Method and device for collecting physiological data of a wearer

Abstract

A method of tuning a wearable device that collects physiological data, a wearable device that collects physiological data, a method of controlling a wearable device that collects physiological data, a wearable device that collects physiological data of a wearer, a method, and a computer readable medium. The method of tuning a wearable device that collects physiological data of a wearer includes collecting a PPG signal using the device, determining whether the collected PPG signal is attributable to the wearer being a human or an animal, and ceasing tuning if the collected PPG signal is not attributable to the wearer being a human or an animal, where determining whether the collected PPG signal is attributable to the wearer being a human or an animal includes determining information regarding at least one cardiac cycle based on pulses in the PPG signal.

Description

[Technical field]

[0001] The present invention relates generally to methods and devices for collecting physiological data of a wearer, and in particular to methods for tuning a wearable device that collects physiological data of a wearer, methods for controlling a wearable device that collects physiological data of a wearer, and wearable devices that collect physiological data of a wearer. [Background technology]

[0002] Any mention and / or discussion of prior art throughout this specification should in no way be taken as an admission that this prior art is known in the art or forms part of the common general knowledge.

[0003] Wearable devices that monitor bio / physiological signals, such as photoplethysmography (PPG) signals, and signals indicative of movement, such as accelerometer (ACC) signals, are becoming increasingly common. Such devices can support various forms of data tracking, such as fitness and health-related analytics, including sleep monitoring.

[0004] In such devices, it may be important to detect when the device is not being worn, generally referred to herein as the "wrist-off" state as compared to the "wrist-on" state. In the event of a failure to (properly) wear the device, acquisition of physiological data becomes a challenge for subsequent calculations. Also, reliable tuning / initialization is desired in such devices.

[0005] In addition, the physiological state of the animals may provide the farmer or researcher with useful knowledge and insights about the animals, for example, their health, function, and psychological state. The wearable device may be attached to the skin of the animal, for example, on the ears, nose, neck, head, hooves, legs, top of the tail, or top of the spine. A "wrist off" state in an animal may refer to a "drop off" state of the device from the animal's skin.

[0006] SUMMARY OF THE PRESENT EMBODIMENTS Embodiments of the present invention seek to address one or more of the above problems. Summary of the Invention

[0007] According to a first aspect of the present invention, there is provided a method of tuning a wearable device for collecting physiological data of a wearer, the method comprising: acquiring a PPG signal using a device; determining whether the collected PPG signal can be attributed to the wearer being a human or an animal; Aborting tuning if the collected PPG signal cannot be attributed to the wearer being a human or an animal; Including, Determining whether the collected PPG signal can be attributed to the wearer being a human or an animal includes determining information regarding at least one cardiac cycle based on at least one pulse in the PPG signal.

[0008] According to a second aspect of the present invention, there is provided a wearable device for collecting physiological data of a wearer, the device comprising: A PPG sensor, a signal collecting unit coupled to the PPG sensor, the signal collecting unit collecting a PPG signal using the PPG sensor for tuning the device; a processor coupled to the signal collecting unit for determining whether the collected PPG signal can be attributed to the wearer being a human or an animal; Equipped with The processor is configured to discontinue tuning if the collected PPG signal is not attributable to the wearer being a human or an animal; Determining whether the collected PPG signal can be attributed to the wearer being a human or an animal includes determining information regarding at least one cardiac cycle based on at least one pulse in the PPG signal.

[0009] According to a third aspect of the present invention, there is provided a method of controlling a wearable device for collecting physiological data of a wearer, the method comprising: acquiring a PPG signal using a device; processing the PPG signal to obtain an analysis result; determining whether the collected PPG signal can be attributed to the device not being worn by the wearer; disabling the display of analysis results by the device and / or turning off a light source of the device if the collected PPG signal can be attributed to the device not being worn by the wearer; Includes.

[0010] According to a fourth aspect of the present invention, there is provided a wearable device for collecting physiological data of a wearer, the device comprising: a signal collecting unit for collecting a PPG signal; a processor coupled to the signal collecting unit for processing the PPG signal to obtain an analysis result; Equipped with The processor is further configured to determine whether the collected PPG signal can be attributed to the device not being worn by the wearer, and if the collected PPG signal can be attributed to the device not being worn by the wearer, disable display of the analysis results and / or turn off the light source of the device.

[0011] According to a fourth aspect of the present invention there is provided a computer readable medium comprising instructions which, when executed by a computing device, instruct the computing device to perform a method according to the first and / or third aspect.

[0012] Embodiments of the present invention will be better understood and readily apparent to those skilled in the art from the following description, given by way of example only, in conjunction with the drawings in which: [Brief description of the drawings]

[0013] [Figure 1] 1 is a flowchart illustrating an overall algorithm flow according to an example embodiment. [Diagram 2] 4 is a flowchart illustrating an algorithm performed during the tuning phase, according to an exemplary embodiment. [Diagram 3] FIG. 1 illustrates feature extraction for wrist-off detection in accordance with an exemplary embodiment. [Figure 4] FIG. 13 shows a comparison of DC levels measured while wearing the device at rest (left side) or during high exertion (right side) according to an exemplary embodiment. [Diagram 5] 1A illustrates an exemplary embodiment in which the measured DC level is close to zero when placed in a dark environment, (b) illustrates an exemplary embodiment in which the measured DC level is saturated (i.e., equal to the supply voltage) when placed directly facing ambient light, (c) illustrates an exemplary embodiment in which the measured DC level can be above zero and below the saturated value when placed in indirect ambient light, and (d) illustrates an exemplary embodiment in which the device is placed in a shadow due to body movement (i.e., wrist-off state) resulting in a DC level with high variance in unstable light. [Figure 6](a) shows that the AC frequency content of the PPG signal when the device is worn is typically in the range of 0.5 Hz to 8 Hz, according to an exemplary embodiment, and (b) shows that when noise is present, i.e., the output signal has other frequencies (other than 0.5 Hz to 8 Hz), the noise may be due to wrist-off conditions caused by ambient light sources such as neon lamps or display monitors, according to an exemplary embodiment. [Figure 7] FIG. 13 illustrates, in accordance with an exemplary embodiment, that a typical PPG has frequency content in the range of 0.5 Hz to 8 Hz, which range is assumed to be the PPG signal, while the remainder of the measured range is assumed to be noise, specifically 8 Hz to 50 Hz. [Figure 8] Scatter plots for 1) PPG DC level and 2) SNR (SNR is rms based) features of PPG_AC in wrist-on and wrist-off reference / training data according to an example embodiment. [Figure 9] 1 is a scatter plot (with magnified inset) of variance (PPG AC) versus mean (PPG DC) for reference / training data in accordance with an exemplary embodiment. [Figure 10] 1A is a scatter plot of variance (PPG DC) versus mean (PPG DC) according to an example embodiment; and FIG. 1B is a scatter plot of sumACSatZero versus mean (PPG DC) according to an example embodiment. [Figure 11] 10 is a flowchart illustrating wrist-off condition detection in accordance with an exemplary embodiment. [Figure 12] 1A illustrates determining a wrist-off condition based on seven consecutive time windows of PPG data, and FIG. 1B illustrates setting a counter threshold based on seven consecutive 4 second time windows of PPG data and counting wrist-off condition outputs in each of the seven windows, according to an exemplary embodiment; [Figure 13]FIG. 13 illustrates results obtained from an evaluation of 21 subject data resulting in wrist-off condition classification with greater than 90% accuracy, according to an exemplary embodiment. [Figure 14] 1A and 1B are diagrams illustrating exemplary zones of high and low motion based on different TAT and PIM thresholds, respectively, according to an exemplary embodiment; [Figure 15] 1 is a high level flow chart illustrating a tuning and roll-off detection method / algorithm in accordance with an exemplary embodiment. [Figure 16] 1 is a general schematic diagram of a device according to an exemplary embodiment; [Figure 17] 17A-17C are detailed views of certain components of the device of FIG. 16 in accordance with an exemplary embodiment. [Figure 18] 17A-17C are detailed views of certain components of the device of FIG. 16, according to another exemplary embodiment. [Figure 19] FIG. 13 illustrates that variance (PPG DC) and counting the sum of the number of saturations and the number of zeros (sumACSatZero) of a PPG AC signal are useful for wrist off / off state classification, according to an exemplary embodiment. [Figure 20] 1 is a flowchart illustrating a method of tuning a wearable device to collect physiological data of a wearer, according to an exemplary embodiment. [Figure 21] 1 is a flowchart illustrating a method for controlling a wearable device that collects physiological data of a wearer, according to an exemplary embodiment. [Figure 22] FIG. 1 illustrates determining rise and / or fall times of cardiac cycles contained in a PPG signal according to an exemplary embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Overall Algorithm Flow According to an Exemplary Embodiment 1 shows a flow chart illustrating the overall algorithm flow according to an exemplary embodiment. The algorithm receives its measurement input from a wearable device 102, generally in the form of a photoplethysmography (PPG) signal 104 (including AC and DC components) and an accelerometer (ACC) signal 106 (including x, y and z axes from respective ACC sensor components). The signal 106 may be xy, yz, xz and / or a combination of x, y and z axes.

[0015] Both the PPG signal 104 and the ACC signal 106 are inputs to the tuning stage 108 of the algorithm. In this exemplary embodiment, tuning requires passing eight conditions in the decision stage 110. Specifically, in this exemplary embodiment, failure cases 1-3 are used to determine tuning failures in the form of "wrist off", i.e., the device 102 not being worn or not properly worn, and failure cases 4-8 are used to determine the assumed "wrist on" scenario, i.e., general tuning failures in the case of a worn device 102. It should be noted that failure cases 1-3 in this exemplary embodiment are suitable for determining that the signal is not from a human / animal as a subcategory of the wrist off determination. Also, "not properly worn" here means that the user is wearing the device 102 but loosely, such as the PPG and / or ACC sensors not touching the surface of the skin or being misplaced.

[0016] Failure cases 1 to 3 in this exemplary embodiment include the following.

[0017] Failure Case 1: Heart rate (HR) is out of range (e.g., outside the range of approximately 30 bpm to 240 bpm for humans).

[0018] Failure Case 2. The PPG sensor light emitting device (LED) drive current is at a maximum value (e.g., ≥ 20mA), but no pulses bearing information about the cardiac cycle within a certain range (e.g., at least three pulses with rise times 10% ≤ RT ≤ 50%, where RT refers to the percentage of rise time in one cardiac cycle) are identified from the PPG signal.

[0019] Fail Case 3. PPG signal amplitude < e.g., 28% of allowable amplitude (the allowable amplitude may be set in the range of approximately 20mV-70mV).

[0020] If any one of the above three failure cases is detected, it is deemed to be a wrist-off status in this exemplary embodiment. In response to determining a wrist-off status, the LED sensor of the device 102 is turned off until the device 102 detects motion by checking the ACC signal 106 against a time above threshold (TAT) threshold and / or a proportional integral mode (PIM) threshold. FIG. 14(a) illustrates exemplary zones of high and low motion based on different TAT and PIM thresholds, according to an exemplary embodiment. The high motion thresholds may be 100 for TAT and 12 for PIM, and the low motion thresholds may be 5 for TAT and 12 for PIM. Upon determining high motion based on the TAT / PIM thresholds, the LEDs are turned back on for the next 5 minutes and tuning in the tuning phase 108 is resumed. FIG. 14(b) illustrates another example with zones of high and low motion based on different PIM thresholds (only), where in some exemplary embodiments, the PIM thresholds may be about 12-85.

[0021] Failure cases 4 to 8 in this exemplary embodiment include the following.

[0022] FAIL CASE 4: Average DC level > e.g. 83% of supply voltage.

[0023] Fail Case 5: The LED drive current is at maximum value, but the PPG amplitude is not acceptable based on the allowable amplitude.

[0024] Failure Case 6: LED drive current is at minimum value (e.g., threshold is 1mA) and PPG amplitude > 50% of supply voltage.

[0025] Failure Case 7: Same LED drive current, processing time >24 seconds, or more generally, a threshold of about 8 seconds to 4 minutes in an exemplary embodiment. If the drive current level is changed during the tuning process, the timer is reset to 0.

[0026] Failure Case 8: The motion detected from the ACC is high motion continuously for a certain period, for example 10 seconds. Note that any other predetermined period can be set and considered.

[0027] If any one of the above failure cases is detected, tuning will be terminated and tuning will be reactivated in the next 5 minutes in one exemplary embodiment.

[0028] If the tuning decision step 108 is successful, then a motion artifact removal (MAR) step 112 algorithm is processed. The outputs of the MAR step 112 are flag motion, filtered PPG, and motion strength (MS) values.

[0029] Also, if the tuning decision step 108 is passed, the algorithm of the wrist off step 114 is processed. An exemplary embodiment of the wrist off step 114 algorithm is described in detail below. Even if tuning is passed, the result from the wrist off (wrist detection flag_4s) is used to determine whether further processing results of further algorithms, shown as step 115 such as HR, are indicated / displayed and whether the sensor LED is turned off. In this embodiment, the output from the wrist off step 114 is used to control a switch 116 to provide the output from the MAR step 112 to the further algorithm step 115 or to provide a zero signal from a zero signal generator step 118. It should be noted that depending on the purpose of the further algorithm or the wavelength of light, the signal may not need to enter the MAR step after passing the tuning step in different exemplary embodiments.

[0030] Through the execution of the algorithm, the step counting stage 120 outputs the activity results and the measured TAT / PIM per minute. Briefly, the step counting stage 120 counts steps to monitor the user's activity. An exemplary step counting algorithm may be as described in WO 2015 / 183193.

[0031] Tuning Phase Algorithm Details According to Exemplary Embodiments 2 shows a flow chart illustrating the algorithm executed in the tuning phase according to an exemplary embodiment. After the device is turned on, the system starts tuning. In one exemplary embodiment, there may be a task schedule that may be up to 5 minutes. For example, if the device is turned on at 10:02 AM, tuning will start at 10:05 with the device configured to start the tuning algorithm at X:05, X:10, X:15, etc. Generally, the tuning process includes adjusting the sensor LED levels and verifying the detected signal.

[0032] Specifically, in this exemplary embodiment, the collection stage 202 collects the PPG signal and the ACC signal for 2 seconds, and the low motion stage 204 checks whether the criterion for failure case 8, e.g. the intensity and / or frequency of the ACC signal representing motion, is below e.g. walking at 4 km / h or running at e.g. 6.5 km / h, if not, the criterion for failure case 8 for high motion is considered to be triggered, i.e. "No" is output from the low motion stage 204.

[0033] Next, the non-pulsatile component (DC) level stage 206 checks the fail case 4 criterion, e.g., if the DC level of the PPG signal is less than a threshold fraction of the sensor LED supply voltage, tuning proceeds. The threshold fraction may be about 83% of the sensor LED supply voltage in this exemplary embodiment. On the other hand, if the DC level is equal to or greater than the threshold fraction, the fail case 4 criterion of high DC level is deemed to be triggered, i.e., a "no" is output from the DC level stage 206.

[0034] Next, time usage step 208 checks the criteria for failure case 7. In this exemplary embodiment, if the time usage of the tuning is greater than a threshold, e.g., 24 seconds, or generally within about 8 seconds to 4 minutes, the tuning proceeds to PPG pulse step 210. On the other hand, if the time usage is outside that range, the time usage criterion for failure case 7 is deemed to have been triggered, i.e., a "yes" is output from time usage step 208.

[0035] Next, the PPG pulse step 210 in this exemplary embodiment checks whether at least three PPG pulses are found in the pulsatile component (AC component) of the PPG signal having a reference rise time within 10%≦RT≦50%, as a non-limiting example of information about at least one cardiac cycle based on at least one pulse in the PPG signal. If yes, tuning proceeds to PPG amplitude step 212. If no, the sensor LED drive current is increased, as shown as step 214, and a determination is made as to whether the sensor LED drive current is at the maximum applicable value, e.g., 20 mA, as shown in step 216. If the sensor LED is at the maximum drive current (or LED intensity) and at least three PPG pulses are not found, then fail case 2 is considered to be triggered. If not, tuning loops back as shown in "A" of FIG. 2.

[0036] The first PPG amplitude stage 212 checks whether the PPG AC amplitude is above a threshold portion of the sensor LED supply voltage and whether the sensor LED drive current is above a minimum value, for example, 1 mA. The threshold portion may be 50% in this exemplary embodiment. If the output from the first PPG amplitude stage 212 is "no", tuning proceeds to the second PPG amplitude stage 218. If the output from the first PPG amplitude stage 212 is "yes", the sensor LED drive current is decreased, as shown as stage 220, and a determination is made whether the sensor LED drive current is equal to (or below) the applicable minimum value, as shown in stage 222. If the sensor LED is at the minimum drive current (or LED intensity) in stage 222, then fail case 6 is deemed to be triggered. If not, tuning loops back as shown in "A" of FIG. 2.

[0037] The second PPG amplitude stage 218 checks whether the PPG AC amplitude is greater than or equal to an acceptable threshold and whether the LED drive current is less than the applicable maximum value. The acceptable threshold may be approximately 20 mV to 70 mV in this exemplary embodiment. If the output from the second PPG amplitude stage 218 is "yes", tuning proceeds to a PPG pulse pair stage 224. If the output from the second PPG amplitude stage 218 is "no", the sensor LED drive current is increased, as shown as stage 226, and a determination is made whether the sensor LED drive current is equal to (or greater than) the applicable maximum value, as shown in stage 227.

[0038] In step 227, if the sensor LED is at maximum drive current (or LED intensity), then fail case 5 is deemed to be triggered. Otherwise, a determination is made as to whether the PPG amplitude is less than a threshold portion of the acceptable amplitude, as shown in step 228. The threshold portion may be 28% in this exemplary embodiment. If yes, then fail case 3 is deemed to be triggered. If no, then tuning loops back as shown in "A" of FIG.

[0039] In the PPG pulse pair stage 224, as a non-limiting example of information about at least one cardiac cycle based on at least one pulse in the PPG signal, for each pair of (at least) three pulses, it is checked whether the PSD (power spectral density) correlation is greater than a correlation threshold and whether the RT difference is less than a given RT difference threshold. In this exemplary embodiment, the correlation threshold may be 90% and the RT difference threshold may be 10%. The PSD correlation may be a pulse shape correlation in this exemplary embodiment. If the output from the PPG pulse stage 224 is "yes", tuning proceeds to the HR stage 230. If the output from the PPG pulse stage 224 is "no", tuning loops back as shown in "A" of FIG. 2.

[0040] Note that other conditions are added to the PPG pulse stage 224, the difference in HR between all pairs in the three PPG pulses is checked, and it can be determined whether any of the differences is less than about 20% to 35% of the normal resting HR. In one exemplary embodiment, the absolute difference in HR between two pulses that is less than 20 bpm is used, but more generally, in the exemplary embodiment, about 20% to 35% that is equal to about 12 bpm to 35 bpm can be used.

[0041] The HR stage 230 checks whether the heart rate HR is within the range of 30 < HR < 240. If the output from the HR stage 230 is "no", it is considered that the failure case 1 is triggered. Otherwise, the tuning is considered to be qualified, and the PPG template is constructed for use in other parts of the entire algorithm.

[0042] Note that in this exemplary embodiment, the algorithm may be applicable to both humans and animals (such as cows, sheep, etc.). In an exemplary embodiment specific to humans only, the heart rate (HR) range may be, for example, 30 < HR < 120.

[0043] Similarly, in an exemplary embodiment specific to humans only, the rise time range (to be compared with the PPG pulse stage 210) may be, for example, 10% ≦ RT ≦ 40% as a non-limiting example of information regarding at least one cardiac cycle based on at least one pulse in the PPG signal.

[0044] For each of the failure cases 1 to 3, the device turns off the sensor LED until motion is detected by checking the TAT threshold and / or the PIM threshold as described above with reference to FIG. 1.

[0045] Note that in different exemplary embodiments, the decision-making steps and the number, type, and / or order / sequence of related processes for determining each failure case may be changed compared to the flowchart shown in FIG. 2.

[0046] List-Off Phase Algorithm Details According to an Exemplary Embodiment An algorithm executed during the wrist-off phase (see number 114 in FIG. 1 ) according to an exemplary embodiment identifies a wrist-off status when the user is not wearing the device (or is not wearing the device properly) and a wrist-on status when the user is wearing the device.

[0047] It should be noted that while the user is wearing the device (wrist-on state), the wearing state can be at rest or on the move, where the resting state can include sleeping. On the move can include irregular movements such as, but not limited to, coding (i.e., typing on a computer / keyboard), lecturing, discussion, etc., and regular movements such as, but not limited to, running, walking, cycling, etc.

[0048] During the wrist-off state, the user is not wearing the device and may be in a stable or moving state, by way of example and not limitation, as follows:

[0049] Stable conditions include: 1) being placed on a table with a bright environment (neon lights, display monitor, sunlight, etc.), 2) being placed on a table with a shadow from body movement, and 3) being placed in a dark environment (e.g., a dark room or inside a bag).

[0050] Moving states include 1) being placed in a dark environment while moving (e.g., walking with the bag while the device is in it) and 2) being placed in a bright environment while moving (e.g., walking while holding the device in your hand).

[0051] FIG. 3 shows a diagram illustrating feature extraction for wrist-off detection according to a non-limiting example embodiment.

[0052] The PPG signal 300 is separated into 4 second windows, e.g., 302, each having 332 data points, as shown in steps, and the data is mapped into new dimensions, i.e., extracted features, as shown in steps (five features in one exemplary embodiment for further calculations). Note that the period of each window, e.g., 302, can be of any predetermined duration, and preferably, two beats / pulses, e.g., are included within each window. In general, accuracy may vary due to window size, with larger window sizes typically providing higher accuracy and smaller window sizes typically requiring less processing.

[0053] Generally, feature data=(feat1, feat2, feat3, ...) is extracted as shown in steps, classification into list-on or list-off is performed, and a corresponding output signal is generated as shown in steps.

[0054] Below are listed the features calculated for each window, e.g., 302, according to a non-limiting exemplary embodiment. 1) Average DC level of the PPG signal 2) Signal-to-noise ratio of the AC components of the PPG signal in the frequency domain 3) Dispersion of the DC level of the PPG signal in the time domain 4) Variance of the AC components of the PPG signal in the time domain 5) The sum of the number of saturation points and zero points of the AC components of the PPG signal in the time domain

[0055] In one exemplary embodiment, the main features are 1) the average DC level, and 2) the signal-to-noise ratio of the AC components in the frequency domain, while 3) the variance of the DC level, 4) the variance of the AC components, and 5) the sum of the number of saturation points and zero points of the AC components are optional features used in specific cases, which will be described in detail below.

[0056] 1) PPG DC level Figure 4 shows a comparison of DC levels measured while wearing the device at rest (left side) or during high exertion (right side). As can be seen from the results in Figure 4, body movement has no discernible effect on DC levels.

[0057] Figure 5(a) shows that when placed in a dark environment, the measured DC level is close to zero. Figure 5(b) shows that when placed directly facing the ambient light, the measured DC level is saturated (i.e., equal to the supply voltage). This can be used to distinguish those scenarios from the wrist-on condition.

[0058] Therefore, the DC level of the PPG signal has been found, in accordance with an exemplary embodiment, to be a useful parameter for identifying a wrist-off condition when the device is placed in a dark environment or directly facing ambient light.

[0059] However, Figure 5(c) shows that the measured DC level can be above zero and below the saturation value when exposed to indirect ambient light, which may not be useful in distinguishing from a wrist-on condition.

[0060] Figure 5(d) shows that the DC level has high variance with unstable light as a result of the device being placed in a shadow caused by body movement (i.e., wrist-off state). However, it was found that the variance of the DC level in the wrist-off state in Figure 5(d) can be similar to the wrist-on state, the reflected light can vary depending on the condition of the user's anatomy (skin, tissue, or bone), and the DC level can be 0.1 Vdc to 2.2 Vdc.

[0061] 2.PPG_AC

[0062] The signal-to-noise ratio (SNR) of the AC components of the PPG signal in the frequency domain was found to be effective in discriminating the ambient light noise during the wrist-off stage.

[0063] When worn, the AC frequency component range of the PPG signal is usually 0.5Hz to 8Hz, as shown in Fig. 6(a). It has been found that if there is noise, i.e., the output signal has one or more other frequencies (other than 0.5Hz to 8Hz), as shown in Fig. 6(b), the noise may be due to a wrist-off state with ambient light as the light source, such as a neon lamp or a display monitor.

[0064] In an exemplary embodiment, the SNR is used to check the characteristics of the signal transmitter and receiver, i.e., the sensor LED and photodetector, respectively, by comparing the level (or power) of the desired signal with the level (or power) of the noise. It has been found that analyzing signals in the frequency domain (amplitude vs. frequency) is preferable compared to analyzing signals in the time domain (amplitude vs. time) for calculating the signal-to-noise ratio, since signals in the frequency domain show the dominant frequencies, i.e., signals can be classified according to their frequency.

[0065] In an exemplary embodiment, the root mean square (rms) of the AC frequency domain amplitude within the PPG signal range is compared to the rms of the amplitude within the noise range. As mentioned above, with a typical PPG range of frequency content of 0.5 Hz to 8 Hz, this range is assumed to be the PPG signal, while the remainder of the measured range is assumed to be noise, specifically, in an exemplary embodiment, 8 Hz to 50 Hz, as shown in FIG.

[0066] To identify the list-on / list-off states according to an exemplary embodiment, a threshold can be pre-determined for each feature. FIG. 8 shows a scatter plot for the features 1) PPG DC level and 2) SNR of PPG_AC (SNR is based on rms, as described above, in one exemplary embodiment) in the list-on and list-off reference / training data. The rectangles represent clusters for list-on conditions according to an exemplary embodiment. The plot in FIG. 8 shows that the list-on and list-off data are well separated. Thus, a threshold for the classification model according to an exemplary embodiment can be set.

[0067] In one exemplary embodiment, the list-on condition can be set as average (PPG DC level)=0.001V to 2.5V, and SNR PPG_AC>5dB to 10dB.

[0068] Although the two main features, mean (PPG DC level) and SNR PPG_AC, are capable of classifying data with high accuracy according to an exemplary embodiment, there are cases where these two main features are not sufficient / accurate enough to identify wrist on / wrist off states.

[0069] One case could be a wrist-off condition when the device is placed under direct sunlight without electronic light. On the other hand, under direct sunlight conditions, the PPG_AC signal was found to have a very low amplitude. Therefore, this case can be classified based on a threshold of the variance (PPG_AC), e.g., less than 0.15mV-0.25mV in one exemplary embodiment. FIG. 9 shows a scatter plot (with a magnified inset) of the variance (PPG AC) vs. mean (PPG DC) for the reference / training data, where the dark line circle indicates the wrist-off condition when the device is placed under direct sunlight, from which the threshold can be set according to an exemplary embodiment.

[0070] Another case may be wrist-off state with the device in hand, in which it was found that there is high variance in both DC and AC PPG signal components up to the limit of the supply voltage, including the lower (zero) boundary equal to zero supply voltage. It was found that when the limit of the supply voltage is reached (or exceeded), the signal saturates at the higher (saturated) boundary of the supply voltage and becomes zero at the lower (zero) boundary of the supply voltage, and the AC signal shows both saturated and zero values. Therefore, the counts of variance (PPG DC) and the sum of the number of saturations and the number of zeros (sumACSatZero) of the PPG AC signal are useful in the exemplary embodiment. In particular, it was found by the inventors that the number of saturations and the number of zeros (sumACSatZero) of the PPG AC signal are very distinctive between wrist-off state and wrist-on state, as shown in FIG. 19.

[0071] 10(a) and 10(b) show scatter plots of variance (PPG DC) vs. mean (PPG DC) and sumACSatZero vs. mean (PPG DC), respectively. Hand wrist-off holds in the reference / training data are shown as light-colored line circles, which allows setting thresholds, e.g., variance (DC)≧0.1-1.0 and / or sumACSatZero≧100-200, according to an exemplary embodiment.

[0072] FIG. 11 shows a flow chart illustrating wrist-off condition detection according to an exemplary embodiment.

[0073] In step 1102, raw PPG DC and AC data is collected in a 4 second window, ie, without any filtering such as finite impulse filtering.

[0074] In step 1104, the total number of saturation points and zero points of the mean PPG DC, the variance PPG DC, the variance PPG AC, and the PPC AC are calculated.

[0075] In step 1106, the calculated total number of saturation points and zero points of the average PPG DC, variance PPG DC, variance PPG AC, and PPC AC are compared with the respective thresholds of the list-on state, and if all the conditions are not met, i.e., the output from step 1106 is "no", the state is determined to be list-off. The values ​​of the thresholds are shown according to an exemplary embodiment.

[0076] In step 1108, if the output from step 1106 is "YES", the PPG AC signal is transformed into the frequency domain.

[0077] In step 1110, the rms values ​​in the signal and noise ranges, respectively, are first calculated in the frequency domain as part of a later calculation of the SNR (see step 1116 below). The range values ​​are shown according to an exemplary embodiment.

[0078] In steps 1112a and 1112b, the rms of the noise signal is limited to a minimum value to avoid calculation errors in subsequent calculations. The value of the minimum value is shown according to an exemplary embodiment.

[0079] In step 1114, the rms of the PPG AC signal and the rms of the PPG AC noise are compared to respective thresholds for the list-on state, and if either of both conditions is not met, the state is determined as list-off. The values ​​of the thresholds are shown according to an exemplary embodiment.

[0080] In step 1116, the SNR is calculated based on the ratio of the rms PPG AC signal to the rms PPG AC noise.

[0081] In steps 1118a and 1118b, the calculated SNR is limited to a minimum value to avoid calculation errors in subsequent calculations, the value of which is shown according to an exemplary embodiment.

[0082] In step 1120, the SNR in dB is calculated.

[0083] In step 1122, the SNR in dB is compared to a threshold. If the result is "no", the state is determined to be list off. If the result is "yes", the state is determined to be list on. The value of the threshold is shown according to an exemplary embodiment.

[0084] In an exemplary embodiment, a counter of wrist-off state determinations is implemented, which ensures that display of PPG / ACC data analysis algorithm results, such as HR results, is disabled only during a "true" wrist-off state, and may serve to turn off the sensor LED rather than showing an inappropriate result. In other words, in an exemplary embodiment, accuracy may be improved by using a predetermined number of wrist-off state output results that must be reached before display of the PPG / ACC data analysis algorithm results is disabled, rather than based on each individual output result.

[0085] It should be noted that in different exemplary embodiments, the number, type, and / or order / sequence of the decision-making steps and associated processes for classifying the list-on / list-off state may be modified as compared to the flowchart shown in FIG.

[0086] In an exemplary embodiment, the counter threshold is set based on seven consecutive 4-second time windows, as illustrated in FIG. 12(a) and FIG. 12(b), and wrist-off state outputs in each of the seven windows are counted. If the wrist-off state is indicated for more than half of the consecutive 4-second time windows, the overall wrist status is determined as wrist-off, and the display of the PPG / ACC data analysis algorithm result is disabled or displayed as "NA". In this embodiment, if the wrist-off state is detected four or more times, the overall wrist status is determined as wrist-off. If the wrist-off state is indicated less than four times, the overall wrist status is determined as wrist-on. This process continues for the next seven windows if the overall wrist status is determined as wrist-on.

[0087] Results of List-On / List-Off Classification According to Exemplary Embodiments FIG. 13 shows the results obtained from an evaluation of the data of 21 subjects, resulting in a list-off condition classification accuracy of over 90%.

[0088] The list-on conditions used in the evaluation were as follows: Average (PPG DC) 0.001~0.080<Average (PPG DC)<2.2V~2.5V, SNR(PPG AC) >5dB~10dB, Dispersion (PPG AC) >0.15mV~0.30mV, Dispersion (PPG DC) <0.1mV~1.0mV, Number of PPG AC saturations + number of PPG AC zeros < 100 points to 200 points.

[0089] 15 shows a high level flow chart illustrating the tuning and wrist-off detection method / algorithm according to an exemplary embodiment. A tuning stage 1502 is operated every hour in this exemplary embodiment, and once the tuning stage 1502 is passed, other algorithms are executed as shown in stage 1504, and a wrist-off detection stage 1506 is operated every 5 minutes in this exemplary embodiment. Loop arrows 1508, 1510 indicate the periodic operation of the tuning stage 1502 and the wrist-off detection stage 1506, respectively. It should be noted that the timing may vary in various exemplary embodiments and is not limited to the exemplary timing shown in FIG. 15.

[0090] In the following, a general description of the wearable device 102 (FIG. 1) according to an exemplary embodiment is provided. FIG. 16 shows an overall schematic diagram of the device 102. The PPG sensor(s) 1630 are connected to a signal conditioning circuit 1640 and a driver circuit 1620, which are connected to a control and processing unit (CPU) 1610, which includes tuning and wrist-off detection algorithms according to an exemplary embodiment, and which is also connected to a motion sensor(s) 1650, a display 1690, a power management unit 1660, a memory 1670, and a communication module 1680.

[0091] FIG. 17 is a detailed diagram of components 1610, 1620, 1630, 1640, showing a drive current control component 1621 (which adjusts the intensity of the LEDs) and a digital-to-analog converter (DAC) circuit 1622 that converts the digital value commanded by the MCU to be an analog value for the current control circuit 1621. The PPG sensor(s) 1630 includes a light source(s) 1631 and a photodetector(s) 1632. The light source(s) 1631 are connected to the current control circuit 1621 and the DAC circuit 1622 to control the intensity. The photodetector(s) 1632 detect the light, and the detected signal is processed by a transimpedance amplifier and noise filtering component 1641 to obtain a non-pulsatile signal (DC component) and is further processed by an analog filter (BPF) and amplifier 1642 to obtain a pulsatile signal (AC component). The AC and DC components of the signal are sent to the control and processing unit 1610 for further processing and calculations.

[0092] In an alternative embodiment shown in FIG. 18, the signal conditioning circuit 1640 may instead comprise a noise filtering (analog) component 1641a, an ADC circuit 1642a, and a noise filtering (digital) component 1643a, which process the detected signals to obtain non-pulsatile (DC) and pulsatile (AC) signals in digital form and transmit them to the control and processing unit 1610 for further processing and calculations.

[0093] 22 shows a diagram illustrating determining the rise time and / or fall time of a cardiac cycle 2200 contained in a PPG signal 2202, according to an exemplary embodiment. The CPU 1610 (FIG. 16) is configured to detect a first valley location 2204, a systolic peak location 2206, and a second valley location 2210 of the cardiac cycle 2200 (and thus the PPG pulse 2208). It should be appreciated that the filtered PPG signal 2202 also includes a dicrotic notch 2212 and a diastolic peak 2214, which in an exemplary embodiment may be used to determine whether the collected PPG signal can be attributed to the wearer being a human or an animal.

[0094] The CPU 1610 (FIG. 16) is configured to calculate the rise time based on the first valley location 2204 and the systolic peak 2206 of the cardiac cycle 2200. Mathematically, referring to FIG. 22, the rise time RT of the cardiac cycle 2200 is RT=100×RT(rise time) / total time of the PPG pulse 2208. A typical range of RT in humans is about 10%-40%, and in animals is about 10%-50%. Alternatively or additionally, the CPU determines the fall time of the cardiac cycle 2200. Mathematically, the fall time FT is FT=total time of the PPG pulse 2208-RT. A typical range of FT in humans is about 60%-90%, and in animals is about 50%-90%.

[0095] That is, in the tuning algorithm according to an exemplary embodiment, determining whether the collected PPG signal can be attributed to the wearer being a human or an animal may include determining information about at least one cardiac cycle 2200 based on the PPG pulse 2208, and more specifically, may include determining one or more of the group consisting of rise time, fall time, time information about the dicrotic notch 2212, time information about the diastolic peak 2214, and information about the shape of the PPG pulse 2208 or the shape of one or more portions of the PPG pulse 2208. Similarly, determining whether the difference in the information about the at least one cardiac cycle is less than a threshold may include determining a difference in one or more of the group consisting of rise time, fall time, time information about the dicrotic notch 2212, time information about the diastolic peak 2214, and information about the shape of the PPG pulse 2208 or the shape of one or more portions of the PPG pulse 2208.

[0096] 20 shows a flowchart 2000 illustrating a method of tuning a wearable device to collect physiological data of a wearer, according to an exemplary embodiment. In step 2002, a PPG signal is collected using the device. In step 2004, it is determined whether the collected PPG signal can be attributed to the wearer being a human or an animal, and in step 2006, if the collected PPG signal cannot be attributed to the wearer being a human or an animal, the tuning is stopped, and determining whether the collected PPG signal can be attributed to the wearer being a human or an animal includes determining information regarding at least one cardiac cycle based on at least one pulse in the PPG signal.

[0097] The information relating to at least one cardiac cycle may include one or more of the group consisting of rise time, fall time, time information relating to the dicrotic notch, time information relating to the diastolic peak, and information relating to the shape of at least one pulse in the PPG signal or the shape(s) of one or more portions of at least one pulse in the PPG signal.

[0098] The method may include increasing an intensity of a light source for PPG measurement if it is determined that the information about the at least one cardiac cycle is not within a predetermined range. The method may include determining whether a drive current of the light source has reached a maximum value and, if so, determining a tuning failure. The method may include repeating collecting PPG signals using the device if the drive current of the light source has not reached a maximum value.

[0099] The method may include determining whether an amplitude of the PPG signal is greater than a first threshold and whether a drive current of the light source is above a minimum value, and reducing an intensity of the light source if both conditions are met. The method may include determining whether a drive current of the light source has reached a minimum value, and if so, determining a tuning failure. The method may include repeating collecting the PPG signal using the device if the drive current of the light source has not reached the minimum value.

[0100] The method may include determining whether the amplitude of the PPG signal is greater than or equal to a second threshold and whether a drive current of the light source is less than a maximum value, and increasing an intensity of the light source if both conditions are met. The method may include determining whether the drive current of the light source is greater than or equal to a maximum value, determining whether the PPG amplitude is less than a third threshold, and determining a tuning failure if at least one of the conditions is met. The method may include repeating collecting the PPG signal using the device if both conditions are not met.

[0101] The method may include determining whether a correlation of power spectral densities for multiple pairs of pulses in the PPG signal exceeds a fourth threshold, determining whether a difference in information regarding at least one cardiac cycle is less than a fifth threshold, and repeating collecting the PPG signal using the device if at least one of the conditions is not met.

[0102] The method may include determining whether a correlation of power spectral densities exceeds a fourth threshold, whether a difference in information regarding at least one cardiac cycle is less than a fifth threshold, and whether a difference in HR is less than a sixth threshold relative to a normal resting heart rate for multiple pairs of pulses in the PPG signal, and repeating collecting the PPG signal using the device if at least one of the three conditions is not met.

[0103] The method may include determining whether the HR is within the normal range for a human and / or animal.

[0104] In one embodiment, a wearable device for collecting physiological data of a wearer is provided, the device comprising: a PPG sensor; a signal collection unit coupled to the PPG sensor and collecting PPG signals using the PPG sensor for tuning the device; and a processor coupled to the signal collection unit and configured to determine whether the collected PPG signals can be attributed to the wearer being a human or an animal, the processor being configured to abort tuning if the collected PPG signals cannot be attributed to the wearer being a human or an animal, and determining whether the collected PPG signals can be attributed to the wearer being a human or an animal comprises determining information regarding at least one cardiac cycle based on at least one pulse in the PPG signal.

[0105] The information relating to at least one cardiac cycle may include one or more of the group consisting of rise time, fall time, time information relating to the dicrotic notch, time information relating to the diastolic peak, and information relating to the shape of at least one pulse in the PPG signal or the shape(s) of one or more portions of at least one pulse in the PPG signal.

[0106] The processor may be configured to increase an intensity of a light source of the PPG sensor for PPG measurement if it is determined that the information regarding at least one cardiac cycle of the pulses in the PPG signal is not within a predetermined range. The processor may be configured to determine whether a drive current of the light source reaches a maximum value and, if so, determine tuning failure. The signal collection unit may be configured to repeat collecting the PPG signal if the drive current of the light source does not reach a maximum value.

[0107] The processor may be configured to determine whether the amplitude of the PPG signal is greater than a first threshold and whether the drive current of the light source is above a minimum value, and to reduce the intensity of the light source if both conditions are met. The processor may be configured to determine whether the drive current of the light source has reached a minimum value, and if so, to determine a tuning failure. The signal collection unit may be configured to repeat collecting the PPG signal if the drive current of the light source has not reached the minimum value.

[0108] The processor may be configured to determine whether the amplitude of the PPG signal is greater than or equal to a second threshold and whether the drive current of the light source is less than a maximum value, and to increase the intensity of the light source if both conditions are met. The processor may be configured to determine whether the drive current of the light source is greater than or equal to a maximum value, determine whether the PPG amplitude is less than a third threshold, and determine a failure of tuning if at least one of the conditions is met. The signal collection unit may be configured to repeat collecting the PPG signal if both conditions are not met.

[0109] The processor may be configured to determine whether the correlation of the power spectral densities for multiple pairs of pulses in the PPG signal exceeds a fourth threshold and to determine whether the difference in information relating to at least one cardiac cycle is less than a fifth threshold, and the signal collection unit is configured to repeat collecting the PPG signal if at least one of the conditions is not satisfied.

[0110] The processor may be configured to determine whether the correlation of the power spectral densities for multiple pairs of pulses in the PPG signal exceeds a fourth threshold, whether the difference in information regarding at least one cardiac cycle is less than a fifth threshold, and whether the difference in HR is less than a sixth threshold relative to a normal resting heart rate, and the signal collection unit is configured to repeat collecting the PPG signal using the device if at least one of the three conditions is not met.

[0111] The processor may be configured to determine whether the HR is within a normal range for a human and / or animal.

[0112] FIG. 21 shows a flowchart 2100 illustrating a method of controlling a wearable device that collects physiological data of a wearer, according to an exemplary embodiment. In step 2102, a PPG signal is collected using the device. In step 2104, the PPG signal is processed to obtain an analysis result. In step 2106, it is determined whether the collected PPG signal can be attributed to the device not being worn by the wearer. In step 2108, if the collected PPG signal can be attributed to the device not being worn by the wearer, the display of the analysis result by the device is disabled and / or the light source of the device is turned off.

[0113] Determining whether the collected PPG signal may be due to the device not being worn by the wearer may include extracting one or more features from each time window of the PPG data.

[0114] Determining whether the collected PPG signal may be due to the device not being worn by the wearer may include determining a mean and variance of a DC component of the PPG signal, a variance of an AC component of the PPG signal, a number of times the amplitude of the AC component is saturated, and a number of times the amplitude of the AC component is zero. The method may include determining whether the mean of the DC component is within a first range, determining whether the variance of the DC component is within a second range, determining whether the variance of the AC component is within a third range, determining whether the sum of the number of times the amplitude of the AC component is saturated and the number of times the amplitude of the AC component is zero is less than a first threshold, and determining a wrist-off if any one of the conditions is not met.

[0115] Determining whether the collected PPG signal may be due to the device not being worn by the wearer may include calculating a signal-to-noise ratio of the PPG signal.

[0116] The signal-to-noise ratio of the PPG signal can be determined in the frequency domain.

[0117] A signal-to-noise ratio of the PPG signal may be determined based on the root mean square of the PPG signal in a signal range and a noise range, respectively. The method may include determining that the device is not worn if the root mean square in the signal range is less than or equal to a second threshold and / or the root mean square in the noise range is greater than or equal to a third threshold.

[0118] The method may include determining that the device is not worn if the calculated signal to noise ratio is less than or equal to a fourth threshold.

[0119] In one embodiment, a wearable device for collecting physiological data of a wearer is provided, the device comprising: a signal collecting unit for collecting PPG signals; and a processor coupled to the signal collecting unit for processing the PPG signals to obtain analysis results, the processor being configured to determine whether the collected PPG signals can be attributed to the device not being worn by the wearer, and to disable display of the analysis results and / or turn off a light source of the device if the collected PPG signals can be attributed to the device not being worn by the wearer.

[0120] The processor may be configured to determine, based on extracting one or more features from each time window of the PPG data, whether the collected PPG signal may be attributable to the device not being worn by the wearer.

[0121] The processor may be configured to determine whether the collected PPG signal may be attributable to the device not being worn by the wearer based on determining the average and variance of the DC component of the PPG signal, the variance of the AC component of the PPG signal, the number of times the amplitude of the AC component is saturated, and the number of times the amplitude of the AC component is zero. The processor may be configured to determine whether the average of the DC component is within a first range, determine whether the variance of the DC component is within a second range, determine whether the variance of the AC component is within a third range, determine whether the sum of the number of times the amplitude of the AC component is saturated and the number of times the amplitude of the AC component is zero is less than a first threshold, and determine wrist-off if any one of the conditions is not met.

[0122] The processor may be configured to determine whether the collected PPG signal may be attributable to the device not being worn by the wearer based on calculating a signal-to-noise ratio of the PPG signal.

[0123] The signal-to-noise ratio of the PPG signal can be determined in the frequency domain.

[0124] The signal-to-noise ratio of the PPG signal may be determined based on the root mean square of the PPG signal in a signal range and a noise range, respectively. The processor may be configured to determine that the device is not worn if the root mean square in the signal range is less than or equal to a second threshold and / or the root mean square in the noise range is greater than or equal to a third threshold.

[0125] The processor may be configured to determine that the device is not worn if the calculated signal-to-noise ratio is less than or equal to a fourth threshold.

[0126] In one embodiment, a computer-readable medium is provided that includes instructions that, when executed by a computing device, instruct the device to perform a method according to one of the exemplary embodiments.

[0127] Various functions or processes disclosed herein may be described in terms of their behavior, register transfers, logical components, transistors, layout geometry, and / or other characteristics as data and / or instructions embodied in various computer-readable media. Computer-readable media in which such formatted data and / or instructions may be embodied include, but are not limited to, various forms of non-volatile storage media (e.g., optical, magnetic, or semiconductor storage media) and carrier waves that may be used to transfer such formatted data and / or instructions through wireless, optical, or wired signaling media or any combination thereof. Examples of transfer of such carrier-wave formatted data and / or instructions include, but are not limited to, transfer (upload, download, email, etc.) over the Internet and / or other computer networks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP, etc.). When received within a computer system via one or more computer-readable media, such data and / or instruction-based representations of the components and / or processes under the described system may be processed by a processing entity (e.g., one or more processors) within the computer system in conjunction with the execution of one or more other computer programs.

[0128] Aspects of the systems and methods described herein may be implemented as programmed functions in any of a variety of circuits, including programmable logic devices (PLDs), such as field programmable gate arrays (FPGAs), programmable array logic (PAL) devices, electrically programmable logic and memory devices, and standard cell-based devices, as well as application specific integrated circuits (ASICs). Some other possibilities for implementing aspects of the system include microcontrollers with memory (such as electrically erasable programmable read-only memory (EEPROM)), embedded microprocessors, firmware, software, and the like. Additionally, aspects of the system may be embodied in microprocessors with software-based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types. Of course, the underlying device technology can be provided in a variety of component types, e.g., metal-oxide-semiconductor field-effect transistor (MOSFET) technologies such as complementary metal-oxide-semiconductor (CMOS), bipolar technologies such as emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, etc.

[0129] The above description of illustrated embodiments of the systems and methods are not intended to be exhaustive or to limit the systems and methods to the precise forms disclosed. While specific embodiments and examples of system components and methods are described herein for illustrative purposes, those skilled in the art will recognize that various equivalent modifications are possible within the scope of the systems, components, and methods. The teachings of the systems and methods provided herein may be applied to other processing systems and methods, as well as the systems and methods described above.

[0130] It will be understood by those skilled in the art that numerous variations and / or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as generally described. The present embodiments are therefore considered in all respects to be illustrative and not restrictive. The present invention also includes any combination of features, and in particular any combination of features in the claims, even if that feature or combination of features is not expressly recited in the claims or in the present embodiments.

[0131] Modifications can be made to the systems and methods in light of the above detailed description.

[0132] In general, in the claims that follow, the terms used should not be construed to limit the systems and methods to the specific embodiments disclosed in this specification and summary, but should be construed to include all processing systems operating under the summary. Thus, the systems and methods are not limited by the disclosure in the exemplary embodiments described herein.

[0133] Unless the context clearly dictates otherwise, throughout this specification and claims, words like "comprise," "comprising," and the like are to be construed in an inclusive sense, i.e., "including, but not limited to," as opposed to an exclusive or exhaustive sense. Words using the singular or plural also include the plural or singular, respectively. In addition, the words "herein," "hereunder," "above," "below," and words of similar import refer to this application as a whole and not to any particular portions of this application. When the word "or" is used in reference to a list of two or more items, the word includes all of the interpretations of that word, i.e., any of the items in the list, all of the items in the list, and any combination of the items in the list.

Claims

1. 1. A method for tuning a wearable device that collects physiological data of a wearer, comprising: acquiring a PPG signal using said device; determining whether the collected PPG signal is attributable to the wearer being a human or an animal; abandoning the tuning if the collected PPG signal is not attributable to the wearer being a human or an animal; Including, The method, wherein determining whether the collected PPG signal can be attributed to the wearer being a human or an animal includes determining that the wearer is a human or an animal if information regarding at least one cardiac cycle based solely on one or more pulses in the collected PPG signal is within a first range.

2. 2. The method of claim 1, wherein the information regarding the at least one cardiac cycle includes one or more of the group consisting of rise time, fall time, time information regarding a dicrotic notch, time information regarding a diastolic peak, and information regarding a shape of the at least one pulse in the collected PPG signals or a shape(s) of one or more portions of the at least one pulse in the collected PPG signals.

3. The method of claim 2 , further comprising increasing an intensity of a light source for the PPG measurement if the information regarding the at least one cardiac cycle is determined not to be within a predetermined range.

4. 4. The method of claim 1, comprising determining whether an amplitude of the collected PPG signal is greater than a first threshold and whether the drive current of the light source is above a minimum value, and reducing the intensity of the light source if both conditions are met.

5. 2. The method of claim 1, comprising determining whether an amplitude of the collected PPG signal is greater than or equal to a second threshold and whether a drive current of a light source is less than a maximum value, and increasing an intensity of the light source if at least one of the conditions is not met.

6. 2. The method of claim 1, comprising: determining whether a correlation of power spectral densities for a plurality of pairs of pulses in the collected PPG signals exceeds a fourth threshold, whether a difference in the information regarding the at least one cardiac cycle is less than a fifth threshold, and whether a difference in heart rate HR based on the collected PPG signals is less than a sixth threshold relative to a normal resting heart rate, and repeating collecting PPG signals using the device if at least one of the three conditions is not met.

7. The method of claim 1, comprising determining whether the heart rate HR is within the normal range for humans and / or animals.

8. A wearable device that collects physiological data of a wearer, comprising: A PPG sensor; a signal acquisition unit coupled to the PPG sensor and configured to acquire a PPG signal using the PPG sensor for tuning the device; a processor coupled to the signal collection unit for determining whether the collected PPG signal is attributable to the wearer being a human or an animal; Equipped with the processor is configured to discontinue the tuning if the collected PPG signal is not attributable to the wearer being a human or an animal; The device, wherein determining whether the collected PPG signals can be attributed to the wearer being a human or an animal includes determining that the wearer is a human or an animal when information regarding at least one cardiac cycle based solely on one or more pulses in the collected PPG signals is within a first range.

9. 9. The device of claim 8, wherein the information regarding the at least one cardiac cycle includes one or more of the group consisting of rise time, fall time, time information regarding the dicrotic notch, time information regarding a diastolic peak, and information regarding a shape(s) of the at least one pulse in the collected PPG signals or a shape(s) of one or more portions of the at least one pulse in the collected PPG signals.

10. 10. The device of claim 9, wherein the processor is configured to increase an intensity of a light source of the PPG sensor of the PPG measurement if the information regarding the at least one cardiac cycle is determined to not be within a predetermined range.

11. 11. The device of claim 9 or 10, wherein the processor is configured to determine whether an amplitude of the collected PPG signal is greater than a first threshold and whether the drive current of the light source is above a minimum value, and to reduce the intensity of the light source if both conditions are met.

12. 10. The device of claim 9, wherein the processor is configured to determine whether an amplitude of the collected PPG signal is greater than or equal to a second threshold and whether a drive current of a light source is below a maximum value, and to increase an intensity of the light source if at least one of the conditions is not met.

13. 9. The device of claim 8, wherein the processor is configured to determine whether the correlation of power spectral densities for multiple pairs of pulses in the collected PPG signal exceeds a fourth threshold, whether the difference in the information regarding the at least one cardiac cycle is less than a fifth threshold, and whether the difference in heart rate HR is less than a sixth threshold relative to a normal resting heart rate, and the signal collection unit is configured to repeat collecting PPG signals using the device if at least one of the three conditions is not met.

14. 1. A method for controlling a wearable device that collects physiological data of a wearer, comprising: acquiring a PPG signal using said device; processing the PPG signal to obtain an analysis result; determining that the device is not worn by the wearer if the collected information about only the PPG signal does not satisfy at least one condition; if the collected PPG signal can be attributed to the device not being worn by the wearer, disabling the display of the analysis results by the device and / or turning off a light source of the device; A method comprising:

15. 15. The method of claim 14, wherein determining whether the collected PPG signal can be attributed to the device not being worn by the wearer includes determining a mean and variance of a DC component of the collected PPG signal, a variance of an AC component of the collected PPG signal, a number of times the amplitude of the AC component is saturated, and a number of times the amplitude of the AC component is zero.

16. 16. The method of claim 14 or 15, wherein determining whether the collected PPG signal can be attributed to the device not being worn by the wearer comprises calculating a signal-to-noise ratio of the collected PPG signal.

17. A wearable device that collects physiological data of a wearer, comprising: a signal acquisition unit for acquiring a PPG signal; a processor coupled to the signal acquisition unit for processing the PPG signal to obtain an analysis result; Equipped with A device, wherein the processor is configured to determine that the device is not worn by the wearer if information relating only to the collected PPG signal does not satisfy at least one condition, and to disable display of the analysis results and / or turn off a light source of the device if the collected PPG signal can be attributed to the device not being worn by the wearer.

18. 18. The device of claim 17, wherein the processor is configured to determine whether the collected PPG signal can be attributed to the device not being worn by the wearer based on determining a mean and variance of a DC component of the collected PPG signal, a variance of an AC component of the collected PPG signal, a number of times the amplitude of the AC component is saturated, and a number of times the amplitude of the AC component is zero.

19. 19. The device of claim 17 or 18, wherein the processor is configured to determine whether the collected PPG signal can be attributed to the device not being worn by the wearer based on calculating a signal-to-noise ratio of the collected PPG signal.

20. A computer readable medium comprising instructions that, when executed by a computing device, instruct the device to perform the method of claim 1 and / or claim 14.

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