Patent Application
20200383636
This application claims benefit and priority to European Application No. 19178174.9, filed Jun. 4, 2019, which is incorporated by reference herein in its entirety.
The invention relates to measurement devices and algorithms and, in particular, to a system for measuring a stress level of a human.
An autonomic nervous system (ANS) of a human controls involuntary functions of a body, including the function of a cardiovascular system. The ANS consists of two branches, a sympathetic nervous system (SNS) and a parasympathetic nervous system (PNS). These two branches generally exert opposing effects on a target organ. For example, SNS activity shortens inter beat intervals (IBI) of the heart whereas PNS activity extends the IBIs. Autonomic control is typically reciprocal but co-activation of SNS and PNS or independent activation of one branch can also happen.
The status of the ANS reflects how stressed/recovered a person is. The status of the ANS is a crucial parameter when determining if the person has had significant amount of mental or physical load and when determining how well the person is able to recovery from the load. In a daily life, the person is exposed to many stressors that may include, for example a self-inflected stressor such as a physical workout or an unpredictable mental stressor such as traffic jam. Stress response is a non-specific arousal, both physical and mental, to situations or events (i.e. stressors) that the person perceives as threatening or challenging. The stress response is characterized by a decrease in PNS activity and an increase in SNS activity as well as a release of stress hormones including adrenaline, noradrenaline and cortisol. If the person encounters frequent stressors, even minor ones, the load accumulates and the stress response (i.e. physical and mental arousal) is sustained even though the stressor itself has passed. A balance between SNS and PNS activity is shifted towards SNS dominance even at night when the person sleeps when no special demands are made on the person. This sustained arousal reflects incomplete recovery over the course of the day. Moreover, if the status of the ANS remains disturbed night after night, it reflects accumulated load and insufficient recovery at day and night time. In this situation, the person has to mobilize more effort to cope with challenges he face, for example in sports or at work. This may cause performance/health impairment in the long run. In general, measurement of the ANS function does not only indicate stress/recovery but also provide information of cardiovascular health that may be risked due to a prolonged stress.
The invention is defined by the independent claims. Embodiments are defined in the dependent claims.
According to an aspect, there is provided a method for estimating a stress level of a human by an apparatus, comprising: executing, by the apparatus, a breathing exercise application by the apparatus, starting a breathing exercise of the breathing exercise application and outputting, during the breathing exercise, breathing instructions to a user of the apparatus; acquiring, by the apparatus, a set of heart activity measurement data samples measured by a heart activity sensor from the user during the breathing exercise; computing, by the apparatus, a set of inter-heartbeat interval samples of the set of heart activity measurement data samples; computing, by the apparatus, a cardiac coherence of the user during the exercise from the set of inter-heartbeat interval samples; measuring, by the apparatus, a respiratory rate of the user during the breathing exercise; computing, by the apparatus, a score of the breathing exercise on the basis of the respiratory rate and the cardiac coherence, the score indicating a stress level of the user; and outputting, by the apparatus, the score through an interface of the apparatus.
In an embodiment, an inter-heartbeat interval sample represents a time interval between two consecutive heartbeats detected from the heart activity measurement data samples.
In an embodiment, said measurement of the heart activity measurement data samples and said output of the breathing instructions are performed simultaneously by the apparatus upon starting the breathing exercise, and wherein said computing the score of the breathing exercise is performed within the breathing exercise application at least after ending the breathing exercise.
In an embodiment, said computing the score comprising: comparing the measured respiratory rate with the breathing instructions; and providing the score in proportion to how synchronized the measured respiratory rate is with a breathing rate instructed by the breathing instructions.
In an embodiment, the cardiac coherence represents how harmonic a signal represented by the set of inter-heartbeat interval samples is, higher harmonicity indicating higher cardiac coherence.
In an embodiment, the method further comprises computing a momentary score during the exercise on the basis of the respiratory rate and the cardiac coherence, and outputting the momentary score through the interface during the exercise.
In an embodiment, the method further comprises defining a plurality of zones for the score and outputting, through the interface during the breathing exercise, an indicator indicating a zone where the momentary score currently resides as a feedback to the user.
In an embodiment, the respiratory rate is computed from the set of inter-heartbeat interval samples.
In an embodiment, said computing the score comprises mapping a higher correlation between the measured respiratory rate and the breathing instructions and a higher cardiac coherence to a score value indicating a lower stress level.
In an embodiment, the method further comprises: estimating the stress level by using a second method where a metric associated with blood pulse wave velocity measured from the user is mapped to the stress level; and incorporating the metric into the score.
In an embodiment, the method further comprises: detecting at least one stressor on the basis of measurements conducted on the user; and executing the breathing exercise as a result of said detecting.
According to an aspect, there is provided an apparatus comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to perform at least the following: executing a breathing exercise application, starting a breathing exercise of the breathing exercise application and outputting, during the breathing exercise, breathing instructions to a user of the apparatus; acquiring a set of heart activity measurement data samples measured by a heart activity sensor from the user during the breathing exercise; computing a set of inter-heartbeat interval samples of the set of heart activity measurement data samples; computing a cardiac coherence of the user during the exercise from the set of inter-heartbeat interval samples; acquiring a respiratory rate of the user measured during the breathing exercise; computing a score of the breathing exercise on the basis of the respiratory rate and the cardiac coherence, the score indicating a stress level of the user; and outputting the score through an interface of the apparatus.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to perform said measurement of the heart activity measurement data samples and said output of the breathing instructions simultaneously upon starting the breathing exercise, and to perform said computing the score of the breathing exercise within the breathing exercise application at least after ending the breathing exercise.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to compute the score by at least: comparing the measured respiratory rate with the breathing instructions; and providing the score in proportion to how synchronized the measured respiratory rate is with a breathing rate instructed by the breathing instructions.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to compute a momentary score during the exercise on the basis of the respiratory rate and the cardiac coherence, and to output the momentary score through the interface during the exercise.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to define a plurality of zones for the score and to output, through the interface during the breathing exercise, an indicator indicating a zone where the momentary score currently resides as a feedback to the user.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to compute the respiratory rate from the set of inter-heartbeat interval samples.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to compute the score by mapping a higher correlation between the measured respiratory rate and the breathing instructions and a higher cardiac coherence to a score value indicating a lower stress level.
In an embodiment, the at least one memory and computer program code are configured, with the at least one processor, to cause the apparatus to estimate the stress level by using a second method where a metric associated with blood pulse wave velocity measured from the user is mapped to the stress level, and to incorporate the metric into the score.
According to an aspect, there is provided a computer program product embodied on a non-transitory distribution medium readable by a computer and comprising a computer program code which, when read and executed by the computer of an apparatus, causes the computer to execute a computer process comprising: executing a breathing exercise application, starting a breathing exercise of the breathing exercise application and outputting, during the breathing exercise, breathing instructions to a user of the apparatus; acquiring a set of heart activity measurement data samples measured by a heart activity sensor from the user during the breathing exercise; computing a set of inter-heartbeat interval samples of the set of heart activity measurement data samples; computing a cardiac coherence of the user during the exercise from the set of inter-heartbeat interval samples; acquiring a respiratory rate of the user measured during the breathing exercise; computing a score of the breathing exercise on the basis of the respiratory rate and the cardiac coherence, the score indicating a stress level of the user; and outputting the score through an interface of the apparatus.
In the following the invention will be described in greater detail by means of preferred embodiments with reference to the accompanying drawings, in which
The following embodiments are exemplifying. Although the specification may refer to “an”, “one”, or “some” embodiment(s) in several locations of the text, this does not necessarily mean that each reference is made to the same embodiment(s), or that a particular feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments.
At least one sensor device 12, 14 may be configured to measure a photoplethysmogram (PPG) optically. PPG represents a volumetric measurement of an organ. A PPG sensor 12, 14 may comprise a light source such as a light emitting diode (LED) configured to illuminate a skin of the user 20 and, further, comprise a light-sensitive sensor such as a photodiode configured to measure changes in light reflected from the illuminated skin. With each cardiac cycle, the heart pumps blood to peripheral arteries. Even though this blood wave pulse is damped by the artery system as it propagates, it is enough to distend arteries and arterioles in the subcutaneous tissue. If the light source and the light-sensitive sensor are place appropriately against the skin, the blood wave pulse can be detected as a change in the reflecting light measured by using the light-sensitive sensor. Each cardiac cycle appears as a peak in a measurement signal acquired through the light-sensitive sensor. The blood pulse wave may be modulated by multiple other physiological systems and, therefore, the PPG may also be used to monitor breathing, hypovolemia, and other physiological conditions. The PPG may be measured at various locations of the human body, e.g. from a wrist (sensor 12), head, ear canal or ear leaf (sensor 14).
At least one sensor device 16 may be configured to measure a ballistocardiogram (BCG). The BCG is a measure of ballistic forces generated during the heartbeat. Ballistocardiogram characterizes motion of the human body resulting from the ejection of blood into the great vessels during each heartbeat. The BCG shows on a frequency range between 1 and 20 Hertz (Hz), and is caused by the mechanical movement of the heart. As the ECG and the PPG, the BCG can be recorded by using a non-invasive sensor 16 from the surface of the body. One The BCG sensor 16 may be a ballistocardiographic scale configured to measure a recoil of the human body standing on the scale. The recoil is caused by the heartbeat and can be measured from the user standing on the BCG scale, e.g. by using a pressure sensor. The BCG scale may be configured to show the user's 20 heart rate as well as weight.
As described above, the status of the ANS reflects on how stressed the user 20 is, and the functions of the ANS affect the cardiovascular system as well. This means that, by using proper measurements and signal processing, a stress level of the user may be determined from cardiovascular measurement data or, in other words, heart activity measurement data.
It may be beneficial to carry out the stress level estimation in a situation where conditions for measuring the stress level are controlled.
The set of heart activity measurement data samples may be measured by using any one or more of the above-described heart activity sensors 10 to 16.
The apparatus may be any one of the above-described sensors 10 to 16, a personal electronic devices such as a portable computer, wrist-worn computer, or a mobile phone or a tablet computer, or the apparatus may be a server computer.
In an embodiment, the interface is a user interface of the apparatus, e.g. a display screen. In another embodiment, the interface is a network adapter such as a wireless or wired communication circuitry, e.g. a Bluetooth® interface complying with the Bluetooth® standard specifications.
The IBI samples characterize a time interval between consecutive heartbeats detected from the heart activity measurement data. The computation of an IBI sample may comprise detecting a determined heartbeat feature from the heart activity measurement data, e.g. a peak in ECG or PPG measurement data, and measuring a time interval between consecutively detected heartbeat features. The IBI samples also characterize heart rate variability (HRV).
In an embodiment of the process of
The distance between the dominant frequency component in the IBI sample set and the reference frequency represents the stress level. In an embodiment, the closer the dominant frequency component is to the reference frequency, the lower the stress level is. Accordingly, the more distant the dominant frequency component is from the reference frequency, the higher is the determined stress level. Block 204 may employ a mapping table or a mapping rule that maps each distance to one of a plurality of stress levels by using this principle: the higher distance between the dominant frequency component and the reference frequency is associated with a higher stress level.
The deviation of the IBI samples in the frequency domain represents the cardiac coherence of the user 20.
Referring to
In an embodiment, the cardiac coherence is defined in terms of deviation or variance of the IBI sample set. High deviation or high variance is associated with low cardiac coherence while low deviation or low variance is associated with high cardiac coherence.
The cardiac coherence may be understood to represent a degree of a harmonic component in a signal represented by the set of IBI samples, higher degree of the harmonic component indicating higher cardiac coherence. In other words, the cardiac coherence represents how sinusoidal the signal represented by the IBI sample is. The IBI samples have a correlation with the respiratory rate in the sense that the IBIs shorten when the inhales and lengthen during exhalation, thus forming a periodic characteristic. The purer the periodicity, the higher the cardiac coherence. The signal represented by the IBI samples is less correlated, i.e. more noise-like, during normal breathing and more correlated, i.e. more harmonic, during deep breathing. Therefore, a more accurate estimate of the stress level may be acquired when the user is breathing deeply. Therefore, the breathing instructions may instruct the user to breathe deeply, e.g. four to six breaths per minute.
In an embodiment, the cardiac coherence is estimated through correlation with a sinusoidal reference signal. The reference signal may be aligned with the breathing instructions such that the reference signal represents a sinusoidal signal that matches with the instructed respiratory frequency. The correlation between the reference signal and the IBI samples may then directly represent the cardiac coherence. Other embodiments for computing the cardiac coherence from the IBI samples are equally possible.
In an embodiment, the reference frequency represents a respiratory frequency or a breathing frequency of the user at the time of measuring the heart activity measurement samples. Referring to
In an embodiment, the reference frequency is lower than one Hertz (Hz) or even lower than 0.5 Hertz.
In an embodiment of
In an embodiment, the user is instructed to breathe with a determined respiratory frequency forming the reference frequency (block 404). Block 404 may be executed in the process of
In an embodiment, the respiratory frequency is input by the user.
In an embodiment, the respiratory frequency is measured (block 402) while performing the heart activity measurements. The respiratory frequency may be measured by using an accelerometer or a strain gauge attached to the user. There also exist solutions for estimating the respiratory frequency or respiratory rate from the ECG or PPG measurement data, for example. The reference frequency may then be computed from the measured respiratory frequency, e.g. by using an average function. In an embodiment, block 402 is employed when the user 20 is sleeping. Modern wearable activity monitoring devices and training computer are capable of detecting determining when the user is sleeping. Such a detection may trigger an apparatus to execute the process of
The respiratory rate may be understood as equal to the respiratory frequency.
Performing the stress level estimation when the user is asleep provides for a long-term estimation of the stress level and comparable results that span over several days or weeks. Let us for the simplicity sake that the user sleeps at nights. The status of ANS and the stress level may be based on comparing the measurement of each night to a baseline value of the user. The baseline value may be an average value of the stress level from a period of days when the user's status is considered to be normal or stress level low. Measured motion data and an activity monitoring algorithm may be employed to ignore days with exceptional physical activity. Similarly, the measured motion data and a sleep quality estimation algorithm may be employed to exclude nights with disturbed sleep or an exceptional sleep-wake rhythm. If the user provides other information, such as days when the user is ill, such information may be used to ignore days when the ANS status is not reflecting the normal baseline of the user.
When the individual stress level measurement is compared with the baseline, a limit may be designed to define whether or not the difference between the current measurement and the baseline is significant.
In an embodiment, a stress level estimation application is stored as a computer program product in the apparatus. The stress level estimation application may be considered as a measurement function in a fitness application of the apparatus. The breathing exercise application executed in the embodiment of
In an embodiment, the animation of the object 510 is periodic and adapted to the instructed breathing frequency in the following manner. The animation may follow the same trajectory regardless of the instructed breathing frequency but the speed of the animation may be adapted to the instructed breathing frequency. Accordingly, speed of a motion vector of the object 510 during the animation may be proportional to the instructed breathing frequency: the higher the breathing frequency the higher the speed.
In another embodiment, another illustration is displayed. The illustration may be such that it enables the user to see or evaluate a remaining inhale/exhale time of a current inhale/exhale period. This helps the user to find breathing rhythm. An example of such an illustration is provided in
In an embodiment, the breathing frequency is defined in terms of an inhale time and an exhale time. The inhale time may equal to the exhale time but, in some embodiments, the inhale time may differ from the exhale time. In an embodiment, the exhale time is (always) greater than or equal to the inhale time. The inhale time and the exhale time may be preset and determined by the application but, in some embodiments, the user may change the inhale time and/or exhale time before starting the breathing exercise. The preset value may be five seconds for inhale time and five seconds for exhale time, for example. The application may prevent the user from setting an inhale time that is greater than the exhale time. The user may set the duration of the breathing exercise.
Block 502 and associated measurements may be performed for a determined duration or until a sufficient number of heart activity measurement data samples have been measured. The computation of the stress level (block 204) or the score for the breathing exercise (block 222) may then be carried out after measuring the set of heart activity measurement data samples. Regarding block 200, the heart activity measurement samples may be measured and acquired simultaneously with block 502 and the IBI samples may be computed after the set of heart activity measurement samples have been measured.
An alternative input triggering block 500 may be from an activity monitoring application of the apparatus, reporting that the user has been detected to fall asleep. In such a case, block 502 may be omitted. The apparatus may be configured to determine the input triggering block 500 and, as a result either execute block 502 or omit block 502 on the basis of the input. The user input may trigger execution of block 502 while the activity monitoring input may cause omission of block 502.
In an embodiment, upon detecting that the score or the estimated stress level exceeds a determined threshold THalarm, the apparatus may trigger output of one or more instructions instructing the user to perform a mental and/or physical exercise that reduces the stress level, or output of a proposal for performing such an exercise.
The score for the breathing exercise may be computed at least after the breathing exercise has ended. In another embodiment, the score is computed during the breathing exercise as a momentary score and output as a feedback to the user so that the user may evaluate the performance during the exercise. The score may be computed by using only a subset of the measurement data available for the computation of the cardiac coherence and the respiratory rate, e.g. a certain amount of latest measurement data measured defined by a time window. Accordingly, when the exercise starts the apparatus may first gather a sufficient amount of measurement data to compute the first momentary score and output the first momentary score. As the exercise continues, the measurement data acquired at the beginning of the exercise may be omitted from the computation of later momentary scores. When the exercise has ended, all or at least a majority of the measurement data acquired during the exercise may be used in the computation of a total score for the exercise.
A blood pulse is modulated on its way from the heart and through the human body. The modulation may be caused by various physiological conditions and functions. Therefore, characteristics of the blood pulse wave may comprise representation of such physiological conditions. One set of such characteristics may include propagation characteristics of the blood pulse wave. The propagation characteristics may be considered as time characteristics that represent a pulse transit time (PTT), for example, within a certain distance in the human arteries. Equivalent characteristics may include pulse wave velocity (PWV) which is proportional to the pulse propagation time and, therefore, can be considered to represent the time characteristics of the blood pulse wave.
The PWV is mainly a function of arterial stiffness, arterial blood pressure, the heart rate, the age, and conditions of the arteries (affected by smoking habits, arteriosclerosis, high blood pressure, etc.). Arterial stiffness is modified during mental or physical stress due to local SNS activity. For an otherwise healthy person, the PWV or its corresponding PTT can thus be a measure of stress if the measure is performed when the blood pressure is at its lowest value, e.g. when lying still or before breakfast. The PWV can be estimated by different means such as: 1) using a reference signal such as the ECG R-wave together with a distal measure of the blood pressure such as for example measured by the PPG placed on a specific body location that can sense the blood pressure wave and influences of vascular tone, e.g. on a wrist, finger, or ear; 2) from the sole features of the PPG by using two spatially separated PPG measurement points and detection of the same blood pulse wave at the two measurement points. The PWV may be measured on the basis of a time of occurrence of the detection of the blood pulse wave at each measurement point and distances from the heart to each measurement point. As an alternative to the PPG, arterial applanation tonometery (ATO) or Doppler Ultrasound flow meter may be employed.
An embodiment estimates the stress level by using the method of
In an embodiment combining embodiments of
In an embodiment, block 604 comprises: estimating an accuracy of the first stress level estimate Q1 and an accuracy of the second stress level estimate Q2 and comparing each accuracy Q1, Q2 with a determined accuracy threshold TH1, TH2; and selecting, on the basis of the threshold comparison, one of the stress level estimates or a combination of both stress level estimates as the aggregate stress level Qtot. The combination may include unequal weighting of the stress level estimates. The weights may be based on, for example, the estimated accuracy of each estimate: a higher weight may be assigned to a more accurate estimate.
A stressor is a chemical or biological agent, environmental condition, external stimulus, or an event that causes stress to the user. An event that triggers the stress response may include environmental stressors (hypo or hyper-thermic temperatures, elevated sound levels, over-illumination, overcrowding), daily stress events (e.g., traffic, lost keys, quality and quantity of physical activity), life changes (e.g., divorce, bereavement), workplace stressors (e.g., high job demand vs. low job control, repeated or sustained exertions, forceful exertions, extreme postures), chemical stressors (e.g., tobacco, alcohol, drugs), or social stressors (e.g., societal and family demands).
In an embodiment, the process of
In an embodiment, the detection of a stressor is based on measurements (block 802). For example, the daily activity of the user may be measured and, if the activity exceeds a determined activity threshold, the detection of a stressor may be triggered. On the other, if the activity deviates from a normal or average daily activity by a determined amount, the detection of the stressor may be triggered. As another example, the location of the user may be measured. If the user is distant from a home location, e.g. in a different country, the detection of the stressor may be triggered. If the user's location is associated with an abnormal event, e.g. a football stadium or a night club, the detection of the stressor may be triggered. If the user's location is at work longer than a conventional work day for extended period of time, the detection of the stressor may be triggered. As yet another example, an amount of light during the day and/or night may be measured. If a total amount of light is below a threshold or amount light during night is above a threshold, the detection of the stressor may be triggered. As yet another example, an ambient audio noise level may be measured. If the noise level is above a threshold, the detection of the stressor may be triggered. User's voice may indicate the stress level. Accordingly, a voice analysis algorithm may be employed to determine whether or not the user is under stress. If stress is detected in the voice analysis, the detection of the stressor may be triggered. As yet another example, meal times may be detected on the basis of motion data. Irregular meal times may cause detection of a stressor.
In another embodiment, the detection of a stressor is based on analysing user's digital files such as events stored in a digital calendar or posts in social media (block 804). If the analysis indicates abnormal events or posts, the detection of the stressor may be triggered. Detection may be based on a number of calendar events exceeding a threshold (too busy) or lack of calendar events. The type of calendar events may also be evaluated. For example, too heavy training schedule, no training schedule, or too many sports competitions may cause detection of a stressor.
As described in connection with blocks 802 and 804, the user's behaviour may be monitored and compared with a normal daily behaviour (block 806). When the user deviates from the normal daily behaviour, the detection of the stressor may be triggered. If the stress level estimation is carried out under an influence of one or more stressors, the presence of the stressors stored in association with the stress level estimation may help in reducing the stress.
In an embodiment, detection of one or a plurality of stressors in the process of
In an embodiment, a plurality of zones are provided for the score. Each zone may be defined by a unique range of values of the score. A higher score may indicate a better performance and a lower stress level of the user. The apparatus may output, through the interface during the breathing exercise and/or after the exercise, the score by utilizing the zones. In the embodiments where the momentary score is computed during the exercise, the apparatus may accumulate a duration the score stays within each zone during the breathing exercise. After the exercise, the apparatus may output the score in the form of the duration the score has spent in each zone, e.g. in the following form:
A, B, C, and D may have arbitrary values defining the scale for the score.
In an embodiment, the apparatus outputs, during the breathing exercise an indicator indicating a zone where the momentary score currently resides as a feedback to the user.
The processor 101 may comprise a measurement signal processing circuitry 104 configured to acquire the heart activity measurement data and to process the heart activity measurement data samples. In an embodiment, the measurement signal processing circuitry 104 is configured to compute the IBI samples and to estimate the stress level from the IBI samples, as described above. The measurement signal processing circuitry may further compute the cardiac coherence and the respiratory rate from the heart activity measurement data samples and, optionally, from other measurement data. The measurement signal processing circuitry 104 may comprise a time characteristics estimation circuitry configured to estimate the time characteristics such as the PTT or PWV from received detected measurement signals, as described above in the embodiment of
Upon successful computation of the stress level, the processor 101 may output an indication about the measured stress level to the user via the user interface 124. The indicator may be a display indicator displayed on the display unit of the user interface 124, an audio output, or a haptic output.
The apparatus may comprise a communication circuitry 102 connected to the processor 101. The communication circuitry may comprise hardware and software suitable for supporting Bluetooth® communication protocol such as Bluetooth Smart specifications. It should be appreciated that other communication protocols are equivalent solutions. The processor 101 may use the communication circuitry 102 to transmit and receive frames according to the supported wireless communication protocol. In some embodiments, the processor 101 may use the communication circuitry 102 to transmit the heart activity measurement data and/or estimated stress levels to another apparatus, e.g. to a cloud server storing the user's 20 user account.
In an embodiment, the apparatus comprises at least one heart activity sensor 120. The heart activity sensor(s) 120 may comprise one or more of the above-described sensors such as an ECG sensor 10, PPG sensor 12, 14, and the BCG sensor 16. In another embodiment, the apparatus may communicate with at least one heart activity sensor 122 through the communication circuitry 102. The at least one heart activity sensor 122 may comprise an external heart activity sensor with respect to the apparatus. The heart activity measurement data may thus be acquired from an internal or external heart activity sensor for the estimation of the stress level.
It will be obvious to a person skilled in the art that, as the technology advances, the inventive concept can be implemented in various ways. The invention and its embodiments are not limited to the examples described above but may vary within the scope of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 19178174.9 | Jun 2019 | EP | regional |
