedance and bio-resonance measurement circuitry, as discussed above. An example of bio-impedance and bio-resonance measurement circuitry is shown in
B. Gestures
A personal device or component in accordance with one or more embodiments of the present invention may be capable of performing predetermined actions or activities in response to detecting and identifying a predefined gesture.
The illustrated embodiment of
In one embodiment, gesture or movement recognition may be used in conjunction with personal devices being worn by two different individuals. As described with respect to
C. Position or Speed Determination
Components of the behavior modification system, such as a personal device, may be capable of monitoring a user's activity and determining one or more of position and orientation of the user. Although described in connection with the personal device, the user's activity may be monitored and categorized by one or more components, including or not including the personal device, in the system. For example, the personal device may be used in conjunction with a separate accelerometer sensor worn or carried by the user.
By monitoring data from the accelerometer sensor, the personal device may be capable of determining whether the user is standing, sitting, or laying.
A method according to one embodiment is shown in
The graphs and information depicted in
The personal device may have essentially any type of housing. For example, the housing may be in the form of a wearable item, such as a wristband, bracelet, anklet or other similar item. As another example, the housing may be in form suitable for carrying or clipping to a user's clothing. In any event, it may be desirable to provide a housing that is water-resistant or waterproof.
As noted above, the personal device may be capable of communicating with separate sensors to collect data from those sensors. For example, a user may wear one or more sensors that are separate from the personal device and are capable of wirelessly providing data to the personal device.
In one embodiment, the behavior modification system is capable of predicting caloric intake based on one or more factors. The prediction processing may be located on any component within the system. For example, the prediction processing may be carried out by a processor located on the personal device. As another example, the prediction processing may be carried out by a processor located on a server on the Internet.
In one embodiment, the method for predicting caloric intake uses the change in body composition, U(t), along with the caloric expenditure, E(t). Equation 1 shows one calculation of caloric intake I(t):
U(t)+E(t)=I(t) (1)
There are a number of methods of obtaining information relating to change in body composition and caloric expenditure. A number of examples of obtaining U(t) and E(t) are described herein.
A. Energy Expenditure
E(t) is the energy or caloric expenditure of a user over a period of time. In one embodiment, E(t) can be calculated from the user's total activity along with other means of energy expenditure. In an alternative embodiment, the caloric expenditure could be input by the user or otherwise obtained as discussed herein.
One estimation of total E(t) over a defined period of time (shown in equation (1)) is comprised of basal metabolic rate (BMR), activity induced energy expenditure (AIE), the thermic effect of food (TEF), and non-exercise activity thermogenesis (NEAT). Total E(t) for an individual can be calculated by equation (2).
E(t)=BMR+AIE+TEF+NEAT. (2)
Since BMR is a clinical measurement that can only be measured while the person is completely stationary, The system may substitute RMR (resting metabolic rate) which has more tolerance for small movements while measuring. There are many equations that can be used to predict RMR. By comparing predictive equations for resting metabolic rate in healthy non-obese and obese adults, the RMR can be predicted. One equation that can be used to predict RMR is the Mifflin-St Jeor equation:
Men: RMR=9.99·weight+6.25·height−4.92·age+5 (3)
Women: RMR=9.99·weight+6.25·height−4.92·age−161 (4)
As part of equation (2) the system may calculate AIE. In one method of finding AIE, speed is a component. Speed of a moving person may be calculated based on certain physical characteristics and data collected by a 3-axis accelerometer. The speed may be calculated using equation (5):
The following variables from equation (5) are defined below.
H—Height (inches)
NC—Number of times person does cardio each week.
W—Weight (pounds)
VO
2=α1·S+β1·S·G. (6)
Another component of AIE is VO2, which is a measure of the rate at which a person's body uses or transports oxygen. Equation (6) from the American College of Sports Medicine (ACSM) can be used to estimate VO2. VO2 can be expressed in liters per minute, or as a rate per unit mass of the person such as milliliters per kilogram per minute. In equation (6) there are three parts, horizontal, vertical, and resting. Resting is left out for our purposes since it is address earlier. The horizontal portion is the first part of equation (6). The α1 term is constant, and S is the speed the person is moving in meters per minute. The second portion is the vertical piece where β1 is a constant S is speed, and G is the gradient of the hill.
Another way to estimate VO2 is identified below in Equation (7). Equation (7) may be implemented in one embodiment of the personal device:
VO
2=αn·S+βn·S·G+F(GP,A,S). (7)
The first part of equation (7) is similar to equation (6), however, the coefficients change depending on what segment of speed the user is moving at. If the user is walking, these coefficients are different from when the user is running. By collecting accelerometer data, the personal device can determine these coefficients to smaller speed segments, and may be able to fit them to a function based on speed as can be seen in equation (8) and (9),
αn=a·S+b, (8)
βn=c·s+d, (9)
where a, b, c, and d are constants. Substituting these equations into the first portion of equation (7) results in a multivariable polynomial equation (10):
VO
2
=a·S
2
+b·S+c·S
2
·G+d·S·G+F(GP,A,S)+ε, (10)
ε is an error term, and F(GP, A, S) is a function of genetic profile, age, and sex. This function can make the calculations specific to the user. Each user takes in a different amount of oxygen when working out, and according to the ACSM equations two people weighing the same will have the same VO2 levels. However, this is typically not the case. For example, an out of shape 130 lb male child will burn energy at a different rate than a 130 lb female marathon runner.
Equation (10) uses the following conversion equation (11) to calculate AIE. It is based on the premise that the average person burns 5 kcal per liter of O2.
The thermic effect of food (TEF) portion of equation (2) for calculating E(t) is based on the number of calories consumed in a day. An accepted approximation for TEF is given below in equation (12):
TEF=0.075·I(t) (12)
As for the non-exercise activity thermogenesis (NEAT) portion of E(t) in equation (2), NEAT is a fixed caloric expenditure value based on a person's lifestyle. Whatever is not quantified by the personal device from the AIE equation can be adjusted for with NEAT approximations using activity codes and Metabolic Equivalent Task (MET) intensities. If I(t) is unknown, the system may ignore the NEAT portion of the E(t) calculation.
B. Body Composition
U(t) is the change in energy stored (positive) or used (negative) by the body. This energy is stored either as Fat Mass or Fat Free Mass. One method for determining U(t) is based on bio-impedance spectroscopy, which is discussed in the background. In one embodiment, the U(t) determination may be based on bio-resonance, which is discussed herein.
In one embodiment, the system may include bio-impedance measurement circuitry.
Intracellular and extracellular water can be indicative of fat free mass and fat mass in a user's body. That is, in one embodiment, extracellular water and intracellular water provided by the Hanai model can be converted to FFM and subsequently FM. More specifically, extracellular water and intracellular water from the Hanai model can be combined to estimate an individual's total body water. Total body water may be converted to FFM using an empirical model. For example, one empirically determined model is FFM=TBW/0.73. Put another way, for a typical person total body water weight is about 73% of free fat mass. The estimation of free fat mass can be used to estimate fat mass by subtracting FFM from total body mass. Total body mass may be provided by a user or determined by a sensor in the behavior modification system.
Bio-impedance spectroscopy can be used to determine changes in body composition (e.g., weight loss).
Measurements taken using bio-impedance spectroscopy can be subject to short term variations due to a number of factors.
In one embodiment, bio-resonance includes improving the accuracy of bio-impedance spectroscopy. For example, bio-resonance includes adjusting the bio-impedance spectroscopy readings based on additional sensors indicative of the user's state. Information from the additional sensors may be used to normalize bio-impedance readings over time.
For example, a heart rate measurement may be taken before or after a bio-impedance or bio-resonance measurement to provide additional information to the component about the current state of the user. For example, a high heart rate may be indicative of strenuous activity of the user and can be used as a tag along with the Bio-impedance measurement. This can be used to normalize the Bio-impedance data if each measurement is correlated to the position and state of the user. For example, all of the measurements taken while the user's heart rate is elevated can be grouped and analyzed apart from all measurements taken when the user's heart rate is low.
Additional sensors may include, for example, a hydration sensor or a three axis accelerometer worn by the user. These sensors may provide additional information to more accurately predict the bio-impedance reading, which lead to increased accuracy in determining Fat-Free Mass (FFM) and Fat Mass (FM). The majority of FFM is made up of a conductive water-electrolyte solution, in contrast to FM, which is mostly composed of lipids that are generally non-conductive. Therefore, FFM can be estimated based on total body water (TBW). User hydration level can affect the measurement of TBW even without a change in FFM or FM because hydration level affects the conductivity of the electrolyte solution.
The same hydration level can affect the bio-impedance of two people differently. Specifically,
By using a hydration sensor, the measurement of TBW can be normalized to a nominal value. The hydration level of a user can be determine by monitoring the liquid or by measuring hydration levels directly. For example, by measuring the presence of sweat, the component can estimate hydration levels, since as a user sweats, their hydration state is lowered, increasing the electrolyte concentration within the body and lowering the measured TBW.
A fluid intake sensor may be utilized to track hydration level. In one embodiment, the fluid intake sensor may be a remote sensor located within a beverage container or dispenser may communicate the type and volume of liquid consumed by a user to predict the change in hydration.
As the device increases its sampling rate of data collection, the resolution of measurements for activity levels, body composition, location, and other physiological data can increase. Likewise, the more often the personal device communicates to the hub or to other remote sensors, the greater resolution of information. However, this can drain the battery of the personal device.
To increase battery life and decrease the required memory space of the personal device, a variable sampling rate may be used for data collection.
Alternatively, the personal device may use the method shown in
The average position of the user may be determined by taking averages of each of the columns to determine the force vector on the accelerometer during that portion of time. The location of the accelerometer is shown in
To ensure that the 3-axis accelerometer is oriented according to the defined axis, the personal device may be constructed to clip to a belt or article of clothing to ensure that it is oriented in an expected manner. For example, in
Alternatively, the personal device may be constructed to be worn on the wrist such as the embodiments shown in
The personal device may be constructed in a way that may be worn or attached to the user. The personal device may be calibrated to determine the vertical and horizontal axis. To do this, the personal device may prompt the user to stand, sit, and lay down and record each state using the gravitational force to define the vertical axis. In one embodiment, this determination can be made using a three axis accelerometer. The personal device may prompt the user for alternate actions such as jumping or walking to further calibrate.
For taking bio-impedance or bio-resonance measurements, the personal device may take measurements at standard intervals throughout the day at specified times to reduce variation in measurements due to hydration, activity levels, and body position. However, a person's daily schedule can be subject to fluctuations and may not be relied upon for standardizing measurements in some situations. To compensate, the personal device may use activity levels along with general time intervals to determine when to take a Bio-impedance measurement.
As discussed above, a bio-impedance measurement may be conducted on a user 5320 using bio-impedance measurement circuitry 5300 shown in
In one embodiment, bio-impedance measurement circuitry may be used to take bio-impedance measurements using electrodes and be reconfigurable to measure other biological factors, such as heart rate or skin resistance, using electrodes.
Describing
As discussed above, a heart rate measurement may be taken before or after a Bio-impedance or bio-resonance measurement to provide additional information to the component about the current state of the user. This additional information can be used to normalize the Bio-impedance data if each measurement is correlated to the position and state of the user.
In the illustrated embodiment of
In one embodiment of the present invention, the behavior modification system includes a network of components that are able to take measurements of: a user's present physiological state, the user's actions and location, the environment around the user, the devices and objects around the user, and also has a user interface for the user to receive data and also input information into the system. This network of components can be enabled to wake one another up using RF signals or inductive power, or the wake up can be motion based. These components can provide fast information transfer and storage, and can even be connected to the internet so that data can be gathered and pushed to an online storage and tracking system. The components may be powered by energy storage elements such as batteries or can be directly powered from a wired or wireless connection to another device, and can use essentially any charging connection. The components can also sync data information—for example, when connected to a computer via USB, a wearable device may charge, but may also sync its data history to the computer. Additionally, this network may include a hub or set of hubs to download data to be shared amongst components and stored remotely in a cloud computing device, or remote server. This reduces the memory and processing power required by the networked components, making them smaller, less costly, and reduces their power consumption.
An example of one embodiment of the behavior modification system generally can be used to change the behavior of a user or the user's environment by providing recommendations, automatic updates, warnings, reminders, and progress information to a user. These pieces of information can also include a user's electronic calendar of appointments, electronic grocery lists, and data from external resources, such as a weather database. To assist in accomplishing the behavior modification, the system may make determinations based on the data the components gather, the information provided by the users, and the correlation of data to activities, moods, changes to a person's health, and diet.
This network of components includes devices that can be worn or carried by a user, or may be embedded in articles already worn or carried by the user. One example may include a wristwatch capable of taking skin measurements, heart rate, levels of dissolved oxygen, and temperature. Another example may be a pedometer that can be worn as a device on the belt of a user, or it may be embedded in the belt, shoe, or other article of clothing, and is capable of gathering the same information. These devices can also be applied directly to the skin of a user, or even implanted in the user. For example, a sticker or temporarily adhered flexible circuit may be applied to a user to measure the amount a user sweats for certain period of time. Another example is an implantable medical device such as a pacemaker or a blood sugar monitor that is capable of not only gathering biometric information, but also transmitting that data wirelessly along with being wirelessly charged. This charging could be through an inductively coupled system that not only charges but provides a secured data interface between the base and the device.
These devices may also communicate with one another to provide a constant (or near constant) stream of user data, and may also detect when one device has been removed from the network. In this instance, the system may determine that the location (based on a GPS signal) where the device was last detected may be an unacceptable location to be left behind, such as a restaurant or other public location.
Additionally, these components may be powered from one another either through wired connections or wireless connections. For example, devices in accordance with the present invention may be charged by the hub while transferring data to and from the hub. This wireless charging may be used to initiate the data connection, prompting the transfer of information. This device may also be capable of powering other devices or sensors as well. For example, this device may provide power to removable sensors worn on the body. These may include adhesive backed skin patches, RFID tags, pedometers, or other wearable sensors. These sensors may provide information back to the device including number of steps or distance walked, heart rate, perspiration, hydration, temperature, or any other type of biological data. This enables remote sensors to be used that are not enabled with a longer-range wireless data connection such as Bluetooth or WiFi.
This network may also include user interactive components. These components may be woken by a proximity signal from the device(s) being worn on the user such as the articles of clothing shown in
Another example is a scale in a bathroom that can be woken by a user stepping on the scale. Alternatively, a device being worn or carried by the user may determine that the user should be stepping on the scale if it has been too long since last used. Once the scale and the device(s) being worn or carried by the user sync together, the scale may display the weight and will also transmit that data to the remote devices. If the scale detects devices being worn or carried by the user have sufficient weight to change the measurement taken, the scale may adjust the recorded weight by the estimated weight of said devices/articles of clothing. Additionally, this scale may be capable of measuring Fat Mass (FM) and Fat Free Mass (FFM) using Bio-impedance spectroscopy or bio-resonance (as described in more detail below). This data may be transmitted to a device where it is used to calibrate the on-board Bio-impedance or bio-resonance measuring circuit, may be used to calculate caloric intake as compared to energy expenditure, may be stored and later transferred to the hub to be analyzed by a remote computing device, or any combination thereof. Alternatively, this data may be transferred directly to the hub.
Another example is a television that is woken up with the remote control. Once woken, the television may synchronize with all of the users within a specified range. The antenna may also be made as a directional antenna so that users at a specified range in front are recognized, but users behind are not detected as easily. This prevents users in another room, but close to the location of the television, from being detected. Devices being worn by users watching TV may record the event, and the TV may periodically (or continuously) check to see which users are within range, in case some (or all) of the users leave the room but the television is left on.
These devices may use various user interface methods to interact with a user including a touch screen, button control interface, a microphone or set of microphones to obtain audio information, and a speaker, transducer, or any other type of audio output device, or LEDs indicating various states of the devices.
This system may also be enabled with location based sensors and long range network connections that interact with one another when the devices are within a certain distance from one another but out of range of their proximity based sensors and communication systems. For example, a device being carried by a user may be enabled with a GPS receiver to detect when the user enters a building, such as a hotel. Alternatively, the lobby of the building may be enabled with a proximity based system that detects the user entering the lobby. Once detected, the hotel's computer system may send a proximity based message, an SMS message, or an email to the user letting them know that (if they had previously booked a room) that their room was ready, their room number and location, and any instructions the user may require. The hotel computer system may then send a message through a communication network, such as a LAN network, to the door lock of user's reserved room. This lock may unlock once the signal is received, once the users device is in proximity to the door itself, or it may be enabled with an unlock code. If a code system is used, the device the user is carrying may receive the code from the hotel computer system and may provide a numerical code for the user to enter, or may provide a code that is then transmitted to the door lock through a proximity system once the device is near the door. For example, a cell phone may be enabled with a GPS system that alerts the hotel once a user has entered the hotel, and in response, the hotel computer system sends an unlock code to the cell phone. Once the user approaches the door of his/her room, the cell phone may be used as the key either using a proximity-based RF system, or can be used as an inductive interface or RFID/NFC device.
Another example may be a restaurant location that automatically downloads information, such as a menu, list of specials, or other information, to a user's phone once the user is in proximity to the restaurant's proximity sensor network. Alternatively, a user's phone may be equipped with a GPS receiver and may be configured to automatically communicate to the restaurant's computer system either directly through a proximity communication network or through an internet connection to download restaurant information.
This system of devices may also be enabled with a (set of) hubs or central devices capable of communicating to remote devices over several different wireless communication methods, such as Bluetooth, ZigBee, Wi-Fi, NFC/RFID, and a number of wired communication methods, such as an internet connection, USB, FireWire, LAN, X10, or other such communication topologies. This hub can connect to devices, download information from the devices, and transfer that information to a central data storage area either on a large memory storage device (such as a hard drive or desktop computer), or can be sent through the internet to a remote storage location or server.
This hub can also receive device updates, instructions, warnings, or event information that can be sent back to the remote devices so that they can be updated. Finally, this hub can send messages through a wired connection either through a local network connection or through an internet connection to control remote devices that the user does not wear or carry, such as a thermostat, television, lighting system, exercise machine, or any other non-mobile or semi-mobile device a user may interact with.
The system may track caloric intake directly or indirectly. It may track caloric intake directly by using any combination of methods. For example, a user's device may communicate to food packaging or home appliances to detect what foods in which quantities the user may be taking and eating, a user may take a picture of a meal and allow an image processor to determine the nutritional and caloric values, a user may take a picture of a receipt or product label, or a user may specifically enter a food and quantity into a survey or other user prompted data input (a program running on a cell phone for example). Other means of tracking caloric intake may include inventory management wherein a refrigerator, pantry shelf, or other inventory management hardware may determine the removal of products while in proximity to certain users. This may prompt a device being carried by the user such as the health monitoring device if the user did in fact remove said product. Alternatively, the system may determine without prompting the user that the said product was in fact consumed by the user. Additionally, appliances or computing devices managing recipes or an automated cooking setup may communicate with the device, bridge, or other networked communication protocol to provide nutritional information about the food being prepared.
The system may also include input devices that can collect information directly from the user such as a computer, tablet, mobile phone, or other type of computing device. By prompting the user to enter information about himself or herself, the system may collect information about the user that may be difficult to directly measure. For example, information gathered from the survey shown in
A. Event Packeting
In order for the behavior modification system to track the information received from the components, the system may generate event logs.
The personal device can also build a list of nearby components including their identification, type, and optionally their current status or location. These components may be other personal devices worn either by the same user, or remote sensors worn by the same user, sensors worn by the user such as those shown in
The packet may include a tag from a user indicating their mood, stress level, energy level, or other types of emotion or physiological information. These tags can be stored along with the current status of the user for further analysis. This information may be input into the personal device using a touchscreen, button interface, or other type of user interface. Alternatively, the user may enter information into a networked component such as a cell phone or personal computer. This entry can be routed back to the hub, personal device, or server based analysis tool to be combined with additional event data.
These packets may be collected and stored on the personal device, where each piece of information can be recorded by the personal device. Alternatively, the personal device may measure some of the values, and may collect some of the other values by communicating with other remote sensors or with the hub. The hub may also collect and store information either in addition to or instead of the personal device. By collecting information on the hub, data may be processed by the hub or by internet connected components such as personal computers, cell phones, or server based computing devices.
B. Data Transfer
The transfer of information between a component and its surrounding may be triggered through many different ways. Some triggers may include a maximum time requirement for a physical measurement, change in location or activity, a tagged input from the user such as their mood or emotion, or an RF wakeup pulse from a hub or other component.
When the personal device is triggered to record an event packet, it may proceed according to the method 6300 shown in the flow diagram of
The triggers may be many different types of events. For example, if the user suddenly changes their activity level or average position, the personal device may record an event packet to note the change from one state to another. Alternatively, the personal device may be triggered from a time-out warning for a Bio-impedance and bio-resonance measurement, heart rate measurement, or other type of physical measurement. Additionally, a component may be triggered by an RF wakeup signal, which can result in the successful identification of a hub or other component. When a wakeup signal is detected along with the identification of a hub or other component, the personal device may determine that the user has changed locations based on identification of new hubs, or may have changed activities based on new components within proximity or previous components no longer being within proximity.
For each trigger, the personal device may prompt the user for an input response to indicate mood, emotion, or other data that may not be able to be determined from the components within proximity. For example, the personal device may not be within range of any hubs capable of indicating location. In this circumstance, the personal device may prompt the user to enter a location to be included in the data packet.
C. Behavior Identification
By tagging as many of these event packets with a mood or emotion tag, the system may begin to associate the event packets with behaviors and activities. Additionally, the system can analyze the current and previous states to determine the top causal relationships for certain moods or emotions. This data may be analyzed after event packets are collected, or it may be included within each event packet, where the current and previous activity, location, and connected components may be recorded. By understanding the top causes of changes in mood or emotion, the system can begin to predict changes in mood or emotion based on the identified patterns.
Behaviors can be identified by combining actions, locations, biometric data, and components that a user is interacting with, and detecting patterns. These behaviors can be simple, like how a driver behaves while driving their normal route to work, or can be complex, like how a user interacts with other people in a work or home environment. When behaviors are identified, the data processor can begin linking behaviors together to form a lifestyle based on net caloric intake, stress factors, and relationships within a user's life. The psychological effects of such a lifestyle can be measured by number and type of user tags, the number of positive relationships, and can also appear in the physical effects. The physical effects of such a lifestyle can be measured by tracking a user's weight, blood pressure, sleeping habits, and activity levels.
Once these behaviors and lifestyles are identified, the data processing unit (either on a mobile computer such as a laptop, cell phone, or tablet, or on a central data processing machine such as a desktop computer or internet server, or on essentially any other component) can start to recommend changes in activities and nutrition to begin to modify a user's behavior and ultimately their lifestyle. These recommendations are made based on a target behavior or set of target behaviors with a desired outcome. For example, if a user wants to lose weight, the program can recommend different eating habits by suggesting different restaurants, different recipes at home, or supplements to try and improve a user's metabolism. The recipes used may automatically upload items to a user's shopping list for their next trip to the store, or may be automatically ordered if a user prefers to set up an automatic system with limits for price and quantity. The supplements may automatically be dispensed by a pill or liquid dispenser in the quantities needed by the user. The program may also recommend changes in levels of activity by giving suggestions for activities that would fit the user's lifestyle in terms of length of activity, level of exertion, and type of workout (muscle building vs. cardio vs. just walking). These activities may be targeted to simply burn more calories than a user normally burns, or they may be activities to prevent a user from eating at a time he or she would normally eat something unhealthy. Over time, the system can track the user's progress against set goals, and can make adjustments. For example, adjustments can be made if the progress is not meeting the set goals.
The goals for the behavior modification may be entered by the user, by a physician or doctor, can be suggested by the program if the program detects bad or potentially dangerous habits or lifestyles in the user, or can be uploaded from a set list of suggested goals, or a combination thereof. Once the goals are established, the system may use a set formula for determining the behaviors that require modifying and how it suggests such changes, or the system may use a subset of possible changes, begin suggesting them, and adjust the suggestions based on how well they worked. For example, a person struggling with hypertension may have goals entered by a physician, targeting a specific weight, blood pressure, and sodium intake levels. The system can make recommendations to avoid foods high in sodium and fat, provide activities that are aerobic but at a low exertion level to prevent high blood pressure during exercising, and suggestions for people to be around, to avoid, or to improve a relationship with. If the user does not respond to messages about which foods to avoid by either continuing to eat them or if their body simply does not respond by removing certain foods from a diet, the system can alter its suggestions by giving other options of food that a user may enjoy.
Another example is a person who wants to train for a marathon. The user can select a race date and distance, and the system can provide recommended activities, nutritional supplements, and a workout plan to help the user reach his or her targets. The system may also provide warnings to users that perhaps are unfit to train for a certain goal in a short timeframe, and may suggest an alternative goal (perhaps a shorter race at a more distant date).
The system can also be configured to track the progress of a user against a set goal. For example, a user may set a target weight and use data gathered to track not only the current progress of a user against his or her targets, but also look at the reasons why he or she is making the progress they are making.
The system may also suggest changes to a person's schedule. The schedule changes may be suggested to change the time of day when they interact with certain individuals, avoid traffic delays or other activities that create stress. For example, the system may suggest a workout in the morning instead of in the evening if the person is having trouble waking up in the morning.
The system may also change settings or operating conditions for components within the system to aid the user in reaching his or her goals—for example, for just living a more comfortable life. For instance, the personal device may detect elevated body temperatures and levels of movement while the user is trying to sleep, and may decide that the ambient temperature is set too high. Rather than suggesting that the user change the temperature, the system can automatically adjust the room temperature until the user is comfortable. For example, the temperature may be automatically adjusted until measurements on the user's personal device indicate the user is comfortable, such as when the user's temperature reaches a threshold or when the user's activity level returns to normal. The comfort level of the user can be preprogrammed by the system or set based on the user's input. This change can also be recorded and repeated over time to ensure a consistent sleeping pattern for the user, and may determine that the user should be made aware of the change once he or she wakes up. The program may also automatically update components within the network with new settings as the user's behavior changes. For example, as a user improves his or her cardiovascular strength, their walking pace may increase in speed, along with their running pace. The system may choose to update the motion sensors in the network with new values to determine whether the user is walking or running.
The system may also provide up to date information to the user in an effort to modify behavior. For example, when a user walks up to a vending machine, the system may provide an up-to-date calorie count for the day, with an estimate as to how far over or under the user will be for the day or week. Alternatively, the system may prompt the user with an event reminder to try and change the user's current actions. For example, the program may remind a user that they have an early morning meeting the following day, and need to turn off the TV and go to bed. Another example is when a user sits down to eat lunch, and the system may remind the user that they are going to be at a dinner meeting later, and that they should probably not eat as large of a lunch.
In addition to providing recommendations to a user, the system may provide the user with access to data and other types of information collected, calculated or otherwise obtained by the system. For example, the system may provide the user with data collected by system-enabled components. The data may relate to specific activities or combinations of activities. As another example, the system may provide the user with result tracking data and efficacy information. The data and information made available to a user may be limited to that user's data and information, or it may include data and information for other users. If it includes data and information from other users, the data and information of other users may be made anonymous.
The system recommendations may include product and service recommendations based on data and information collected through the system. For example, the system may allow tailored product advertising. The system may identify potential product recommendations for a user by evaluating the data and information collected from that user. The data and information may suggest that the user might have an interest in a specific product. As a few examples, the locations frequented by a user, the types of activities of the user and the consumption habits of a user may alone or in combination allow the system to determine products or services of potential interest to a user.
The system can also be used to train animals. In one embodiment, a device for training animals is provided that is similar to the personal device describe herein. The device can be embedded in a collar to track a pet's activity and location relative to other remote components in the house and can be used to track the pet's status. For example, the device may use proximity communications to detect when the pet is near its food dish, and can alert the owner within a short time frame that the pet may need to go to the bathroom. The system can also time how long it has been since the pet last went to the bathroom, and again alert the owner, or perhaps open a pet door to let the pet into the yard. The device can also determine how far the pet has gone from the house, and if equipped with a GPS based location system, the pet can be tracked. If the pet wanders too far, a correction tone or voltage stimulation may be used to correct the pet.
The system of components described in the embodiments above can also use proximity based interactions with other individuals or pets to understand how the interactions between these people and animals affect the users. For example, the personal device, sometimes referred to as a widget, being worn by users can use proximity-based sensing to determine the users within a room, and which other types of components are present. If a number of people are gathered in a conference room with few other components around, the system can categorize the gathering as a type of meeting. The biological sensors may be able to detect levels of stress based on audio analysis, heart/respiration/perspiration rates, and other biological responses. These interactions can be tracked over time to determine who in your list of relationships causes stress, causes you to relax, and causes other reactions based on these interpersonal interactions.
Additionally, components may determine the proximity of other similar components and produce a response. For example, two vehicles 6506, 6508 passing each other, such as those shown in the
Another example is tracking the effectiveness of exercising with other individuals or pets to determine which method best fits the needs of the user. It could be that when a user is exercising to relax himself/herself, the best method is to exercise alone. However, when they are feeling tired or sluggish, it may be best to exercise with their friends to push them harder or encourage them to exercise longer. It could also be that a pet with a collar enabled with a component can be detected by the user's personal device when they are running, and can determine if exercising with a pet is more or less effective base on speed, duration, or caloric output and weighing them against the desired workout type. The system can make a recommendation to the user based on this determination to encourage or discourage particular activities.
Pet training can also be done by monitoring pet activities and correlating them with desired or undesired events and activities. For example, a pet may be more active on the days when the owner is not around and the pet sleeps more during the day. On those days, the pet may be more likely to be destructive or ill-behaved, causing stress for the user.
Behaviors involving interactions with others can also be modified using in the behavior modification system. The moods of the user along with the user's circle of people they commonly interact with can be tracked and users can be given updates or warnings if the system detects that the user is about to interact with another person that is in a bad mood, that the user commonly causes stress to, or that someone they know is feeling down. The system can suggest actions for upcoming interactions such as a compliment to diffuse a potentially tense situation, a reminder that it is some one's birthday and they feel tired or down, or a suggestion to buy flowers for a spouse if they have had a rough day and you are on your way home.
The system can also suggest activities and interactions based on where a user is and what their needs or goals may be. For example, if two users have not burned enough calories for the week and they both enjoy similar activities, the system may suggest that the two users get together for a game. The system can even check the calendars of the two users, suggest a time that works best, and automatically make an online reservation for a restaurant, tee time, or any other type of reservation. Another example may be a user walking home from work may pass a restaurant or bar, and the personal device may detect a number of people either in the user's normal network of interactions, or a number of people matching the user's usual type of interactions, or people matching the user's desired interactions. The personal device can provide a text message or other alert as to who may be in the restaurant, can automatically download the menu as the user walks in, can send a message alerting the restaurant or bar of their typical order, and can even set up a secure payment system with the personal device the user is carrying or wearing.
In one embodiment of the present invention, the behavior modification system, sometimes referred to generally as a network, may gather data from components within the system. This gathered data may be used to obtain information about the user or the user's environment. For example, one or more sensors within the network may gather information relating the user's activity, audio levels, and biological data. This data may be combined or aggregated to understand the user's actions, mood, and track physical health and status over time. The data may also be combined with user information input.
Sensors can be worn or carried by a user (for example, a wrist-worn sensor device), embedded in an article of clothing or device worn or carried by a user (for example, sensors embedded in a shoe or used in a cell phone), implanted in or ingested by a user (for example, a sensor device placed in a capsule to be swallowed by a user), or even applied directly to the body of a user (for example, a sticker or temporary tattoo). The data gathered by the sensor network may be gathered and shared by processing unit of the system, which can organize the data.
Biometric data gathered by the sensor network may combined to indicate levels of stress, tension, or biophysical state by measuring skin salinity (perspiration and content of the perspiration), heart rate, respiration rate (either by measuring breathing motions or my measuring dissolved oxygen levels in the bloodstream), and body temperature, along with other known biometric sensors.
Another example is the detection of stress or activity based on respiration rate. When a user is breathing long slow breaths (detected either through motion sensor located around the chest cavity, or by measuring dissolved oxygen levels), the network can detect that a user is at a more relaxed or calm state, indicating that a user may be sitting comfortably or even sleeping. If a user is drawing deep breaths at a faster pace, the user may be more active or at an increases state of stress or anger. When the breaths become very short and not very deep, it may indicate that a user is at a high level of stress, is nervous, or perhaps is active but running short of breath.
The components within the system may gather data, analyze the data, and categorize activity patterns for use with the behavior modification system. For example, the sensor network may also use accelerometers or other motion sensors to more directly measure activity level and type of activity. These sensors may be a single stand-alone sensor such as a pedometer, or can be a combination of sensors sharing data with one another, a central hub, or another component within the behavior modification system. This network of sensors can detect activity levels by measuring the amount of motion. For example, accelerometers located in a phone or embedded in a shoe can detect elevated levels of motion when the user is running as opposed to walking. By using several sensors located at various parts of the body, such as the feet, wrists, arms, or chest, the motion of each area of the body can be measured and compared to one another to detect the activity type. For example, when a user is running a distance run, the motion of each sensor may be rhythmic and relatively at the same level as one another, since the entire body is moving forward at a near constant rate. As another example, when the user is playing basketball or another type of sport involving numerous twists, bursts of speed, or motions that involve one part of the body and not another, the sensors can detect varying amounts of motion over time, and at a rate different to one another. Still another example is when a user is asleep, the network of motion sensors may be able to detect when a user is less comfortable based on how much a user is moving around, or may be able to detect which sleep phase a user may be in.
The sensor network may also gather environmental information to share within the network. Data such as temperature, audio level, ambient audio level, light level, ambient light level, presence of allergens or pollutants, and other known environmental sensors can be used to understand the environment a user may be in. For example, a microphone on a cell phone may be activated periodically to measure ambient audio levels as well as the general frequencies of the ambient audio. For example, a component can detect large amounts of speech in a busy conference room or waiting area by sensing elevated audio levels in the range of human speech frequencies (90 Hz to 500 Hz), or a manufacturing environment with extremely elevated audio levels and lower frequency components (below 100 Hz), or a concert with elevated audio levels at higher frequencies (above 1 kHz). By using more advanced speech recognition technology, stress levels within human speech may also be detected.
Additional input from the user in the form of surveys, periodic questions, or other forms of user entered events can also be recorded for further analysis by the system. For example, a user may periodically be prompted by a component to input information about how they are feeling both physically and emotionally, or a user may determine on their own to tag certain events with specific information. For example, a user may input information to their personal device about which TV show they watch when they turn the TV on so that additional data can be correlated with that event. Alternatively, a user may prompt a delay of certain events by giving specific information. An example of this is when a user removes food from the refrigerator, the user may tag the event by saying that the food removed will be consumed at a later time, or by multiple users, or both. A user may also be able to prompt surveys themselves by tagging a meal or event and answering questions about that meal or event. A user may also be able to record information about an event for the network to process. For example, a user may take a picture of a meal, tag the event as their own meal at a certain time/place, and the system can process the image to determine caloric and nutritional information about the meal. That data may be correlated to other time-relevant data about the user and the user's environment.
The system may also include a knowledge database or an expert system capable of providing recommendations based in part on feedback to select questions from the user. For example, the user may indicate that she has a headache and the system may begin to ask questions that may assist in determining the cause of the headache and providing one or more potential solutions. Expert systems for various categories of interaction may be incorporated into the system. For example, a medical expert system, such as WebMD, may be incorporated into the present invention and may be used to determine appropriate questions to ask the user, to analyze the user responses and make appropriate recommendations to the user. The present invention may generate recommendations based on user feedback from the expert system queries in combination with other information collected by system-enabled components, such as activity data and biometric data collected by the system. Hybrid recommendations of this type may provide better results than recommendations based on only one type of input.
A survey may be used to determine how a user is predisposed to respond to certain events both in a physical and emotional way. This survey may include information about health information, such as height or weight from the user, or can automatically pull in information gathered during a health physical from a physician. It may also include relationship information about the user, such as current job status, marital status, history of mental health, and other such information. The information collected in the survey may vary from application to application to collect essentially any information that might conceivably be relevant to operation of the system.
In one embodiment, the system may be configured to solicit user feedback on the efficacy of system recommendations. For example, the system may ask the user to provide feedback on how successful a particular recommendation was in addressing an issue. As another example, the system may ask the user to provide feedback on the relative effectiveness of different recommendations, such as the relative effectiveness of two alternative recommendations previously made by the system. The system can use this user feedback in formulating future recommendations for that user and for other users. The questions presented to a user by the system may extend beyond subject matter relevant to user recommendations. For example, the system may present essentially any type of question that might benefit from consideration by a user or a large pool of users, such as questions concerning a user's impression of a new marketing concept or a potential new product. If desired, these types of questions may be intermixed with user feedback questions or question presented by an expert system or knowledge database.
In one embodiment, the system may be capable of providing different recommendations to different groups of users so that, among other things, the efficacy of different recommendations can be assessed. The system may create two or more groups and provide each group with a different recommendation or different set of recommendations. The system may implement a control group and may provide with a placebo recommendation.
The data from the arrays of biological, environmental, and motion sensors may be combined with location based information, data shared between remote devices and components located around the user, and information directly input from the user to detect a user's mood. A user's activity level, location, and the identification of surrounding components provides data as to what type of activity a user is most likely doing. By determining the mood of the user for given points in time, the network can begin to identify trends and habits. For example, if a user has an elevated heart rate and perspiration along without a large amount of activity, it can be determined that the user has an elevated excitement level due to stress, nervousness, anxiousness, or other elevated states of anxiety. If the system then detects the television being turned on and the accelerometers detect that the user has sat down, the user is most likely now watching TV. Now if the user's heart rate and skin salinity become reduced, the system can determine that this action relaxes the user. The system may additionally prompt the user to input information at that time about the person's current mood to verify, or calibrate, the prediction algorithm. However, if the heart rate and skin salinity stay elevated, the system can determine that this action does not in fact improve the user's level of stress.
When location, activity levels, and surrounding component data are combined with biological data, the user's activities can be tied to a physical or emotional state and recorded. For example, if a user is sitting watching TV but has an elevated heart rate and shallow breath, the user may be watching an exciting movie or sports program. If the user had an elevated heart rate and shallow breathing prior to turning on the television, the user is most likely stressed or angry, and is using the television as a method to cope or distract from the elevated stress levels. Another example is when a user is getting food from a refrigerator, the proximity sensors in the user's personal device connect with the refrigerator and information about which user, what food, and what time of day is removed. The biological sensors on the user may also record the state of the user before and while the user is taking the food and given timestamp information. Once the data is collected—or while it is being collected—personal device and the refrigerator can sync the data to one another, or can sync information to a common hub or bridge, or set of bridges, or can store the information along with the timing and location data on their own internal memory storages to be later downloaded to a central bridge or hub. Information can then be processed to determine the state of the user before food was taken (stressed, relaxed, dehydrated, tired, etc.), what foods were consumed by the user, and what effect it had on the user (became relaxed, woke up, felt nauseas, fell asleep) by tracking the biological data for a period of time after the food was consumed. By tracking these before and after states and correlating them to the event trigger, the system can detect foods or activities that have either positive or negative effects on a user. For example, a food allergy can be detected by correlating a nauseas feeling with eating certain foods over a long period of time. The system may also be able to detect patterns of eating, drinking, and activity with a user's physical and emotional state. For example, a user may be more likely to sit and watch TV when he or she is tired and stressed, but may be more likely to go on a walk when they are stressed but not as tired.
The system can also detect patterns by looking at the users physiological data over time such as how well a user is hydrated, body temperature, and information input from the user about how they are feeling and compare that to past data tagged by the user.
The illustrated embodiment of
The system can also use data gathered to track the health of an individual over time, rather than looking at specific events. For example, the system may be used to track the long term effects of changes in environment, diet, or activity levels. A user may tag a day, week, or month in which they made a major shift in habits, such as drinking more water and less coffee.
The system may also be able to determine effectiveness of certain activities against certain moods. For example, the system can determine which type of activity or food is best for relaxing the user when they are stressed by comparing previous events of when the user was tagged as stressed and comparing the different results against the various activities performed and food/beverage eaten. Alternatively, the network can determine the most likely causes of certain emotions based on events, activities, locations, sleep and work habits, and food.
The processing of data can be accomplished by a central program running on a computer or server that collects all of the pertinent data from the network of components. This central processing unit can match events and data with the timestamp information and user tags to build a linear database of information in the appropriate order and timeframe, so that events can be tracked not just at a certain point, but over time.
The processing of data can also be accomplished by a distributed program running on several different components located in proximity to or associated with various hubs (such as a server or desktop computer) but also components located near or on the user. For example, a program that processes data to understand a user's mood may use a large server system, while a program that processes health and exercise data may be running on a cell phone or other personal device that the user carries with them.
The present invention may incorporate the use of a behavior monitoring survey. The behavior analysis survey assists the systems in identifying the daily habits of a user. For example, the survey can be completed by a user via communication with the personal device. The personal device may monitor various activities, such as how often the user visits certain rooms. The survey can be used to assist in understanding the behavior of the user better.
With the system having an understanding of the user's behavior, the system can compare the stored information with current personal device readings. From this the system can build metrics on behavior analysis. Behavior analysis is sometimes referred to generally as the field of Applied Behavior Analysis (ABA). This field monitors behavior, analyzes behavior, and introduces stimulus to affect changes in behavior.
Three Applied Behavior Analysis metrics are repeatability, temporal extent, and temporal locus. Repeatability deals with the count, rate/frequency, and celeration (how the rate changes) of a behavior. The temporal extent is a dimension that indicates how long a behavior occurs. The temporal locus deals with when the behavior occurs in time. It uses measurements such as response latency and inter-response time. Response latency is the measure of time that elapses between the onset of a stimulus and the initiation of the response, and the inter-response time is the amount of time that occurs between two consecutive instances of a response class. When trying to obtain quantifiable measures for a behavior it can be helpful to look at one or more of these three metrics.
To monitor behavior and develop these metrics and measures, multiple surveys and tracking components may be used. The survey shown in
In alternative embodiments, the survey in
One embodiment of the present invention may be capable of providing input with respect to essentially any behavior. Behaviors that are predictable or correlate with another action may be simplest to modify. Behaviors that are erratic and non-predictable may be more complicated to modify; however, they probably happen infrequently and may not be of particular concern. Behaviors that are tied to an action can be straight forward to modify. For example, say you want to quit smoking. Many smokers have a cigarette with a drink, or when they are bored or not occupied. If the system knew when you were having a drink, it may recommend having a piece of Nicorette to curb the need for a cigarette. Also if the system notices the user is stagnant, and there is a strong correlation between this behavior and smoking, then the system may be capable of sending you an article on your phone, or bringing up a game or puzzle to occupy your brain and limit the amount of stagnation or boredom.
Any behavior that can be correlated to surroundings, interactions, actions, or emotions can be modified. Having a user interact with the personal device and tagging certain situations can provide numerous data points for identifying correlations. Also, knowing who the user interacts with can provide valuable insight into correlations and modifying behaviors. If for instance a user is informed he/she has a cigarette every time he/she runs into a certain person at work, the user can try to eliminate the subconscious behaviors.
In one embodiment, the behavior modification system may implement component-assisted behavior modification to affect a user's behavior. For purposes of disclosure, the example in which the system can be used in connection a hypothetical user—John. It should be understood that, in alternative embodiments, the described sequence may include additional features as described herein, and may include some but not all described features.
The scenario described below can lead to multiple behaviors by hypothetical user, John. Four of John's behaviors are highlighted to demonstrate how they could be modified using one embodiment of a behavior modification system and an Antecedents, Behaviors, and Consequences (ABC) approach to behavior modification. In the ABC approach, observations are made on the Antecedents, Behaviors, and Consequences. Antecedents can be defined as the events or conditions present in the environment before the behavior occurs, Behaviors can be what is said or done by the person, and the Consequences can be the results, outcomes, or effects following a behavior.
When using the ABC approach for behavior modification there may be particular attention paid to the antecedents and the consequences. When analyzing the antecedents it can be useful to understand who was present, what activities are or have occurred, the time of day, season, time of year, and the location or physical setting in which the behavior occurred. When analyzing the consequences of a behavior, the consequences can be classified into at least three categories: i) reinforcing, ii) non-reinforcing, or iii) neutral. These consequences can occur naturally or be applied. Naturally occurring consequences can occur without intentional human intervention and applied consequences can be defined as those that are deliberately arranged.
Scenario: On Monday John wakes up late for work because his wife forgot to reset the alarm before she went to work. Realizing he is going to be late for work, he takes very quick shower and rushes to get out of the house. In his haste, John forgets to take his ADHD medication, and to take the dog out for its morning walk (Behavior 1). When he gets to work he realizes that he has an important report that needs to be completed. He spends the entire morning sitting at his desk, but cannot concentrate well enough to make any progress. At 11:45 am John realizes that he forgot his lunch and will not have time for his normal routine of going to the gym before he eats. Instead, he goes out to a local restaurant with his colleges and eats a bacon cheese burger (Behavior 2). After lunch, he goes back to work feeling very tired and gets about one quarter of the report finished. Just as John goes to save the report, his computer crashes and he loses all of the work he did after lunch. Frustrated with his day, he curses at his computer and leaves the office. When he gets home, he is greeted by his wife who is upset because he forgot to take out the dog in the morning (resulting in a large mess on the living room floor). In response to these acquisitions, John yells at his wife about forgetting to reset the alarm (Behavior 3). They get into a blowout argument, causing his wife to storm out of the house. For dinner, John makes a frozen pizza and eats it alone while sitting on the couch and watching his favorite football team. At half-time, he gets up and goes to the kitchen and gets a bowl of ice cream (Behavior 4). While watching the game, John falls asleep on the couch.
In one embodiment, if the system uses an event packet approach to behavior modification, it may determine recommendations based on the identified relationships between different states. For example, if the behavior modification system is able to predict that the current actions, activities, locations, and nearby components typically cause the user to go from relaxed to stressed, the system may determine the most common relationships that cause the user to go from stressed to relaxed, and suggest such actions.
In one embodiment, the network of components may change their control or communication methods in response to the identification of certain actions or events. For example, the system may determine that a user is not sleeping well based on the time of day, the level of activity, average position of the user, the location of the user, and the identification that these antecedents typically result in the user prompting the system with information that they are tired. The system may identify that the user typically sleeps better with a cooler temperature, and rather than prompting the user, the system may automatically adjust the thermostat cooler.
The system may track daily activities of the user. For example, as shown in the illustrated embodiment of
Components may also be used to collect information about a user or set of users and the components they may be interacting with. This data may be used for market research, for automatic component to component, or component to user interactions. For example, two vehicles of the same brand that are both equipped with a personal device may pass one another, detect one another using their proximity and identification protocols, and honk their horns at one another as they pass. Another example may be a store tracking movement of shoppers through their aisles using proximity and identification protocols. The store can understand how shoppers typically move through their store, get demographic information about the users, and can even understand how shoppers interact with product and components on the shelves by matching a user location with a component that is picked up off the shelf, turned on, or otherwise interacted with by a user at a given point in time.
As discussed above, the behavior modification system may include a hub that is capable of routing communication throughout the network. The hub may include transmitters and receivers for communicating over different protocols along with circuitry for routing communication.
One example of a hub for use in one embodiment of a behavior modification system is illustrated in
The hub may use an RF wake-up transceiver instead of a transmitter so that components may be used to wake up the hub. For example, if a component enters a room it can send an RF wake-up signal to the hub. As another example, the hub may be currently waiting to send another wake-up signal, and the device may determine that it needs to determine the other devices and hubs in the room, so it may transmit an RF wake-up signal. For example, if a personal device has completed a Bio-impedance reading, it may communicate that measurement to the nearest hub. Rather than waiting for the nearest hub to send an RF wake-up signal, the personal device may instead send an RF wake-up signal.
The hub may include or include a portion of a behavior analysis and modification engine 7614 to identify behaviors, trends, habits, and patterns of the users and their devices, and take action to change the behavior of the users. One embodiment of a method of behavior analysis and modification that can be implemented as a behavior analysis and modification engine is shown in
Components may be powered from one another either through wired connections or wireless connections. For example, components in accordance with the present invention may be charged by the hub while transferring data to and from the hub. This wireless charging may be used to initiate the data connection, prompting the transfer of information.
In one embodiment, the hub is a smart hub that includes wake-up circuitry for waking up components that come within proximity of the smart hub. Exemplary wake-up circuitry is described herein.
A smart hub may include a router and protocol controller along with wake-up circuitry. One embodiment of such a smart hub is illustrated in
The protocol translator can enable a command to be pushed from one component to another, even if those components are on different networks. Specifically, the protocol translator can enable a command to be pushed from any component to any network within the bridge network using the proper protocol. This may include, for example, pushing data from a simple network to an encrypted database on the cloud. A component can interface to a central controller that is compiling and synthesizing daily performance and activities to recognize patterns and behavior changes. In one embodiment, the central controller may be located in the hub as part of an internal behavior modification engine. In alternative embodiments, the central controller may be located remotely on a network.
An exemplary embodiment of a hub interacting with a plurality of behavior modification components is illustrated in
The hub can utilize a configurable and interoperable data communication protocol. An example of such a protocol 7700 is illustrated in
In one embodiment, the network of components may also include one or more hubs or central components capable of communicating with other components over several different wireless communication methods, such as Bluetooth, ZigBee, Wi-Fi, NFC/RFID, and a number of wired communication methods, such as an internet connection, USB, FireWire, LAN, X10, or other such communication topologies. The hub in this embodiment of the behavior modification system can connect to components, download information from the components, and transfer that information to a central data storage area either on a large memory storage device (such as a hard drive or desktop computer), or can be sent through the internet to a remote storage location or server.
The hub in this embodiment may also be configured to receive component updates, instructions, warnings, or event information that can be sent back to the components so that they can be updated. The hub can send messages through a wired connection either through a local network connection or through an internet connection to control components that the user does not wear or carry, such as a thermostat, television, lighting system, exercise machine, or any other non-mobile or semi-mobile component a user may interact with.
One aspect of the invention is directed to reducing system wide power consumption. In one embodiment, the system components have the ability to enter a low-power standby mode when inactive. In one embodiment, the system components may utilize a wake-up signal to wake-up from standby mode. For example, a wake-up signal may be transmitted by one device, such as a hub described herein, to wake-up another device, such as a personal device described herein. As another example, a wake-up signal may be generated internally by an event. The event may occur within the component (e.g. timer-based event, motion-based event, or gesture-based event).
In one embodiment, the system may utilize an RF wake-up signal. For example, an RF signal may be broadcast at a predetermined frequency to wake-up components that receive the signal. The strength of the broadcast signal and the sensitivity of the receive antenna may be selected to control which devices are activated.
While the devices within this network may maintain a constant radio signal, or may periodically turn on their radio transceivers to listen for a communication method, another possible method is to use an RF interrogation unit that sends a pulse of power at a specified frequency. This pulse of power is strong enough to power a portion of the remote device, causing a trigger on the remote device to sense that it is being interrogated. These devices may use several antennas dedicated either to a communication transceiver or an interrogation transceiver, or they may be combined such that the device configures an antenna as an interrogation antenna when being used to wake-up other devices, or when the device is not using its communication system, that way an interrogation signal from a remote device may be received. Once an interrogation sequence has occurred, the devices may switch control of the antennas to the communication transceivers. Alternatively, a device may use a diplexer to allow both the communication transceivers and interrogation transceivers to use the antenna at the same time. In such an instance, each transceiver would be connected to the diplexer through a narrow band filter to prevent interactions between the two transceivers. An RF switch may be used to prevent damage to the interrogation receiver when the device begins to transmit an interrogation signal. For example, a device may use a SAW filter stabilized Colpitts oscillator and an amplifier to transmit interrogation signals. This transmitting circuit would be connected to an RF switch, which would multiplex the signal from the diplexer to either allow an interrogation signal to be transmitted from the device, or to allow an interrogation signal to be received. When using a common antenna and a diplexer, the carrier frequency for the communication transceiver and the interrogation transceiver could be different to prevent interference from one another. If it is required that they be the same, another RF switch should be used to disconnect the antenna from one transceiver when the other is being used.
The sensitivity of a component receiver to receive a signal sent by a component transmitter can depend on a number of factors. These factors may include distance between the transmitter and receiver, the frequency of the signal, and what the RF wake-up signal travels through to get to the receiver.
Determining the sensitivity for an RF wake-up circuitry on a given component can be determined based on starting beam signal strength, estimated environmental path loss, and estimated free space path loss. That is, designing appropriate minimum measurement accuracy for an RF wake-up circuit on a component within a behavior modification system can depend on the starting beam signal strength, the estimated environmental path loss, and the estimated free space path loss.
Environmental path losses can be estimated by making approximations for signals in the ultra-high frequency band propagating over the earth's surface. For example, it can be approximated that the path loss increases with roughly 35-40 DB per decade and 10-12 dB per octave.
Free space path loss can be estimated by calculating how much strength the signal loses going through a certain distance of air. This can be represented with the equation below,
where d is the distance between the receiver and the transmitter and λ is the signal wavelength. Dividing the speed of light by 900 MHz gives a wavelength of 0.333 m. Assuming the component receiver is typically about one meter away results in a free space path loss of about 0.000704. This estimation can be adjusted if the typical distance between the component receiver and the component transmitter will be different. The free space path loss can be converted to decibels using the following equation.
L
dB=10·log(P)
For this example, P is about 0.000704, which results in a free space path loss of about −31.53 dB.
Assuming that beam signal is about 0 dB, the desired sensitivity of a component receiver can be determined. The path loss plus the free space path loss added on to that is −62 dBm. This can be converted to power by using the above equation setting it equal to −62 dBm and solving for P gives 0.704 μW. This means the receiver has to measure with at least this accuracy in order to receive an RF wake-up signal from the transmitter.
A block diagram for one embodiment of a personal device with an RF wake-up system is shown in
Using the RF wake-up circuit, it is possible to build a low power receiver that can be run continuously without greatly limiting battery life. In one embodiment, the RF wake-up circuit has a sensitivity of approximately −50 dBm. The RF wake-up circuitry can receive a wake-up circuit at about between 6 and 8 feet.
An additional embodiment of an RF wakeup transceiver is shown in
When the device is no longer transmitting, the RF switch can be configured into receive mode, connecting the antenna to the SAW filter U, which receives the 916.5 MHz signal from another device and filters out any ambient noise. After the SAW filter, the signal can be passed to a peak detector T that uses a half wave rectifier and an RC filter. This signal can be amplified by a non-inverting amplifier S, then a comparator R can output a high in the presence of a detected 916.5 MHz signal. This signal may be used to trigger an input on the microcontroller, or may be used to turn on a power supply for another circuit, providing a way for the rest of the device to be in a power down mode while only the RF wakeup transceiver is drawing power.
As discussed herein, the behavior modification system includes a variety of components that accomplish various behavior modification functions, including gathering, sensing, and routing data and providing behavior modification stimulus to the user. A number of examples of specialized devices that provide one or more the behavior modification functions are discussed herein.
The illustrated embodiment of
In the illustrated embodiment of
The drinking dispenser 8520 may be capable of enabling a user to drink fluid, such as water or flavored water. In the illustrated embodiment, the drinking dispenser 8520 is a bottle or container, which may include electronics (not shown) positioned in the cap or around the body of the container. These electronics may monitor one or more of tilt, drinking duration, and volume of fluid within the drinking dispenser 8520. This information or data may be communicated to the personal device 8550 when or after a user has drunk from the drinking dispenser 8520. Alternatively or in addition to communicating this monitored information, such as drinking duration, the electronics may process the monitored information—e.g., to determine the amount of calories consumed—and communicate the processed information to the personal device 8550. The drinking dispenser 8520 may also communicate presence to the personal device 8550, enabling for example the personal device 4450 to expect information from the drinking dispenser 8520.
The drinking dispenser 8520 may include one or more displays 8530 and a selector (not shown) incorporated on the display 8530 or elsewhere on the drinking dispenser 8520. The display 8530 may interface with the electronics of the drinking dispenser 8520, and may provide notifications to the user, or provide information about the fluid in or provided by the drinking dispenser 8520, or a combination thereof. For example, the display 8530 may provide inventory information, a notification to the user about one or more of when or how much to drink, the drink type, the number of fills, and usage. The selector may be in the form of a button that allows selection of fluid type and enables downloading of information, such as new fluid types, from the device 8510.
The position of the electronics, the display 8530, and the selector on the drinking dispenser may vary among configurations. Further, in an alternative embodiment, these components may be incorporated into a cup holder or a cup insulator, separable from the drinking dispenser 8520. In this way, a variety of fluid containers may be used in conjunction with the system 8500. For instance, by including the electronics in a cup holder, a user's favorite coffee mug or drinking bottle may be used while still tagging and tracking the user's drinking consumption.
The personal device 8550 may be similar to one or more personal devices described herein. The personal device 8550 in this embodiment may be capable of wirelessly receiving information from the drinking dispenser 8520, such as presence and fluid information, and wirelessly transmitting recommendations based on health information to the drinking dispenser 8520. The personal device 8550 may include an interface that provides data or information about identification, activity, hydration, biometrics, and device interfaces (e.g., the drinking dispenser 8520 and the device 8510). The personal device 8550 may also wirelessly exchange information with the device 8510, such as user status and diet data. In this way, based on a variety of user data, the personal device 8550 may make a determination about whether to send a recommendation to the drinking dispenser 8520 and ultimately to the user.
The device 8510 may be any type of device capable of communicating with the personal device 8550, but for purposes of disclosure, the device 8510 is shown and described as a mobile phone. It should be understood that the present invention is not limited to a mobile phone and that other devices may be used. Further, in one embodiment, the device 8510 and the personal device 8550 may be integrated together such that the device 8510 includes features and functionality of the personal device 8550.
In the illustrated embodiment of
As described, the device 8610 includes wireless communication capabilities. These capabilities may involve a near field communication (NFC) interface in the device 8610, which may enable and facilitate enable payment processing with other devices. The device 8610 may also transmit payment recommendations to one or more of the personal device 8650 and the drinking dispenser 8620 so that, for example, the user can be notified to purchase a fluid type or to pick up an already purchased fluid.
The illustrated embodiment of
In the illustrated embodiment of
The system 8600 in the illustrated embodiment of
The illustrated embodiment of
The illustrated embodiment of
The illustrated embodiment of
The illustrated embodiment of
The illustrated embodiment of
The illustrated embodiment of
Directional terms, such as “vertical,” “horizontal,” “top,” “bottom,” “upper,” “lower,” “inner,” “inwardly,” “outer” and “outwardly,” are used to assist in describing the invention based on the orientation of the embodiments shown in the illustrations. The use of directional terms should not be interpreted to limit the invention to any specific orientation(s).
The above description is that of current embodiments of the invention. Various alterations and changes can be made without departing from the spirit and broader aspects of the invention as defined in the appended claims, which are to be interpreted in accordance with the principles of patent law including the doctrine of equivalents. This disclosure is presented for illustrative purposes and should not be interpreted as an exhaustive description of all embodiments of the invention or to limit the scope of the claims to the specific elements illustrated or described in connection with these embodiments. For example, and without limitation, any individual element(s) of the described invention may be replaced by alternative elements that provide substantially similar functionality or otherwise provide adequate operation. This includes, for example, presently known alternative elements, such as those that might be currently known to one skilled in the art, and alternative elements that may be developed in the future, such as those that one skilled in the art might, upon development, recognize as an alternative. Further, the disclosed embodiments include a plurality of features that are described in concert and that might cooperatively provide a collection of benefits. The present invention is not limited to only those embodiments that include all of these features or that provide all of the stated benefits, except to the extent otherwise expressly set forth in the issued claims. Any reference to claim elements in the singular, for example, using the articles “a,” “an,” “the” or “said,” is not to be construed as limiting the element to the singular.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US12/68503 | 12/7/2012 | WO | 00 | 6/3/2014 |
| Number | Date | Country | |
|---|---|---|---|
| 61567962 | Dec 2011 | US |
