Patent Grant
10080530
This application is related by subject matter to: U.S. patent application Ser. No. 15/048,965 filed on Feb. 19, 2016 and entitled “GENERATION OF SEDENTARY TIME INFORMATION BY ACTIVITY TRACKING DEVICE;” U.S. patent application Ser. No. 15/048,972 filed on Feb. 19, 2016 and entitled “TEMPORARY SUSPENSION OF INACTIVITY ALERTS IN ACTIVITY TRACKING DEVICE;” and U.S. patent application Ser. No. 15/048,976 filed on Feb. 19, 2016 and entitled “LIVE PRESENTATION OF DETAILED ACTIVITY CAPTURED BY ACTIVITY TRACKING DEVICE,” all of which are incorporated herein by reference.
The present embodiments relate to methods, systems, and programs for tracking user motion activity, and more particularly, methods, systems, and computer programs for communicating information to enable reduction of sedentary time by users.
The use of portable activity tracking devices has grown increasingly popular for people that want a way to track their activity levels throughout the day to accomplish fitness goals. Oftentimes, activity tracking devices, also referred to as trackers, report the number of steps taken by the person wearing the tracking device throughout the day, with the idea that the more steps taken, the higher the activity level, the better level of fitness will be achieved.
However, recent scientific studies have discovered that long periods of inactivity (e.g., sedentary times) may be bad for a person's health, even if that person is able to include regular exercise in their daily routine.
Methods, devices, systems, and computer programs are presented for generating alarms and congratulatory messages to influence reductions in sedentary time. It should be appreciated that the present embodiments can be implemented in numerous ways, such as a method, an apparatus, a system, a device, or a computer program on a computer readable medium. Several embodiments are described below.
One general aspect includes a method, which includes an operation for capturing motion data using an activity tracking device when worn by a user. The method also includes an operation for storing the motion data to memory of the activity tracking device. The method also includes identifying one or more intervals of time during a day, each interval including a start time and an end time, a near-end time being defined between the start time and the end time. For each of the intervals, the method determines from the motion data a number of steps taken by the user during the interval, comparing the number of steps taken by the user against a goal defined by a predetermined number of steps to be taken by the user during the interval, and generating a first notification for display on the activity tracking device during the interval when the number of steps taken by the user is less than the goal and the near-end time of the interval has been reached.
One general aspect includes a method, which includes an operation for capturing motion data using an activity tracking device when worn by a user, and an operation for storing the motion data to memory of the activity tracking device. The method also includes identifying an interval of time having a start time and an end time, where a near-end time is defined between the start time and the end time. The method also includes determining from the motion data a number of steps taken by the user during the interval. The method also includes comparing the number of steps taken by the user against a goal defined by a predetermined number of steps to be taken by the user during the interval. The method also includes generating a first notification for display on the activity tracking before the end time when the number of steps taken by the user is less than the goal and the near-end time of the interval has been reached.
One general aspect includes a non-transitory computer-readable storage medium storing a computer program. The computer-readable storage medium includes program instructions for capturing motion data using an activity tracking device when worn by a user. The storage medium also includes program instructions for storing the motion data to memory of the activity tracking device. The storage medium also includes program instructions for identifying one or more intervals of time during a day, each interval including a start time and an end time, a near-end time being defined between the start time and the end time. The storage medium also includes, for each of the intervals, program instructions for determining from the motion data a number of steps taken by the user during the interval, program instructions for comparing the number of steps taken by the user against a goal defined by a predetermined number of steps to be taken by the user during the interval, and program instructions for generating a first notification for display on the activity tracking device during the interval when the number of steps taken by the user is less than the goal and the near-end time of the interval has been reached.
Other aspects will become apparent from the following detailed description, taken in conjunction with the accompanying drawings.
The embodiments may best be understood by reference to the following description taken in conjunction with the accompanying drawings.
Methods, devices, systems, and computer programs are presented for generating alarms and congratulatory messages to influence users to reduce sedentary time. It will be apparent, that the present embodiments may be practiced without some or all of these specific details. In other instances, well-known process operations have not been described in detail in order not to unnecessarily obscure the present embodiments.
Embodiments presented herein periodically analyze user activity to encourage the user to avoid being inactive for long periods of time. Typically, users may only look at a daily goal (e.g., 10,000 steps) and do not pay much attention to activity levels throughout the day. Thus, a user may accomplish the daily goal but have large sedentary periods during the day. One way to avoid long sedentary periods is to monitor user activity in smaller intervals than a day, such as an hour, and then check if the user meets hourly goals. This way, the user is encouraged to meet the smaller hourly goals and avoid staying still for long periods.
Simple idle or sedentary alerts (e.g., “you haven't moved for one hour and 45 minutes) may provide a simple way for alerting a user to get up and move around, which may come with some health benefits. However, these “simple” sedentary alerts provide little information to the user, lack well-defined goals, and may generate alerts at inconvenient times for the user. Such downsides may have a negative effect on user engagement and motivation.
Recent studies suggest that regular activity breaks are more effective than continuous physical activity at decreasing postprandial glycemia and insulinemia in healthy, normal-weight adults. This proves the importance of avoiding prolonged uninterrupted periods of sedentary time.
Embodiments presented herein provide for the definition of sedentary-related goals and the tracking of activity throughout the day in order to reduce the amount of sedentary time of the user. In one embodiment, the period of time during which the activity is tracked during a day may vary, and can be user defined. Users enjoy positive reminders to walk around, or do some other exercise, throughout the day even though users may have already exercised that day. Further, the awareness of being sedentary for long stretches of time is important as users may overlook how much time users sit throughout the day. In addition, ongoing achievements throughout the day are compensated with motivating messages for an improved user experience.
What is needed is a way to motivate and inform users regarding their sedentary times in order to reduce sedentary times for a better fitness level. It is in this context that embodiments arise.
The devices collect one or more types of physiological or environmental data from embedded sensors or external devices. The devices can then communicate the data to other devices, to one or more servers 112, or to other internet-viewable sources. As one example, while the user is wearing an activity tracking device 102, the device can calculate and store the number of steps taken by the user (the user's step count) from data collected by embedded sensors. Data representing the user's step count is then transmitted to an account on a web service (such as www.fitbit.com for example) where the data may be stored, processed, and viewed by the user. Indeed, the device may measure or calculate a plurality of other physiological metrics in addition to, or in place of, the user's step count.
These metrics include, but are not limited to, energy expenditure (e.g., calorie burn), floors climbed or descended, heart rate, heart rate variability, heart rate recovery, location and/or heading (e.g., through GPS), elevation, ambulatory speed and/or distance traveled, swimming lap count, bicycle distance and/or speed, blood pressure, blood glucose, skin conduction, skin and/or body temperature, electromyography, electroencephalography, weight, body fat, caloric intake, nutritional intake from food, medication intake, sleep periods (e.g., clock time), sleep phases, sleep quality, and/or sleep duration, and respiration rate. The device may also measure or calculate metrics related to the environment around the user such as barometric pressure, weather conditions (e.g., temperature, humidity, pollen count, air quality, rain/snow conditions, wind speed), light exposure (e.g., ambient light, UV light exposure, time and/or duration spent in darkness), noise exposure, radiation exposure, and magnetic field.
As used herein, the term “sync” refers to the action of exchanging data between a first device and a second device to update the second device with new information available to the first device that is not yet available to the second device. Additionally, “sync” may also refer to the exchange of information between two devices to provide updates to one of the devices with information available to the other device, or to coordinate information that is available, overlapping, or redundant in both devices. “Sync” may also be used in reference to sending and/or receiving data to and/or from another computing device or electronic storage devices including, but not limited to, a personal computer, a cloud based server, and a database. In some embodiments, a sync from one electronic device to another may occur through the use of one or more intermediary electronic devices. For example, data from an activity tracking device may be transmitted to a smart phone that forwards the data to a server.
Inactivity alerts are message presented to the user carrying activity information regarding sedentary times. The inactivity alerts are designed to trigger the wearer to get up and move around to break up long sedentary periods, and to give the wearer positive reinforcement when the wearer responds to the inactivity alert. In some embodiments, the alerts may also identify an amount of activity achieved.
In one embodiment, a sedentary time is a continuous period of time where the user has not reached an activity threshold to be considered active. In some embodiments, a sedentary time may represent a collection of two or more continuous periods of time where the user has not reached the activity threshold to be considered active. In one embodiment, the activity threshold is defined as a number of steps taken within the sedentary period of time (e.g., 20 steps). For example, a user is considered to be sedentary, or inactive, if the user has not walked at least 20 steps since the last active period ended, and if the user has walked 20 or more steps, the user is considered no longer sedentary and is now considered active. In some embodiments, a user is considered sedentary if the user has not walked the required number of steps within a predetermined period (e.g., 5 minutes, or 15 minutes, but other values are also possible). Once the user is considered sedentary, the timer for the sedentary time is started, and the sedentary time will end once the user becomes active again.
In another embodiment, the metabolic equivalent of task (MET) measurement is used to determine if the user is sedentary or active. The MET is a physiological measure expressing an energy cost of physical activity, and the MET is defined as the ratio of metabolic rate (related to the rate of energy consumption) to a reference metabolic rate.
In general, MET values range from 0.9 (while sleeping) to approximately 23 (while running at a 4 mile pace for a young healthy individual). The MET can be thought of as an index of the intensity of activities. For example, a MET measure for an inactive or asleep status is close to 1.0, a MET measure for a user walking is generally above 2.0, and a MET measure for a user swimming is between 10.0 and 11.0. While in some embodiments the sensor information obtains MET measurements, alternative embodiments may use more or different measurements (e.g., a number of steps, number of stairs climbed, number of turns of a bicycle pedal, etc.) indicative of the motion of the user wearing the wearable electronic device and/or heart rate measures indicative of the heart rate of the user. The term “heart rate monitor” may be used to refer to both a set of one or more sensors that generate heart sensor data indicative of a heart rate of a user and the calculation of the heart rate measures of the user.
MET is used as a means of expressing the intensity and energy expenditure of activities in a way comparable among persons of different weight. Actual energy expenditure (e.g., in calories or joules) during an activity depends on the person's body mass; therefore, the energy cost of the same activity will be different for persons of different weight.
In one embodiment, a person is considered active when the MET exceeds a value of 2, but other threshold values are also possible. Thus, the user is determined to be sedentary when the MET is below the predetermined MET threshold (e.g., 2) and the user is determined to be active when the MET is above, or at, the predetermined MET threshold.
In one embodiment, an inactivity alert is generated when a threshold time within the hour has been reached and the hourly goal has not been reached. For example, in one embodiment, the inactivity alert is generated after 50 minutes past the hour if the user has not walked 250 steps yet during those 50 minutes. The threshold time within the interval is also referred to as the near-end time. Thus, each hour associated with an hourly goal has a start time, an end time, and a near-end time between the start time and the end time. In one embodiment, the near-end time is 50 minutes past the hour, but in other embodiments, the near-end time is in the range of 30 minutes to 1 minute before the end time.
In other embodiments, the near-end time may be variable, and can be adjusted depending on how far the user is from reaching the hourly goal. For example, if the user only needs five more steps to reach the goal, the inactivity alert may be postponed five minutes to give the user the chance to walk those five steps.
Further, the goal for the number of hourly steps is configurable. For example, the user may start with an hourly goal of 250 steps and later increase or decrease that number.
Referring to the exemplary flowchart of
From operation 206, the method flows to operation 208 where a check is made to determine if messaging is possible (e.g., enabled on the device) or if the device is on. If the result of the check is positive, the method flows to operation 210 where an inactivity alert in the form of a message (see “alert text” in
From operation 210 or operation 212, the method flows to the inactivity alert achievement flowchart discussed below with reference to
In one embodiment, if the user has not met the hourly goal when the near-end time is reached but the user responds within the remaining time of the interval to meet the goal, then the user gets a congratulatory message, but the user only gets the congratulatory message if the user previously received the inactivity alert (as described above in connection with
Further, based on behavioral change models, it is easier to change by defining and meeting small goals, instead of going for a hefty goal that may be difficult or impossible to achieve, resulting in a feeling of failure. By meeting small goals, the user gets a feeling of accomplishment.
In some embodiments, there are other conditions that must be met before generating the inactivity alert. For example, if the user starts an exercise (e.g., swimming, yoga), the inactivity alert is suspended. Also, if the user is sleeping or not wearing the activity tracking device, the inactivity alert is not generated. This means, that in order to generate the inactivity alert, the user must be wearing the activity tracking device and be awake.
Further, if the user configures the activity tracking device to cancel all alerts (e.g., “Do not disturb”), the inactivity alerts will not be presented. Also, if the user configures the activity tracking device to temporarily suspend inactivity alerts, the inactivity alerts will not be generated. More details are provided below with reference to
In some embodiments, if the user hits the hourly goal after receiving the inactivity alert, the user receives a celebratory alert, also referred to as congratulatory alert or message or a reward alert or message. For example, if the user reaches 250 steps before the hour expires, the user gets a congratulatory message.
In operation 222, the activity tracking device continues checking for reaching the interval goal (e.g., 250 steps) during the remaining time of the current interval. If the goal is not reached by the end of the current interval, the method flows to operation 224 where no action is taken. However, if the goal is reached during the remaining time of the current interval, the method flows to operation 226 where a vibration is generated. In one embodiment, the vibration of operations 206 (in
From operation 226, the method flows to operation 228 to check if messaging is possible in the activity tracking device. If messaging is possible, the method flows to operation 230 where a congratulatory message (see “achievement text” in
In other solutions, alerts are generated based on the amount of time that the user has been inactive, but those alerts can come at any random time and/or at an unexpected or inopportune time. However, presenting the inactivity alerts at expected times (such as the near-end times described herein), which can be configured or throttled by the user, provides a more positive and satisfying experience.
Most people have activities that are tied to the hour, so using hourly intervals is more successful for a higher percentage of people, because of the predictability of the inactivity alerts tied to the specific time on the hour.
The circle is used to show how much of the goal has been met within the hour, where the circle may have two different types of shading, or color, or any other distinctive visual clue to differentiate between percentage of goal accomplished and percentage of amount left to reach the goal. In
In one embodiment, a daily goal is also defined, as described in more detail below with reference to
As discussed above, some of the messages are accompanied by a vibration to call the user's attention towards meeting the hourly goal or the satisfaction of the hourly goal. Some activity trackers do not include a display, therefore, the activity alerts and messages may be displayed on a mobile device that is in communication with the activity tracker.
It is noted that the embodiments illustrated in
Each of the messages includes a graphic icon that identifies the area of information. For example, two footsteps within a circle represents the number of daily steps, heart icon represents the heart rate, etc. Regarding hourly goals, the information includes an icon for hourly goals (e.g., a silhouette of a person with her arms up in the air and one bent knee) followed by information regarding the hourly goals.
As discussed above with reference to
In
In some embodiments, the hourly-goal messages change to avoid monotony and to make the experience more interesting. In one embodiment, there is a plurality of inactivity alert messages (e.g., 15 messages) and a plurality of congratulatory messages (e.g., 20 messages). Therefore, the messages are selected at random, or following a linear order, or with some other selection criteria, to provide variety.
In one embodiment, a certain degree of randomness is combined with logic for selecting the messages. For example, the first three messages presented to the user for the inactivity alert include specific information (e.g., number of steps left to reach the goal), and the remainder of the messages include motivational information, but not necessarily the step count.
In one embodiment, the messages are defined as follows:
Where <n> represents the number of steps left to meet the goal. Other embodiments may include other messages, such as the number of steps taken during the current hour. Further, in some embodiments, the messages may be location or situation aware, such as, “it stopped raining, let's go!” “You're almost at home, keep walking,” “it's 7:55 PM, if you meet your hourly goal you will get the daily goal,” etc.
In one embodiment, the messages may be downloaded from a server to the tracker (e.g., via a mobile device). This way, the messages keep changing to keep the experience fresh. For example, the server sends the message to the mobile device, and then the mobile device syncs with the tracker by transferring the new messages to the tracker.
The interface 500 includes an hourly-goal area 502, a longest-sedentary-period area 504, and a daily-breakdown area 510. In the hourly-goal area 502, the interface shows whether the goal for each hourly goal has been met or not met. When the goal has been met, the circle is filled with a first color (e.g., red) and if the goal has not been met, the circle is filled with a different color (e.g., grey). In one embodiment, the circles are laid out on an arc, and the icon used for hourly goals is in the center. Additionally, a message indicating how many hourly goals have been met (e.g., “6 of 9 hours”) is presented, and a second message below providing additional information (e.g., “67% nicely done Nick!”).
It is noted that the time of the day for hourly goals is configurable by the user, which is able to define a time box for hourly goals. In the exemplary embodiment of
In some embodiments, a first goal of the GUIs described herein is to communicate an otherwise negative statistic in a positive way, and a second goal is to make the data as actionable as possible for the user. The graphic display for the hourly goals makes it easy to see if the user had “good” hours with step activity, and see when there were gaps which represented sedentary hours.
The sedentary time information accompanies inactivity alerts and gives users a sense for how active or sedentary users are during the day. For each day, the longest sedentary time is shown next to the last-30-day average for comparison. Area 504 for longest sedentary period includes two graph bars. The first bar 506 describes the longest sedentary period of the day, and a value is provided to the right of the bar indicating the actual length of the longest sedentary period (e.g., “2 hr 16 min”) and the actual time of the longest sedentary period (e.g., “11:45 AM-1:41 PM”).
The second bar 508 provides the 30-day average for the longest sedentary period, and the corresponding values to the right, the average duration (e.g., “1 hr 7 min”) and a message indicating it is the 30 day average. The first bar and the second bar are drawn to the same scale in order to visually compare the longest sedentary period of the day to the 30-day average. It is noted that the measurement of the longest sedentary period does not include times when the user is sleeping or not wearing the activity tracking device.
Showing the longest sedentary period helps the user identify the time of the day where the user is less active. This way, the user can prioritize efforts to become more active during the time when the user is more sedentary.
Daily-breakdown area 510 includes a bar divided into two segments: a first segment 512 for the active time and a second segment 514 for the sedentary time (e.g., the total sedentary time S described in more detail below). The length of each of the segments is proportional to the actual percentage of time during the day when the user was active or sedentary, respectively. In the exemplary embodiment of
Below, a legend is presented indicating the color of the segments and if they are for active or sedentary times, and the actual amount of time when the user was active and sedentary (e.g., 8 hr 23 min).
As used herein, active time is the amount of time that the user is active during the day. In one embodiment, the total sedentary time S is calculated with the following equation:
S=24 hrs−time not wearing tracker−time sleep−active time
In some embodiments, the active time described herein may be calculated based on a comparison of measured MET values to a MET threshold, as described in more detail elsewhere in this disclosure.
In some embodiments, the system may determine that the activity tracking device is not being worn using various techniques, such as determining based on a motion sensor of the activity tracking device that the activity tracking device is too still or exhibits too little motion or activity to be worn. Further, the system may determine that the user is asleep based on motion associated with sleep being detected by the motion sensor of the activity tracking device. In some embodiments, the activity tracking device may include a heart rate sensor (such as an optical heart rate sensor), which can be used to detect when the activity tracking device is not being worn or the user is asleep. For example, if the heart rate sensor does not detect a heart rate signal, the system may determine that the activity tracking device is not being worn. Further, if the heart rate sensor detects a heart rate signal associated with a sleep pattern, the system may determine that the user is asleep.
In some embodiments, the longest sedentary period may detected by first detecting discrete sedentary periods throughout the day (e.g., periods where measured MET values always or mostly remain below a predetermined threshold, such as 2). The system then excludes from these detected sedentary periods any sub-portions where the device is off-wrist or the user is sleeping. The system will then select the longest remaining sedentary period as the longest sedentary period.
In some embodiments, the longest sedentary period is more specifically calculated by first identifying periods of time in a day (e.g., minute long intervals) where the user is always or mostly below a METS threshold. In some cases, the sedentary periods are able to span short moments of higher activity (e.g., as measured by higher METs values), as described in U.S. Provisional Patent Application No. 62/137,750, filed Mar. 24, 2015, and entitled “Sedentary Period Detection Utilizing a Wearable Electronic Device”, which is herein incorporated by reference. Thereafter, the system described herein excludes, from the aforementioned sedentary periods, minutes where the user is asleep, or minutes where the device is off wrist and/or too still to be worn. The remaining sedentary minutes are then accumulated into contiguous sedentary periods (e.g., if at 3:59 pm and 4.31 pm the user's activity is classified as not sedentary, but if the user's activity is classified as sedentary for each of the minutes from 4 pm-4.30 pm, then the minutes from 4 pm-4.30 pm will be accumulated and classified as a single continuous sedentary period from 4 pm-4.30 pm). Of the remaining sedentary periods longer than a threshold value (e.g., longer than 10 minutes), the system selects the longest one as the longest sedentary period.
In some embodiments, the total sedentary time S is calculated as the summation of the sedentary periods detected in the process described above for identifying the longest sedentary period. In some embodiments, sedentary periods (detected in the process described above for identifying the longest sedentary period) that are shorter than 10 minutes, are classified as active time. Thus, in some embodiments, active time is detected based not only on METS being below or above a threshold, but also based on the relevant period being shorter or longer than some threshold length (e.g., 10 minutes). More information on determining active time is described in U.S. Provisional Patent Application No. 62/137,750, filed Mar. 24, 2015, and entitled “Sedentary Period Detection Utilizing a Wearable Electronic Device”, which is herein incorporated by reference.
Since the user has met all the hourly goals, a congratulatory message is displayed (e.g., “Boom!” and “Way to get all 9 of 9 hours”). In this embodiment, the hourly circles change color (e.g., to green) and are connected by a half-circle to signify that the daily goal has been reached. In this embodiment, the icon on area 502 is changed to a star, but other embodiments may include other icons.
Hourly-goal section 604 of interface 602 presents hourly-goal related information, with similar messages to the ones presented on the tracking device. For example, the message may be “3 of 9 hours with 250+”, but it could be other messages, such as “Are you ready to move?” 606, “Are you moving each hour?” 608, “3 of 14 hours with 250+” 610, “8 of 9 hours with 250+” 612, “9 of 9 hours with 250+” 614, “0 of 250 steps this hour” 616, “59 of 250 steps this hour” 618, etc.
The summary graph includes a matrix representation, or grid, of the hourly goals, where each hour is represented by a circle. If the goal was reached in that hour, the circle has a first color (e.g., red) and if the goal was not reached in that hour, the circle has a second color (e.g., black).
Each of the rows is for a different day and each column is for a different time of the day. The top row is for the current day (e.g., Wednesday in the exemplary embodiment) and the rows below show the previous days in descending order.
In one embodiment, if the daily goal is reached in one of the days, the matrix representation includes a line that joins the circles of that day representing that the daily goal was met (e.g., the daily goal was met on Sunday in
The grid representation quickly highlights patterns in hourly activity and when the user is not active. Further, the hourly presentation may be adjusted based on the time box defined by the user for tracking hourly goals.
In one embodiment, if the user selects one of the days listed below the grid representation, the details are provided for the hourly-goals reached during the selected day. Further, if the user scrolls down the list, the user gains access to older dates.
The interface of the mobile device allows the user to check hourly goals on the mobile device, such as how many steps the user needs to meet the goal for the current hour.
The graph is a bar graph with one horizontal bar for each day of the week. The length of the bars is proportional to the longest sedentary period for the day, and a vertical bar is added for the 30-day average.
In one embodiment, the configuration of the activity tracking device is performed on a mobile device that synchronizes the data with the tracking device, and/or a central server that keeps a database of user information. In another embodiment, the user is able to configure the tracking device utilizing a web interface to access the server.
In the exemplary embodiment of
The system allows the user to choose what hours in the day the user wants to track hourly goals to focus on being active, referred to herein as the time box. Therefore, the user does not have to meet hourly goals all the time, only the hours configured within the time box.
In one embodiment, the time box is customizable, meaning that the start time 706 and the end time 708 are customizable. However, in some embodiments, a minimum number of periods are required for tracking hourly goals (e.g., 5, 3, 7, but other values are also possible). Depending on the time box defined, the user interfaces will adapt to fit the time box. Further, the user is able to configure 710 in which days of the week the inactivity alerts will be provided.
In other embodiments, it is also possible to define other intervals besides one hour for interval goal tracking. For example, the user may configure two-hour intervals, or 90-minute intervals, etc.
It is noted that the embodiments illustrated in
Another message in a smaller font is presented below reciting, “Throughout your day, try getting 250 steps an hour. Fitbit will be right by your side, rooting for you!” This message is presented as an introduction to the user of the hourly-goal program to present inactivity alerts and longest sedentary time.
During a sync, tracker 106 transmits data to mobile device 108, which is then synced to cloud-based server 112. The server then uses the most recent data to calculate key metrics (e.g., 30-day average sedentary period, longest sedentary period, etc.). The server transmits these key metrics and user settings back to the mobile device. In one embodiment, the server also transmits user settings and inactivity alert and celebration message text strings to the tracker via the mobile device.
For synchronization purposes, a period of time referred to as epoch is utilized, and the epoch corresponds to period of time associated with a configured frequency for synchronizing.
As illustrated in
Mobile device 108 then syncs the data with server 112 and sends one or more of the step count per epoch, the activity level per epoch, the log of inactivity alerts, and the log of celebration alerts.
When the tracker and the mobile device are connected, the tracker transmits the live steps this hour and/or live total daily steps to the mobile device, enabling the mobile device to display this information. This allows the user to see each step taken this hour, or how many steps left to reach the hourly goal (e.g., “234 out of 250 steps this hour.”)
When the tracker 106 synchronizes with server 112 via mobile device 108, the server 112 sends to the mobile device one or more of the total daily sedentary time, the total daily active time, the longest sedentary period, the hourly step activity, the alert and celebration message text strings, and user settings. As illustrated in
Afterwards, the mobile device sends the tracker one or more of the alert and congratulatory messages text strings, and the user settings. Tracker 106 then generates the inactivity alerts and congratulatory messages, as described above.
After the hold period expires, the tracker will resume to automatically generate inactivity alerts without requiring user input to reconfigure the tracker, that is, the user does not need to remember to turn inactivity alerts back on. The tracker will continue to track inactivity data (e.g., steps taken this hour) through the hold period, but the tracker will not generate the inactivity alerts or celebration messages.
The ability to auto-resume inactivity alerts is important because users often forget to turn inactivity alerts back on again. Also, it is more convenient for the user to avoid having to reconfigure inactivity alerts.
In one embodiment, the mobile device interface includes an option for configuring the hold period. In one embodiment, the user is provided with four options: “Edit settings,” “Turn off alerts this hour,” “Turn off alerts next 2 hours,” and “Turn off alerts today.”
The “Edit settings” option allows the user to enter a different menu for configuring additional options, such as placing the device on hold for several days, or between specific times, a default amount of hold period, holidays, days of the week, etc.
If the user selects the option “Turn off alerts this hour,” the inactivity alerts will be suspended for the remainder of present hour. For example, if it is 8:12 AM and the user turns off alerts for this hour, the alerts will be inactive until 9:00 AM.
If the user selects the option “Turn off alerts next two hours,” the inactivity alerts will be suspended for the remainder of the present hour and the next hour. For example, if it is 8:12 AM and the user turns off alerts for two hours, the alerts will be inactive until 10:00 AM. If the user is currently in the last hour of the time box defined for inactivity alerts, selecting the option to turn off alerts for 2 hours will place a hold for the rest of the day, but not for the next tracked hour on the next day.
If the user selects the option “Turn off alerts today,” the inactivity alerts will be suspended for the remainder of the day. For example, if it is 8:12 AM and the user turns off alerts for today, the alerts will be inactive until the beginning of the time box the next day.
In other embodiments, placing the hold on inactivity alerts may also be performed via user interface on the tracker device itself. For example, the user may select a “Settings” option, followed by an option to configure inactivity alerts, and then an option for “Hold.” As in the case of the mobile device interface, the user may place a hold for this hour, the next 2 hours, today, etc.
It is noted that the embodiments illustrated in
In some embodiments, hold periods may also be generated when other conditions are met, such as when the user is having a meeting which is detected on a calendar application of the user. Also, if the user is asleep, no inactivity alerts are generated so the user is not disturbed. Further, no inactivity alerts are generated when the user is not wearing the tracker.
In another embodiment, the alerts are also placed on hold if it is determined that the user is already exercising, such as in a yoga class, or some other predefined activity. For example, the MET may indicate that the user is exercising but not taking steps. In this case, the inactivity alerts will be placed on hold. Additionally, inactivity alerts may be placed on hold for a predetermined amount of time after the user has finished exercising, because it may be annoying to receive reminders after the user has finished exercising (e.g., while the user is cooling-down or resting after exercising).
In addition, a hold period may be generated automatically by the tracker 106 when it is detected that the user has woken up within the current hour, which is being tracked for an hourly goal. If the user has had at least 15 minutes of sleep (other time periods are also possible) in the current hour, the inactivity alert will not be generated. For example, if the time box is defined between 7 AM and 5 PM, and the user gets up at 7:30 AM, then an alert is not generated at 7:50 AM because it would be a negative experience for the user (e.g., perhaps the user doesn't want to be bothered after getting up late on the weekend).
In another embodiment, the user is able to set “alert-free zones” based on location. For example, a configurable parameter may be set to stop the generation of inactivity alerts when the user is at a hospital, or at a church, or visiting a friend, etc.
In other embodiments, other hold periods may be defined. For example, the user may select to turn off alerts for exactly three hours. This way, if it is 12:55 PM and the user places a hold for exactly 3 hours, alerts will not be generated between 12:55 PM and 3:55 PM, and if at 3:55 PM the user has less than the hourly goal (e.g., 250 steps) then and inactivity alert will be generated at exactly 3:55 PM. In another embodiment, the user may select to turn of alerts for three hours, with the alerts resuming only at the start of the next full clock hour after the expiration of the three hours. For example, if it is 12:55 PM and the user places a hold for 3 hours, alerts will not be generated between 12:55 PM and 4 PM, and if at 4:55 PM the user has less than the hourly goal (e.g., 250 steps for the 4 PM-5 PM hourly interval), then an inactivity alert will be generated at exactly 4:55 PM.
The dashboard provides information related to the activity tracking device, and allows for configuration of the activity tracking device parameters. In addition, the dashboard provides statistical data, such as history over the last week, or month, graphs for daily heart rates, etc. Further yet, the dashboard provides a list of friends connected to the user, enabling for social activities associated with fitness.
The dashboard includes an area 118 that presents information regarding hourly goals and sedentary time, similar to the interfaces described above for a mobile device. For example, area 118 presents an icon for the hourly goals, with an arc above having circles corresponding to the hourly goals, and account of the steps taken in the current hour.
If the user selects area 118, a new page is open with more detailed information and configuration options (e.g., time box, hold periods, hourly goal, etc.). Further, the user is able to access social components for the inactivity tracking to challenge or compare achievements with friends.
In one embodiment, the user is able to send messages to friends, and these messages are presented if the hourly goal is not met, providing a more personal and fun experience. In addition, the system may present leaderboards, badges, cheering messages, taunting messages, etc. The viral interactions may also apply to sedentary time, for example, to challenge a friend on who has the shortest sedentary period for the day, or to challenge a friend on who has the shortest 30-day average for the longest sedentary period, etc.
In operation 252, motion data is captured using one or more sensors of an activity tracking device when worn by a user. The sensors may be biometric sensors, or motion sensors, or any other type of sensor configured to detect user activity. From operation 252, the method flows to operation 254 for determining, based on the motion data, one or more sedentary time periods associated with motion data indicating that the user is sedentary.
From operation 254, the method flows to operation 256 for determining, based on output of the one or more sensors, a first set of one or more time intervals when the user is asleep. In operation 258, a second set of one or more time intervals when the user is not wearing the activity tracking device is determined, based on the output of the one or more sensors.
From operation 258, the method flows to operation 260 where the longest sedentary period for a day is calculated where the user is sedentary, awake, and wearing the activity tracking device, based on excluding the first set of one or more time intervals and the second set of one or more time intervals from the one or more sedentary time periods. From operation 260, the method flows to operation 262 for displaying on the activity tracking device information describing the longest sedentary period.
Operation 272 is for capturing motion data using an activity tracking device when worn by a user. From operation 272, the method flows to operation 274 where one or more intervals of time during a day are identified. Each interval includes a start time and an end time, where a near-end time is defined between the start time and the end time.
From operation 274, the method flows to operation 276 for generating a first notification for display on the activity tracking device when the near-end time of a current interval is reached and a number of steps taken by the user during the current interval is less than a goal defined by a predetermined number of steps.
Further, from operation 276, the method flows to operation 278 for receiving, by the activity tracking device, a hold command from a computing device, the hold command includes a hold period. In operation 280, the generating of the first notification is suspended during the hold period in response to the hold command.
From operation 280, the method flows to operation 282 where the generation of the first notification is resumed, without requiring user input, after the hold period expires.
In operation 352, motion data is captured using an activity tracking device when worn by a user, and in operation 354, the method identifies a plurality of intervals of time during a day, each interval including a start time, an end time, and an interval goal defined by a predetermined number of steps to be taken by the user during the interval.
From operation 354, the method flows to operation 356 where the number of steps taken during the current interval is determined, between the start time and the end time of the current interval. From operation 356, the method flows to operations 358, and responsive to determining that the number of steps taken during the current interval is less than the interval goal, the activity tracking device presents a first message indicating the number of steps taken during the current interval. In an alternative embodiment, the first message indicates the number of steps left to meet the interval goal during the current interval.
From operation 358, the method flows to operation 360, where responsive to determining that the user meets the interval goal during the current interval, the activity tracking device presents a second message indicating in how many intervals of a current day the interval goal was reached.
In operation 372, motion data is captured using an activity tracking device when the activity tracking device is worn by a user. From operation 372, the method flows to operation 374 where the motion data is stored in memory of the activity tracking device.
From operation 374, the method flows to operation 376 for identifying one or more intervals of time during a day. Each interval includes a start time and an end time, and a near-end time is defined between the start time and the end time. From operation 376, the method flows to operation 378 where the tracking device detects that an interval has begun.
From operation 378, the method flows to operation 380 where the step count for the interval is started. In operation 382 a determination is made of the number of steps taken by the user during the current interval based on the motion data.
From operation 382, the method flows to operation 384 where a check is made to determine if the number of steps taken is greater than or equal to a goal defined by a predetermined number of steps to be taken by the user during the interval. If the number of steps is greater than or equal to the goal, the method flows back to operation 378 to wait for the beginning of the next interval. This means, that no inactivity messages are generated if the user has met the goal during the current interval.
If the number of the steps is less than the goal, the method flows to operation 386 where another check is made to determine if the near-end time of the current interval has been reached (e.g., 10 minutes before the hour). If the near-end time has not been reached, the method flows back to operation 384, if the near-end time has been reached the method flows to operation 388, where a first notification is presented on the display of the activity tracking device.
From operation 388, the method flows to operation 390 where a check is made to determine if the number of steps taken during the current interval is greater than or equal to the goal. If so, the method flows to operation 394, where a second notification is presented on the display of the activity tracking device to congratulate the user for accomplishing the goal during the current interval.
If the check of operation 390 is negative, the method flows to operation 392 where a check is made to determine if the end of the interval has been reached. If the end of the interval has not been reached, the method flows back to operation 390, and if the end of the interval has been reached, the method flows back to operation 378 to wait for the beginning of the next interval. From operation 394, the method also flows back to operation 378 to wait for the beginning of the next interval.
Examples of environmental sensors 158 include a barometric pressure sensor, a weather condition sensor, a light exposure sensor, a noise exposure sensor, a radiation exposure sensor, and a magnetic field sensor. Examples of a weather condition sensor include sensors for measuring temperature, humidity, pollen count, air quality, rain conditions, snow conditions, wind speed, or any combination thereof, etc. Examples of light exposure sensors include sensors for ambient light exposure, ultraviolet (UV) light exposure, or a combination thereof, etc. Examples of air quality sensors include sensors for measuring particulate counts for particles of different sizes, level of carbon dioxide in the air, level of carbon monoxide in the air, level of methane in the air, level of other volatile organic compounds in the air, or any combination thereof.
Examples of the position/motion sensor 160 include an accelerometer, a gyroscope, a rotary encoder, a calorie measurement sensor, a heat measurement sensor, a moisture measurement sensor, a displacement sensor, an ultrasonic sensor, a pedometer, an altimeter, a linear position sensor, an angular position sensor, a multi-axis position sensor, or any combination thereof, etc. In some embodiments, the position/motion sensor 160 measures a displacement (e.g., angular displacement, linear displacement, or a combination thereof, etc.) of the monitoring device 152 over a period of time with reference to a three-dimensional coordinate system to determine an amount of activity performed by the user during a period of time. In some embodiments, a position sensor includes a biological sensor, which is further described below.
The vibrotactile module 164 provides sensory output to the user by vibrating portable device 152. Further, the communications module 166 is operable to establish wired or wireless connections with other electronic devices to exchange data (e.g., activity data, geo-location data, location data, a combination thereof, etc.). Examples of wireless communication devices include, but are not limited to, a Wi-Fi adapter, a Bluetooth device, an Ethernet adapter, an infrared adapter, an ultrasonic adapter, etc.
The touchscreen 206 may be any type of display with touch sensitive functions. In another embodiment, a display is included but the display does not have touch-sensing capabilities. The touchscreen may be able to detect a single touch, multiple simultaneous touches, gestures defined on the display, etc. The display driver 168 interfaces with the touchscreen 206 for performing input/output operations. In one embodiment, display driver 168 includes a buffer memory for storing the image displayed on touchscreen 206. The buttons/user interface may include buttons, switches, cameras, USB ports, keyboards, or any other device that can provide input or output functions.
Device locator 172 provides capabilities for acquiring data related to the location (absolute or relative) of monitoring device 152. Examples device locators 172 include a GPS transceiver, a mobile transceiver, a dead-reckoning module, a camera, etc. As used herein, a device locator may be referred to as a device or circuit or logic that can generate geo-location data. The geo-location data provides the absolute coordinates for the location of the monitoring device 152. The coordinates may be used to place the monitoring device 152 on a map, in a room, in a building, etc. In some embodiments, a GPS device provides the geo-location data. In other embodiments, the geo-location data can be obtained or calculated from data acquired from other devices (e.g., cell towers, Wi-Fi device signals, other radio signals, etc.), which can provide data points usable to locate or triangulate a location.
External event analyzer 174 receives data regarding the environment of the user and determines external events that might affect the power consumption of the user. For example, the external event analyzer 174 may determine low light conditions in a room, and assume that there is a high probability that the user is sleeping. In addition, the external event analyzer 174 may also receive external data, such as GPS location from a smart phone, and determine that the user is on a vehicle and in motion.
In some embodiments, the processor 154 receives one or more geo-locations measured by the device locator 172 over a period of time and determines a location of the monitoring device 152 based on the geo-locations and/or based on one or more selections made by the user, or based on information available within a geo-location-location database of the network. For example, the processor 154 may compare the current location of the monitoring device against known locations in a location database, to identify presence in well-known points of interest to the user or to the community. In one embodiment, upon receiving the geo-locations from the device locator 172, the processor 154 determines the location based on the correspondence between the geo-locations and the location in the geo-location-location database.
The one or more environmental sensors 158 may sense and determine one or more environmental parameters (e.g., barometric pressure, weather condition, amount of light exposure, noise levels, radiation levels, magnetic field levels, or a combination thereof, etc.) of an environment in which the monitoring device is placed.
The watch 162 is operable to determine the amount of time elapsed between two or more events. In one embodiment, the events are associated with one or more positions sensed by the position sensor 160, associated with one or more environmental parameters determined by the environmental sensor 158, associated with one or more geo-locations determined by the device locator 172, and/or associated with one or more locations determined by the processor 154.
Power controller 178 manages and adjusts one or more power operational parameters defined for the monitoring device 152. In one embodiment, the power operational parameters include options for managing the touchscreen 206, such as by determining when to turn ON or OFF the touchscreen, scan rate, brightness, etc. In addition, the power controller 178 is operable to determine other power operational parameters, besides the parameters associated with the touchscreen, such as determining when to turn ON or OFF other modules (e.g., GPS, environmental sensors, etc.) or limiting the frequency of use for one or more of the modules within monitoring device 152.
Monitoring device 152 may have a variety of internal states and/or events which may dynamically change the characteristics of the touchscreen or of other modules. These states may include one or more of the following:
It is noted that these states may be communicated to the user through one or more methods including, but not limited to, displaying them visually, outputting an audio alert, and/or haptic feedback.
In some embodiments, data analysis of data produced by different modules may be performed in monitoring device 152, in other device in communication with monitoring device 152, or in combination of both devices. For example, the monitoring device may be generating a large amount of data related to the heart rate of the user. Before transmitting the data, the monitoring device 152 may process the large amount of data to synthesize information regarding the heart rate, and then the monitoring device 152 may send the data to a server that provides an interface to the user. For example, the monitoring device may provide summaries of the heart rate in periods of one minute, 30 seconds, five minutes, 50 minutes, or any other time period. By performing some calculations in the monitoring device 152, the processing time required to be performed on the server is decreased.
Some other data may be sent in its entirety to another device, such as steps the user is taken, or periodical updates on the location of the monitoring device 152. Other calculations may be performed in the server, such as analyzing data from different modules to determine stress levels, possible sickness by the user, etc.
It is noted that the embodiments illustrated in
More details regarding sedentary times and activity monitoring may be found in U.S. Provisional Patent Application No. 62/137,750, filed Mar. 24, 2015, and entitled “Sedentary Period Detection Utilizing a Wearable Electronic Device”, and in U.S. patent application Ser. No. 14/156,413, filed Jan. 15, 2014, and entitled “Portable Monitoring Devices For Processing Applications and Processing Analysis of Physiological Conditions of a User associated with the Portable Monitoring Device.” Both patent applications are herein incorporated by reference. The materials described in this patent applications may be combined with the embodiments presented herein.
In one embodiment, the device collects one or more types of physiological and/or environmental data from embedded sensors and/or external devices and communicates or relays such metric information to other devices, including devices capable of serving as Internet-accessible data sources, thus permitting the collected data to be viewed, for example, using a web browser or network-based application. For example, while the user is wearing an activity tracking device, the device may calculate and store the user's step count using one or more sensors. The device then transmits data representative of the user's step count to an account on a web service, computer, mobile phone, or health station where the data may be stored, processed, and visualized by the user. Indeed, the device may measure or calculate a plurality of other physiological metrics in addition to, or in place of, the user's step count.
Some physiological metrics include, but are not limited to, energy expenditure (for example, calorie burn), floors climbed and/or descended, heart rate, heart rate variability, heart rate recovery, location and/or heading (for example, through GPS), elevation, ambulatory speed and/or distance traveled, swimming lap count, bicycle distance and/or speed, blood pressure, blood glucose, skin conduction, skin and/or body temperature, electromyography, electroencephalography, weight, body fat, caloric intake, nutritional intake from food, medication intake, sleep periods (e.g., clock time), sleep phases, sleep quality and/or duration, pH levels, hydration levels, and respiration rate. The device may also measure or calculate metrics related to the environment around the user such as barometric pressure, weather conditions (for example, temperature, humidity, pollen count, air quality, rain/snow conditions, wind speed), light exposure (for example, ambient light, UV light exposure, time and/or duration spent in darkness), noise exposure, radiation exposure, and magnetic field.
Still further, other metrics can include, without limitation, calories burned by a user, weight gained by a user, weight lost by a user, stairs ascended, e.g., climbed, etc., by a user, stairs descended by a user, steps taken by a user during walking or running, a number of rotations of a bicycle pedal rotated by a user, sedentary activity data, driving a vehicle, a number of golf swings taken by a user, a number of forehands of a sport played by a user, a number of backhands of a sport played by a user, or a combination thereof. In some embodiments, sedentary activity data is referred to herein as inactive activity data or as passive activity data. In some embodiments, when a user is not sedentary and is not sleeping, the user is active. In some embodiments, a user may stand on a monitoring device that determines a physiological parameter of the user. For example, a user stands on a scale that measures a weight, a body fat percentage, a biomass index, or a combination thereof, of the user.
Furthermore, the device or the system collating the data streams may calculate metrics derived from this data. For example, the device or system may calculate the user's stress and/or relaxation levels through a combination of heart rate variability, skin conduction, noise pollution, and sleep quality. In another example, the device or system may determine the efficacy of a medical intervention (for example, medication) through the combination of medication intake, sleep and/or activity data. In yet another example, the device or system may determine the efficacy of an allergy medication through the combination of pollen data, medication intake, sleep and/or activity data. These examples are provided for illustration only and are not intended to be limiting or exhaustive.
This information can be associated to the users account, which can be managed by an activity management application on the server. The activity management application can provide access to the users account and data saved thereon. The activity manager application running on the server can be in the form of a web application. The web application can provide access to a number of websites screens and pages that illustrate information regarding the metrics in various formats. This information can be viewed by the user, and synchronized with a computing device of the user, such as a smart phone.
In one embodiment, the data captured by the activity tracking device 102/106 is received by the computing device, and the data is synchronized with the activity measured application on the server. In this example, data viewable on the computing device (e.g., smart phone) using an activity tracking application (app) can be synchronized with the data present on the server, and associated with the user's account.
The user can therefore access the data associated with the user account using any device having access to the Internet. Data received by the network 176 can then be synchronized with the user's various devices, and analytics on the server can provide data analysis to provide recommendations for additional activity, and or improvements in physical health. The process therefore continues where data is captured, analyzed, synchronized, and recommendations are produced. In some embodiments, the captured data can be itemized and partitioned based on the type of activity being performed, and such information can be provided to the user on the website via graphical user interfaces, or by way of the application executed on the users smart phone (by way of graphical user interfaces).
In one embodiment, the sensor or sensors of a device 102/106 can determine or capture data to determine an amount of movement of the monitoring device over a period of time. The sensors can include, for example, an accelerometer, a magnetometer, a gyroscope, or combinations thereof. Broadly speaking, these sensors are inertial sensors, which capture some movement data, in response to the device 102/106 being moved. The amount of movement (e.g., motion sensed) may occur when the user is performing an activity of climbing stairs over the time period, walking, running, etc. The monitoring device may be worn on a wrist, carried by a user, worn on clothing (using a clip, or placed in a pocket), attached to a leg or foot, attached to the user's chest, waist, or integrated in an article of clothing such as a shirt, hat, pants, blouse, glasses, and the like. These examples are not limiting to all the possible ways the sensors of the device can be associated with a user or thing being monitored.
In other embodiments, a biological sensor or biometric can determine any number of physiological characteristics of a user. As another example, the biological sensor may determine heart rate, a hydration level, body fat, bone density, fingerprint data, sweat rate, and/or a bioimpedance of the user. Examples of the biological sensors include, without limitation, a physiological parameter sensor, a pedometer, or a combination thereof.
In some embodiments, data associated with the user's activity can be monitored by the applications on the server and the users device, and activity associated with the user's friends, acquaintances, or social network peers can also be shared, based on the user's authorization. This provides for the ability for friends to compete regarding their fitness, achieve goals, receive badges for achieving goals, get reminders for achieving such goals, rewards or discounts for achieving certain goals, etc.
As noted, an activity tracking device 102/106 can communicate with a computing device (e.g., a smartphone, a tablet computer, a desktop computer, or computer device having wireless communication access and/or access to the Internet). The computing device, in turn, can communicate over a network, such as the Internet or an Intranet to provide data synchronization. The network may be a wide area network, a local area network, or a combination thereof. The network may be coupled to one or more servers, one or more virtual machines, or a combination thereof. A server, a virtual machine, a controller of a monitoring device, or a controller of a computing device is sometimes referred to herein as a computing resource. Examples of a controller include a processor and a memory device.
In one embodiment, the processor may be a general purpose processor. In another embodiment, the processor can be a customized processor configured to run specific algorithms or operations. Such processors can include digital signal processors (DSPs), which are designed to execute or interact with specific chips, signals, wires, and perform certain algorithms, processes, state diagrams, feedback, detection, execution, or the like. In some embodiments, a processor can include or be interfaced with an application specific integrated circuit (ASIC), a programmable logic device (PLD), a central processing unit (CPU), or a combination thereof, etc.
In some embodiments, one or more chips, modules, devices, or logic can be defined to execute instructions or logic, which collectively can be viewed or characterized to be a processor. Therefore, it should be understood that a processor does not necessarily have to be one single chip or module, but can be defined from a collection of electronic or connecting components, logic, firmware, code, and combinations thereof.
Examples of a memory device include a random access memory (RAM) and a read-only memory (ROM). A memory device may be a Flash memory, a redundant array of disks (RAID), a hard disk, or a combination thereof.
Embodiments described in the present disclosure may be practiced with various computer system configurations including hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like. Several embodiments described in the present disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a wire-based or wireless network.
With the above embodiments in mind, it should be understood that a number of embodiments described in the present disclosure can employ various computer-implemented operations involving data stored in computer systems. These operations are those requiring physical manipulation of physical quantities. Any of the operations described herein that form part of various embodiments described in the present disclosure are useful machine operations. Several embodiments described in the present disclosure also relate to a device or an apparatus for performing these operations. The apparatus can be specially constructed for a purpose, or the apparatus can be a computer selectively activated or configured by a computer program stored in the computer. In particular, various machines can be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.
Various embodiments described in the present disclosure can also be embodied as computer-readable code on a non-transitory computer-readable medium. The computer-readable medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of the computer-readable medium include hard drives, network attached storage (NAS), ROM, RAM, compact disc-ROMs (CD-ROMs), CD-recordables (CD-Rs), CD-rewritables (RWs), magnetic tapes and other optical and non-optical data storage devices. The computer-readable medium can include computer-readable tangible medium distributed over a network-coupled computer system so that the computer-readable code is stored and executed in a distributed fashion.
Although the method operations were described in a specific order, it should be understood that other housekeeping operations may be performed in between operations, or operations may be performed in an order other than that shown, or operations may be adjusted so that they occur at slightly different times, or may be distributed in a system which allows the occurrence of the processing operations at various intervals associated with the processing.
Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications can be practiced within the scope of the appended claims. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the various embodiments described in the present disclosure are not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 2717736 | Schlesinger | Sep 1955 | A |
| 2827309 | Fred | Mar 1958 | A |
| 2883255 | Anderson | Apr 1959 | A |
| 3163856 | Kirby | Dec 1964 | A |
| 3250270 | Walter | May 1966 | A |
| 3522383 | Chang | Jul 1970 | A |
| 3918658 | Beller | Nov 1975 | A |
| 4192000 | Lipsey | Mar 1980 | A |
| 4244020 | Ratcliff | Jan 1981 | A |
| 4281663 | Pringle | Aug 1981 | A |
| 4284849 | Anderson et al. | Aug 1981 | A |
| 4312358 | Barney | Jan 1982 | A |
| 4367752 | Jimenez et al. | Jan 1983 | A |
| 4390922 | Pelliccia | Jun 1983 | A |
| 4407295 | Steuer et al. | Oct 1983 | A |
| 4425921 | Fujisaki et al. | Jan 1984 | A |
| 4575804 | Ratcliff | Mar 1986 | A |
| 4578769 | Frederick | Mar 1986 | A |
| 4617525 | Lloyd | Oct 1986 | A |
| 4855942 | Bianco | Aug 1989 | A |
| 4887249 | Thinesen | Dec 1989 | A |
| 4930518 | Hrushesky | Jun 1990 | A |
| 4977509 | Pitchford et al. | Dec 1990 | A |
| 5058427 | Brandt | Oct 1991 | A |
| 5224059 | Nitta et al. | Jun 1993 | A |
| 5295085 | Hoffacker | Mar 1994 | A |
| 5314389 | Dotan | May 1994 | A |
| 5323650 | Fullen et al. | Jun 1994 | A |
| 5365930 | Takashima et al. | Nov 1994 | A |
| 5446705 | Haas et al. | Aug 1995 | A |
| 5456648 | Edinburg et al. | Oct 1995 | A |
| 5485402 | Smith | Jan 1996 | A |
| 5553296 | Forrest et al. | Sep 1996 | A |
| 5583776 | Levi et al. | Dec 1996 | A |
| 5645509 | Brewer et al. | Jul 1997 | A |
| 5671162 | Werbin | Sep 1997 | A |
| 5704350 | Williams, III | Jan 1998 | A |
| 5724265 | Hutchings | Mar 1998 | A |
| 5817008 | Rafert et al. | Oct 1998 | A |
| 5890128 | Diaz et al. | Mar 1999 | A |
| 5891042 | Sham et al. | Apr 1999 | A |
| 5894454 | Kondo | Apr 1999 | A |
| 5899963 | Hutchings | May 1999 | A |
| 5941828 | Archibald et al. | Aug 1999 | A |
| 5947868 | Dugan | Sep 1999 | A |
| 5955667 | Fyfe | Sep 1999 | A |
| 5976083 | Richardson et al. | Nov 1999 | A |
| 6018705 | Gaudet | Jan 2000 | A |
| 6077193 | Buhler et al. | Jun 2000 | A |
| 6078874 | Piety et al. | Jun 2000 | A |
| 6085248 | Sambamurthy et al. | Jul 2000 | A |
| 6129686 | Friedman | Oct 2000 | A |
| 6145389 | Ebeling | Nov 2000 | A |
| 6183425 | Whalen et al. | Feb 2001 | B1 |
| 6213872 | Harada | Apr 2001 | B1 |
| 6241684 | Amano et al. | Jun 2001 | B1 |
| 6287262 | Amano et al. | Sep 2001 | B1 |
| 6301964 | Fyfe et al. | Oct 2001 | B1 |
| 6302789 | Harada et al. | Oct 2001 | B2 |
| 6305221 | Hutchings | Oct 2001 | B1 |
| 6309360 | Mault | Oct 2001 | B1 |
| 6469639 | Tanenhaus et al. | Oct 2002 | B2 |
| 6478736 | Mault | Nov 2002 | B1 |
| 6513381 | Fyfe et al. | Feb 2003 | B2 |
| 6513532 | Mault et al. | Feb 2003 | B2 |
| 6527711 | Stivoric et al. | Mar 2003 | B1 |
| 6529827 | Beason et al. | Mar 2003 | B1 |
| 6558335 | Thede | May 2003 | B1 |
| 6561951 | Cannon et al. | May 2003 | B2 |
| 6571200 | Mault | May 2003 | B1 |
| 6585622 | Shum et al. | Jul 2003 | B1 |
| 6607493 | Song | Aug 2003 | B2 |
| 6620078 | Pfeffer | Sep 2003 | B2 |
| 6678629 | Tsuji | Jan 2004 | B2 |
| 6699188 | Wessel | Mar 2004 | B2 |
| 6761064 | Tsuji | Jul 2004 | B2 |
| 6772331 | Hind et al. | Aug 2004 | B1 |
| 6790178 | Mault et al. | Sep 2004 | B1 |
| 6808473 | Hisano et al. | Oct 2004 | B2 |
| 6811516 | Dugan | Nov 2004 | B1 |
| 6813582 | Levi et al. | Nov 2004 | B2 |
| 6813931 | Yadav et al. | Nov 2004 | B2 |
| 6856938 | Kurtz | Feb 2005 | B2 |
| 6862575 | Anttila et al. | Mar 2005 | B1 |
| 7041032 | Calvano | May 2006 | B1 |
| 7062225 | White | Jun 2006 | B2 |
| 7099237 | Lall | Aug 2006 | B2 |
| 7133690 | Ranta-Aho et al. | Nov 2006 | B2 |
| 7162368 | Levi et al. | Jan 2007 | B2 |
| 7171331 | Vock et al. | Jan 2007 | B2 |
| 7200517 | Darley et al. | Apr 2007 | B2 |
| 7246033 | Kudo | Jul 2007 | B1 |
| 7261690 | Teller et al. | Aug 2007 | B2 |
| 7272982 | Neuhauser et al. | Sep 2007 | B2 |
| 7285090 | Stivoric et al. | Oct 2007 | B2 |
| 7373820 | James | May 2008 | B1 |
| 7443292 | Jensen et al. | Oct 2008 | B2 |
| 7457724 | Vock et al. | Nov 2008 | B2 |
| 7467060 | Kulach et al. | Dec 2008 | B2 |
| 7502643 | Farringdon et al. | Mar 2009 | B2 |
| 7505865 | Ohkubo et al. | Mar 2009 | B2 |
| 7539532 | Tran | May 2009 | B2 |
| 7558622 | Tran | Jul 2009 | B2 |
| 7559877 | Parks et al. | Jul 2009 | B2 |
| 7608050 | Shugg | Oct 2009 | B2 |
| 7640134 | Park | Dec 2009 | B2 |
| 7653508 | Kahn et al. | Jan 2010 | B1 |
| 7690556 | Kahn et al. | Apr 2010 | B1 |
| 7713173 | Shin et al. | May 2010 | B2 |
| 7762952 | Lee et al. | Jul 2010 | B2 |
| 7771320 | Riley et al. | Aug 2010 | B2 |
| 7774156 | Niva et al. | Aug 2010 | B2 |
| 7789802 | Lee et al. | Sep 2010 | B2 |
| 7881902 | Kahn et al. | Feb 2011 | B1 |
| 7927253 | Vincent et al. | Apr 2011 | B2 |
| 7942824 | Kayyali et al. | May 2011 | B1 |
| 7953549 | Graham et al. | May 2011 | B2 |
| 7959539 | Takeishi | Jun 2011 | B2 |
| 7983876 | Vock et al. | Jul 2011 | B2 |
| 8005922 | Boudreau et al. | Aug 2011 | B2 |
| 8028443 | Case, Jr. | Oct 2011 | B2 |
| 8055469 | Kulach et al. | Nov 2011 | B2 |
| 8099318 | Moukas et al. | Jan 2012 | B2 |
| 8132037 | Fehr et al. | Mar 2012 | B2 |
| 8172761 | Rulkov et al. | May 2012 | B1 |
| 8177260 | Tropper et al. | May 2012 | B2 |
| 8180591 | Yuen et al. | May 2012 | B2 |
| 8180592 | Yuen et al. | May 2012 | B2 |
| 8270297 | Akasaka et al. | Sep 2012 | B2 |
| 8311769 | Yuen et al. | Nov 2012 | B2 |
| 8311770 | Yuen et al. | Nov 2012 | B2 |
| 8386008 | Yuen et al. | Feb 2013 | B2 |
| 8437980 | Yuen et al. | May 2013 | B2 |
| 8462591 | Marhaben | Jun 2013 | B1 |
| 8463576 | Yuen et al. | Jun 2013 | B2 |
| 8463577 | Yuen et al. | Jun 2013 | B2 |
| 8487771 | Hsieh et al. | Jul 2013 | B2 |
| 8533269 | Brown | Sep 2013 | B2 |
| 8533620 | Hoffman et al. | Sep 2013 | B2 |
| 8543185 | Yuen et al. | Sep 2013 | B2 |
| 8543351 | Yuen et al. | Sep 2013 | B2 |
| 8548770 | Yuen et al. | Oct 2013 | B2 |
| 8562489 | Burton et al. | Oct 2013 | B2 |
| 8583402 | Yuen et al. | Nov 2013 | B2 |
| 8597093 | Engelberg et al. | Dec 2013 | B2 |
| 8634796 | Johnson | Jan 2014 | B2 |
| 8638228 | Amigo et al. | Jan 2014 | B2 |
| 8670953 | Yuen et al. | Mar 2014 | B2 |
| 8684900 | Tran | Apr 2014 | B2 |
| 8690578 | Nusbaum et al. | Apr 2014 | B1 |
| 8712723 | Kahn | Apr 2014 | B1 |
| 8734296 | Brumback | May 2014 | B1 |
| 8738321 | Yuen et al. | May 2014 | B2 |
| 8738323 | Yuen et al. | May 2014 | B2 |
| 8738925 | Park | May 2014 | B1 |
| 8744803 | Park et al. | Jun 2014 | B2 |
| 8762101 | Yuen et al. | Jun 2014 | B2 |
| 8764651 | Tran | Jul 2014 | B2 |
| 8825445 | Hoffman | Sep 2014 | B2 |
| 8847988 | Geisner et al. | Sep 2014 | B2 |
| 8849610 | Molettiere | Sep 2014 | B2 |
| 8868377 | Yuen et al. | Oct 2014 | B2 |
| 8909543 | Tropper et al. | Dec 2014 | B2 |
| 8949070 | Kahn et al. | Feb 2015 | B1 |
| 8954290 | Yuen et al. | Feb 2015 | B2 |
| 8961414 | Teller et al. | Feb 2015 | B2 |
| 8968195 | Tran | Mar 2015 | B2 |
| 9031812 | Roberts | May 2015 | B2 |
| 9042971 | Brumback et al. | May 2015 | B2 |
| 9047648 | Lekutai et al. | Jun 2015 | B1 |
| 9062976 | Tanabe | Jun 2015 | B2 |
| 9066209 | Yuen | Jun 2015 | B2 |
| 9288298 | Choudhary | Mar 2016 | B2 |
| 9310909 | Myers | Apr 2016 | B2 |
| 20010049470 | Mault et al. | Dec 2001 | A1 |
| 20010055242 | Deshmuhk et al. | Dec 2001 | A1 |
| 20020013717 | Ando et al. | Jan 2002 | A1 |
| 20020019585 | Dickenson | Feb 2002 | A1 |
| 20020077219 | Cohen et al. | Jun 2002 | A1 |
| 20020082144 | Pfeffer | Jun 2002 | A1 |
| 20020087264 | Hills et al. | Jul 2002 | A1 |
| 20020109600 | Mault et al. | Aug 2002 | A1 |
| 20020178060 | Sheehan | Nov 2002 | A1 |
| 20020191797 | Perlman | Dec 2002 | A1 |
| 20020198776 | Nara et al. | Dec 2002 | A1 |
| 20030018523 | Rappaport et al. | Jan 2003 | A1 |
| 20030050537 | Wessel | Mar 2003 | A1 |
| 20030065561 | Brown et al. | Apr 2003 | A1 |
| 20030131059 | Brown et al. | Jul 2003 | A1 |
| 20030171189 | Kaufman | Sep 2003 | A1 |
| 20030176815 | Baba | Sep 2003 | A1 |
| 20030208335 | Unuma et al. | Nov 2003 | A1 |
| 20030226695 | Mault | Dec 2003 | A1 |
| 20040054497 | Kurtz | Mar 2004 | A1 |
| 20040061324 | Howard | Apr 2004 | A1 |
| 20040116837 | Yamaguchi | Jun 2004 | A1 |
| 20040117963 | Schneider | Jun 2004 | A1 |
| 20040122488 | Mazar et al. | Jun 2004 | A1 |
| 20040152957 | Stivoric et al. | Aug 2004 | A1 |
| 20040239497 | Schwartzman et al. | Dec 2004 | A1 |
| 20040249299 | Cobb | Dec 2004 | A1 |
| 20040257557 | Block | Dec 2004 | A1 |
| 20050037844 | Shum et al. | Feb 2005 | A1 |
| 20050038679 | Short | Feb 2005 | A1 |
| 20050054938 | Wehman et al. | Mar 2005 | A1 |
| 20050102172 | Sirmans, Jr. | May 2005 | A1 |
| 20050107723 | Wehman et al. | May 2005 | A1 |
| 20050163056 | Ranta-Aho et al. | Jul 2005 | A1 |
| 20050171410 | Hjelt et al. | Aug 2005 | A1 |
| 20050186965 | Pagonis et al. | Aug 2005 | A1 |
| 20050187481 | Hatib | Aug 2005 | A1 |
| 20050195830 | Chitrapu et al. | Sep 2005 | A1 |
| 20050216724 | Isozaki et al. | Sep 2005 | A1 |
| 20050228244 | Banet | Oct 2005 | A1 |
| 20050228692 | Hodgdon | Oct 2005 | A1 |
| 20050234742 | Hodgdon | Oct 2005 | A1 |
| 20050248718 | Howell et al. | Nov 2005 | A1 |
| 20050272564 | Pyles et al. | Dec 2005 | A1 |
| 20060004265 | Pulkkinen et al. | Jan 2006 | A1 |
| 20060020174 | Matsumura | Jan 2006 | A1 |
| 20060020177 | Seo et al. | Jan 2006 | A1 |
| 20060025282 | Redmann | Feb 2006 | A1 |
| 20060039348 | Racz et al. | Feb 2006 | A1 |
| 20060047208 | Yoon | Mar 2006 | A1 |
| 20060047447 | Brady et al. | Mar 2006 | A1 |
| 20060064276 | Ren | Mar 2006 | A1 |
| 20060069619 | Walker et al. | Mar 2006 | A1 |
| 20060089542 | Sands | Apr 2006 | A1 |
| 20060111944 | Sirmans, Jr. | May 2006 | A1 |
| 20060129436 | Short | Jun 2006 | A1 |
| 20060143645 | Vock et al. | Jun 2006 | A1 |
| 20060166718 | Seshadri et al. | Jul 2006 | A1 |
| 20060217231 | Parks et al. | Sep 2006 | A1 |
| 20060241521 | Cohen | Oct 2006 | A1 |
| 20060247952 | Muraca | Nov 2006 | A1 |
| 20060277474 | Robarts et al. | Dec 2006 | A1 |
| 20060282021 | DeVaul et al. | Dec 2006 | A1 |
| 20060287883 | Turgiss et al. | Dec 2006 | A1 |
| 20060288117 | Raveendran et al. | Dec 2006 | A1 |
| 20070011028 | Sweeney | Jan 2007 | A1 |
| 20070049384 | King et al. | Mar 2007 | A1 |
| 20070050715 | Behar | Mar 2007 | A1 |
| 20070051369 | Choi et al. | Mar 2007 | A1 |
| 20070061593 | Celikkan et al. | Mar 2007 | A1 |
| 20070071643 | Hall et al. | Mar 2007 | A1 |
| 20070072156 | Kaufman et al. | Mar 2007 | A1 |
| 20070083095 | Rippo et al. | Apr 2007 | A1 |
| 20070083602 | Heggenhougen et al. | Apr 2007 | A1 |
| 20070123391 | Shin et al. | May 2007 | A1 |
| 20070135264 | Rosenberg | Jun 2007 | A1 |
| 20070136093 | Rankin et al. | Jun 2007 | A1 |
| 20070146116 | Kimbrell | Jun 2007 | A1 |
| 20070155277 | Amitai et al. | Jul 2007 | A1 |
| 20070159926 | Prstojevich et al. | Jul 2007 | A1 |
| 20070179356 | Wessel | Aug 2007 | A1 |
| 20070194066 | Ishihara et al. | Aug 2007 | A1 |
| 20070197920 | Adams | Aug 2007 | A1 |
| 20070208544 | Kulach et al. | Sep 2007 | A1 |
| 20070276271 | Chan | Nov 2007 | A1 |
| 20070288265 | Quinian et al. | Dec 2007 | A1 |
| 20080001735 | Tran | Jan 2008 | A1 |
| 20080014947 | Carnall | Jan 2008 | A1 |
| 20080022089 | Leedom | Jan 2008 | A1 |
| 20080032864 | Hakki | Feb 2008 | A1 |
| 20080044014 | Corndorf | Feb 2008 | A1 |
| 20080054072 | Katragadda et al. | Mar 2008 | A1 |
| 20080059113 | Tsubata | Mar 2008 | A1 |
| 20080084823 | Akasaka et al. | Apr 2008 | A1 |
| 20080093838 | Tropper et al. | Apr 2008 | A1 |
| 20080097550 | Dicks et al. | Apr 2008 | A1 |
| 20080114829 | Button et al. | May 2008 | A1 |
| 20080125288 | Case | May 2008 | A1 |
| 20080129457 | Ritter et al. | Jun 2008 | A1 |
| 20080134102 | Movold et al. | Jun 2008 | A1 |
| 20080140163 | Keacher et al. | Jun 2008 | A1 |
| 20080140338 | No et al. | Jun 2008 | A1 |
| 20080146892 | LeBoeuf et al. | Jun 2008 | A1 |
| 20080155077 | James | Jun 2008 | A1 |
| 20080172204 | Nagashima | Jul 2008 | A1 |
| 20080176655 | James et al. | Jul 2008 | A1 |
| 20080243432 | Kato | Oct 2008 | A1 |
| 20080275309 | Stivoric et al. | Nov 2008 | A1 |
| 20080287751 | Stivoric et al. | Nov 2008 | A1 |
| 20090018797 | Kasama | Jan 2009 | A1 |
| 20090043531 | Kahn et al. | Feb 2009 | A1 |
| 20090047645 | Dibenedetto et al. | Feb 2009 | A1 |
| 20090048044 | Oleson et al. | Feb 2009 | A1 |
| 20090054737 | Magar et al. | Feb 2009 | A1 |
| 20090054751 | Babashan et al. | Feb 2009 | A1 |
| 20090058635 | LaLonde et al. | Mar 2009 | A1 |
| 20090063193 | Barton et al. | Mar 2009 | A1 |
| 20090063293 | Mirrashidi et al. | Mar 2009 | A1 |
| 20090093341 | James et al. | Apr 2009 | A1 |
| 20090098821 | Shinya | Apr 2009 | A1 |
| 20090144456 | Gelf et al. | Jun 2009 | A1 |
| 20090144639 | Nims et al. | Jun 2009 | A1 |
| 20090150178 | Sutton et al. | Jun 2009 | A1 |
| 20090156172 | Chan | Jun 2009 | A1 |
| 20090171788 | Tropper et al. | Jul 2009 | A1 |
| 20090195350 | Tsern et al. | Aug 2009 | A1 |
| 20090262088 | Moll-Carrillo et al. | Oct 2009 | A1 |
| 20090264713 | Van Loenen et al. | Oct 2009 | A1 |
| 20090271147 | Sugai | Oct 2009 | A1 |
| 20090287921 | Zhu et al. | Nov 2009 | A1 |
| 20090299691 | Shimaoka | Dec 2009 | A1 |
| 20090307517 | Fehr et al. | Dec 2009 | A1 |
| 20090309742 | Alexander et al. | Dec 2009 | A1 |
| 20100023348 | Hardee et al. | Jan 2010 | A1 |
| 20100056208 | Ashida | Mar 2010 | A1 |
| 20100058064 | Kirovski et al. | Mar 2010 | A1 |
| 20100059561 | Ellis et al. | Mar 2010 | A1 |
| 20100069203 | Kawaguchi et al. | Mar 2010 | A1 |
| 20100079291 | Kroll et al. | Apr 2010 | A1 |
| 20100125729 | Baentsch et al. | May 2010 | A1 |
| 20100130873 | Yuen et al. | May 2010 | A1 |
| 20100158494 | King | Jun 2010 | A1 |
| 20100159709 | Kotani et al. | Jun 2010 | A1 |
| 20100167783 | Alameh et al. | Jul 2010 | A1 |
| 20100179411 | Holmström et al. | Jul 2010 | A1 |
| 20100185064 | Bandic et al. | Jul 2010 | A1 |
| 20100205541 | Rapaport et al. | Aug 2010 | A1 |
| 20100217099 | LeBoeuf et al. | Aug 2010 | A1 |
| 20100222179 | Temple et al. | Sep 2010 | A1 |
| 20100261987 | Kamath et al. | Oct 2010 | A1 |
| 20100262045 | Heaton | Oct 2010 | A1 |
| 20100292050 | DiBenedetto | Nov 2010 | A1 |
| 20100292600 | DiBenedetto et al. | Nov 2010 | A1 |
| 20100295684 | Hsieh et al. | Nov 2010 | A1 |
| 20100298656 | McCombie et al. | Nov 2010 | A1 |
| 20100298661 | McCombie et al. | Nov 2010 | A1 |
| 20100304674 | Kim et al. | Dec 2010 | A1 |
| 20100311544 | Robinette et al. | Dec 2010 | A1 |
| 20100331145 | Lakovic et al. | Dec 2010 | A1 |
| 20110003665 | Burton et al. | Jan 2011 | A1 |
| 20110009051 | Khedouri et al. | Jan 2011 | A1 |
| 20110021143 | Kapur et al. | Jan 2011 | A1 |
| 20110022349 | Stirling et al. | Jan 2011 | A1 |
| 20110080349 | Holbein et al. | Apr 2011 | A1 |
| 20110087076 | Brynelsen et al. | Apr 2011 | A1 |
| 20110087137 | Hanoun | Apr 2011 | A1 |
| 20110106449 | Chowdhary et al. | May 2011 | A1 |
| 20110131005 | Ueshima | Jun 2011 | A1 |
| 20110145894 | Garcia Morchon et al. | Jun 2011 | A1 |
| 20110153773 | Vandwalle | Jun 2011 | A1 |
| 20110167262 | Ross et al. | Jul 2011 | A1 |
| 20110193704 | Harper et al. | Aug 2011 | A1 |
| 20110197157 | Hoffman et al. | Aug 2011 | A1 |
| 20110214030 | Greenberg et al. | Sep 2011 | A1 |
| 20110221590 | Baker et al. | Sep 2011 | A1 |
| 20110224508 | Moon | Sep 2011 | A1 |
| 20110230729 | Shirasaki et al. | Sep 2011 | A1 |
| 20110258689 | Cohen et al. | Oct 2011 | A1 |
| 20120015778 | Lee et al. | Jan 2012 | A1 |
| 20120035487 | Werner et al. | Feb 2012 | A1 |
| 20120046113 | Ballas | Feb 2012 | A1 |
| 20120072165 | Jallon | Mar 2012 | A1 |
| 20120083705 | Yuen et al. | Apr 2012 | A1 |
| 20120083714 | Yuen | Apr 2012 | A1 |
| 20120083715 | Yuen et al. | Apr 2012 | A1 |
| 20120083716 | Yuen et al. | Apr 2012 | A1 |
| 20120084053 | Yuen | Apr 2012 | A1 |
| 20120084054 | Yuen et al. | Apr 2012 | A1 |
| 20120092157 | Tran | Apr 2012 | A1 |
| 20120094649 | Porrati et al. | Apr 2012 | A1 |
| 20120102008 | Käärïäinen et al. | Apr 2012 | A1 |
| 20120116684 | Ingrassia, Jr. et al. | May 2012 | A1 |
| 20120119911 | Jeon et al. | May 2012 | A1 |
| 20120165684 | Sholder | Jun 2012 | A1 |
| 20120166257 | Shiragami et al. | Jun 2012 | A1 |
| 20120179278 | Riley et al. | Jul 2012 | A1 |
| 20120183939 | Aragones et al. | Jul 2012 | A1 |
| 20120203503 | Nakamura | Aug 2012 | A1 |
| 20120215328 | Schmelzer | Aug 2012 | A1 |
| 20120226471 | Yuen et al. | Sep 2012 | A1 |
| 20120226472 | Yuen et al. | Sep 2012 | A1 |
| 20120227737 | Mastrototaro et al. | Sep 2012 | A1 |
| 20120265480 | Oshima | Oct 2012 | A1 |
| 20120274508 | Brown et al. | Nov 2012 | A1 |
| 20120283855 | Hoffman et al. | Nov 2012 | A1 |
| 20120290109 | Engelberg et al. | Nov 2012 | A1 |
| 20120296400 | Bierman et al. | Nov 2012 | A1 |
| 20120297229 | Desai et al. | Nov 2012 | A1 |
| 20120316456 | Rahman et al. | Dec 2012 | A1 |
| 20120324226 | Bichsel et al. | Dec 2012 | A1 |
| 20120330109 | Tran | Dec 2012 | A1 |
| 20130006718 | Nielsen et al. | Jan 2013 | A1 |
| 20130041590 | Burich et al. | Feb 2013 | A1 |
| 20130072169 | Ross et al. | Mar 2013 | A1 |
| 20130073254 | Yuen et al. | Mar 2013 | A1 |
| 20130073255 | Yuen et al. | Mar 2013 | A1 |
| 20130080113 | Yuen et al. | Mar 2013 | A1 |
| 20130094600 | Beziat et al. | Apr 2013 | A1 |
| 20130095459 | Tran | Apr 2013 | A1 |
| 20130096843 | Yuen et al. | Apr 2013 | A1 |
| 20130102251 | Linde et al. | Apr 2013 | A1 |
| 20130103847 | Brown et al. | Apr 2013 | A1 |
| 20130106684 | Weast et al. | May 2013 | A1 |
| 20130132501 | Vandwalle et al. | May 2013 | A1 |
| 20130151196 | Yuen et al. | Jun 2013 | A1 |
| 20130158369 | Yuen et al. | Jun 2013 | A1 |
| 20130166048 | Werner et al. | Jun 2013 | A1 |
| 20130187789 | Lowe | Jul 2013 | A1 |
| 20130190008 | Vathsangam et al. | Jul 2013 | A1 |
| 20130190903 | Balakrishnan et al. | Jul 2013 | A1 |
| 20130191034 | Weast et al. | Jul 2013 | A1 |
| 20130203475 | Kil et al. | Aug 2013 | A1 |
| 20130209972 | Carter et al. | Aug 2013 | A1 |
| 20130225117 | Giacoletto et al. | Aug 2013 | A1 |
| 20130228063 | Turner | Sep 2013 | A1 |
| 20130231574 | Tran | Sep 2013 | A1 |
| 20130238287 | Hoffman et al. | Sep 2013 | A1 |
| 20130261475 | Mochizuki | Oct 2013 | A1 |
| 20130267249 | Rosenberg | Oct 2013 | A1 |
| 20130268199 | Nielsen et al. | Oct 2013 | A1 |
| 20130268236 | Yuen et al. | Oct 2013 | A1 |
| 20130268687 | Schrecker | Oct 2013 | A1 |
| 20130268767 | Schrecker | Oct 2013 | A1 |
| 20130274904 | Coza et al. | Oct 2013 | A1 |
| 20130281110 | Zelinka | Oct 2013 | A1 |
| 20130289366 | Chua et al. | Oct 2013 | A1 |
| 20130296666 | Kumar et al. | Nov 2013 | A1 |
| 20130296672 | O'Neil et al. | Nov 2013 | A1 |
| 20130296673 | Thaveeprungsriporn et al. | Nov 2013 | A1 |
| 20130310896 | Mass | Nov 2013 | A1 |
| 20130325396 | Yuen et al. | Dec 2013 | A1 |
| 20130331058 | Harvey | Dec 2013 | A1 |
| 20130337974 | Yanev et al. | Dec 2013 | A1 |
| 20130345978 | Lush et al. | Dec 2013 | A1 |
| 20140035761 | Burton et al. | Feb 2014 | A1 |
| 20140039804 | Park et al. | Feb 2014 | A1 |
| 20140039840 | Yuen et al. | Feb 2014 | A1 |
| 20140039841 | Yuen et al. | Feb 2014 | A1 |
| 20140052280 | Yuen et al. | Feb 2014 | A1 |
| 20140067278 | Yuen et al. | Mar 2014 | A1 |
| 20140077673 | Garg et al. | Mar 2014 | A1 |
| 20140085077 | Luna et al. | Mar 2014 | A1 |
| 20140094941 | Ellis et al. | Apr 2014 | A1 |
| 20140121471 | Walker | May 2014 | A1 |
| 20140125618 | Panther et al. | May 2014 | A1 |
| 20140142466 | Kawabe | May 2014 | A1 |
| 20140164611 | Molettiere et al. | Jun 2014 | A1 |
| 20140176475 | Myers | Jun 2014 | A1 |
| 20140180022 | Stivoric et al. | Jun 2014 | A1 |
| 20140188431 | Barfield | Jul 2014 | A1 |
| 20140200691 | Lee | Jul 2014 | A1 |
| 20140207264 | Quy | Jul 2014 | A1 |
| 20140213858 | Presura et al. | Jul 2014 | A1 |
| 20140275885 | Isaacson et al. | Sep 2014 | A1 |
| 20140278229 | Hong et al. | Sep 2014 | A1 |
| 20140288435 | Richards | Sep 2014 | A1 |
| 20140316305 | Venkatraman | Oct 2014 | A1 |
| 20140337451 | Choudhary et al. | Nov 2014 | A1 |
| 20140337621 | Nakhimov | Nov 2014 | A1 |
| 20150026647 | Park et al. | Jan 2015 | A1 |
| 20150057967 | Albinali | Feb 2015 | A1 |
| 20150137994 | Rahman et al. | May 2015 | A1 |
| 20150141873 | Fei | May 2015 | A1 |
| 20150198460 | Yamato | Jul 2015 | A1 |
| 20150220883 | B'far et al. | Aug 2015 | A1 |
| 20150289802 | Thomas et al. | Oct 2015 | A1 |
| 20150324541 | Cheung et al. | Nov 2015 | A1 |
| 20150374267 | Laughlin | Dec 2015 | A1 |
| 20160058331 | Keen | Mar 2016 | A1 |
| 20160058372 | Raghuram et al. | Mar 2016 | A1 |
| 20160063888 | McCallum et al. | Mar 2016 | A1 |
| 20160089572 | Liu | Mar 2016 | A1 |
| 20160107646 | Kolisetty et al. | Apr 2016 | A1 |
| 20170239523 | Cheng et al. | Aug 2017 | A1 |
| 20170243056 | Cheng et al. | Aug 2017 | A1 |
| 20170243508 | Cheng et al. | Aug 2017 | A1 |
| Number | Date | Country |
|---|---|---|
| 102111434 | Jun 2011 | CN |
| 102377815 | Mar 2012 | CN |
| 103226647 | Jul 2013 | CN |
| 11347021 | Dec 1999 | JP |
| 2178588 | Jan 2002 | RU |
| WO 2002011019 | Feb 2002 | WO |
| WO 2006055125 | May 2006 | WO |
| WO 2006090197 | Aug 2006 | WO |
| WO 2008038141 | Apr 2008 | WO |
| WO 2009042965 | Apr 2009 | WO |
| WO 2012061438 | May 2012 | WO |
| WO 12170586 | Dec 2012 | WO |
| WO 12170924 | Dec 2012 | WO |
| WO 12171032 | Dec 2012 | WO |
| WO 15127067 | Aug 2015 | WO |
| WO 16003269 | Jan 2016 | WO |
| Entry |
|---|
| Chandrasekar et al., “Plug-and-Play, Single-Chip Photoplethysmography”, 34th Annual International Conference of the IEEE EMBS, San Diego, California USA, Aug. 28-Sep. 1, 2012, 4 pages. |
| Clifford et al., “Altimeter and Barometer System”, Freescale Semiconductor Application Note AN1979, Rev. 3, Nov. 2006, 10 pages. |
| Fang et al, “Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience”, IEEE Transactions on Instrumentation and Measurement, vol. 54, No. 6, Dec. 2005, pp. 2342-2358. |
| Fitbit Inc., “Fitbit Automatically Tracks Your Fitness and Sleep” published online at web.archive.org/web/20080910224820/http://www.fitbit.com, downloaded Sep. 10, 2008, 1 page. |
| Godfrey et al., “Direct Measurement of Human Movement by Accelerometry”, Medical Engineering & Physics, vol. 30, 2008, pp. 1364-1386 (22 pages). |
| Godha et al., “Foot Mounted Inertia System for Pedestrian Naviation”, Measurement Science and Technology, vol. 19, No. 7, May 2008, pp. 1-9 (10 pages). |
| Intersema, “Using MS5534 for altimeters and barometers”, Application Note AN501, Jan. 2006, 12pages. |
| Ladetto et al, “On Foot Navigation: When GPS alone is not Enough”, Journal of Navigation, vol. 53, No. 2, Sep. 2000, pp. 279-285 (6 pages). |
| Lammel et al., “Indoor Navigation with MEMS Sensors”, Proceedings of the Eurosensors XIII conference, vol. 1, No. 1, Sep. 2009, pp. 532-535 (4 pages). |
| Lester et al, “Validated caloric expenditure estimation using a single body-worn sensor”, Proc. of the Int'l Conf. on Ubiquitous Computing, 2009, pp. 225-234 (10 pages). |
| Lester et al., “A Hybrid Discriminative/Generative Approach for Modeling Human Activities”, Proc. of the Int'l Joint Conf. Artificial Intelligence, 2005, pp. 766-772 (7 pages). |
| Ohtaki et al, “Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and barometer”, Microsystem Technologies, vol. 11, No. 8-10, Aug. 2005, pp. 1034-1040 (7 pages). |
| Parkka, et al, Activity Classification Using Realistic Data From Wearable Sensors, IEEE Transactions on Information Technology in Biomedicine, vol. 10, No. 1, Jan. 2006, pp. 119-128 (10pages). |
| PCT/IB07/03617 International Search Report dated Aug. 15, 2008, in related application, 3 pages. |
| Perrin et al, “Improvement of Walking Speed Prediction by Accelerometry and Altimetry, Validated by Satellite Positioning”, Medical & Biological Engineering & Computing, vol. 38, 2000, pp. 164-168 (5 pages). |
| Retscher, “An Intelligent Multi-Sensor system for Pedestrian Navigation”, Journal of Global Positioning Systems, vol. 5, No. 1, 2006, pp. 110-118 (9 pages). |
| Sagawa et al, “Classification of Human Moving Patterns Using Air Pressure and Acceleration”, Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, vol. 2, Aug.-Sep. 1998, pp. 1214-1219 (6 pages). |
| Sagawa et al, “Non-restricted measurement of walking distance”, IEEE Int'l Conf. on Systems, Man, and Cybernetics, vol. 3, Oct. 2000, pp. 1847-1852 (6 pages). |
| Specification of the Bluetooth® System, Core Package, version 4.1, Dec. 2013, vol. 0 & 1, 282 pages. |
| Stirling et al., “Evaluation of a New Method of Heading Estimation of Pedestrian Dead Reckoning Using Shoe Mounted Sensors”, Journal of Navigation, vol. 58, 2005, pp. 31-45 (15 pages). |
| Suunto Lumi, “User Guide”, Copyright Jun. and Sep. 2007, 49 pages. |
| Tanigawa et al, “Drift-Free Dynamic Height Sensor Using MEMS IMU Aided by MEMS Pressure Sensor”, Workshop on Positioning, Navigation and Communication, Mar. 2008, pp. 191-196 (6 pages). |
| VTI Technologies, “SCP 1000-D01/D11 Pressure Sensor as Barometer and Altimeter”, Application Note 33, Jun. 2006, 3 pages. |
| Lee, Mar. 3, 2017, Jawbone gets 2 patents nixed in Fitbit infringement suit, Law360, retrieved from https:///ww.law360.com/articles/898111,jawbone-gets-2-patents-nixed-in-fitbit-infringement-suit, 9 pp. |
| Number | Date | Country | |
|---|---|---|---|
| 20170238881 A1 | Aug 2017 | US |
