Method and device for collecting physiological data from wearers

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 generally relates to a method and a device for collecting physiological data of a wearer, and particularly to a method for tuning a wearable device for collecting physiological data of a wearer, a method for controlling a wearable device for collecting physiological data of a wearer, and a wearable device for collecting physiological data of a wearer.

Background Art

[0002] Any reference and / or discussion of the prior art throughout this specification should not be construed as an admission that this prior art is known in the art or forms part of common general knowledge.

[0003] Wearable devices that monitor bio / physiological signals such as photoplethysmography (PPG) signals and signals representing movement such as accelerometer (ACC) signals have become widely used. Such devices can assist in various forms of data tracking, including sleep monitoring, such as fitness and health-related analysis.

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

[0005] In addition, the physiological state of animals can provide farmers or researchers with useful knowledge and insights about the animals, such as their health, function, and psychological state. Wearable devices can be attached to the animal's skin, for example, the ears, nose, neck, head, hooves, legs, upper part of the tail, or upper part of the spine. The “wrist-off” state in animals may mean the “drop-off” state of the device from the animal's skin.

[0006] Embodiments of the present invention aim to address one or more of the above-mentioned problems. [Overview of the Initiative]

[0007] According to a first aspect of the present invention, a method is provided for tuning a wearable device that collects physiological data of the wearer, the method being: The steps include: collecting the PPG signal using a device, A step of determining whether the collected PPG signal may be attributable to the wearer being a human or an animal, If the collected PPG signal cannot be attributed to the wearer being a human or an animal, the tuning is stopped. Includes, Determining whether the collected PPG signal may be attributable to the wearer being human or animal includes determining information about 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, a wearable device is provided for collecting physiological data of the wearer, the device is PPG sensor and A signal acquisition unit coupled to a PPG sensor, which uses the PPG sensor to collect PPG signals for device tuning, A processor coupled to a signal acquisition unit determines whether the acquired PPG signal may be attributable to the wearer being a human or an animal, Equipped with, The processor is configured to terminate tuning if the collected PPG signal cannot be attributed to the wearer being a human or an animal. Determining whether the collected PPG signal may be attributable to the wearer being human or animal includes determining information about at least one cardiac cycle based on at least one pulse in the PPG signal.

[0009] A third aspect of the present invention provides a method for controlling a wearable device that collects physiological data of a wearer, the method being: The steps include: collecting the PPG signal using a device, The steps include processing the PPG signal and obtaining the analysis results, A step of determining whether the collected PPG signal may be due to the device not being worn by the wearer, If the collected PPG signal may be due to the device not being worn by the wearer, the steps include disabling the display of the analysis results by the device and / or turning off the device's light source, Includes.

[0010] According to a fourth aspect of the present invention, a wearable device is provided for collecting physiological data of the wearer, and the device is A signal acquisition unit that collects PPG signals, A processor coupled to the signal acquisition unit processes the PPG signal and obtains analysis results, Equipped with, The processor is further configured to determine whether the collected PPG signal may be due to the device not being worn by the wearer, and if the collected PPG signal may be due to the device not being worn by the wearer, to disable the display of the analysis results and / or turn off the device's light source.

[0011] According to a fourth aspect of the present invention, a computer-readable medium is provided which, when executed by a computing device, includes instructions that instruct the computing device to perform the 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 in conjunction with the drawings, which are merely illustrative examples. [Brief explanation of the drawing]

[0013] [Figure 1] This flowchart shows the overall algorithm flow using an exemplary embodiment. [Figure 2] This flowchart shows an algorithm executed during the tuning phase according to an exemplary embodiment. [Figure 3] This figure shows feature extraction for list-off detection according to an exemplary embodiment. [Figure 4] This figure shows a comparison of DC levels measured while wearing the device at rest (left side) or during high-intensity exercise (right side), according to an exemplary embodiment. [Figure 5] (a) is a diagram showing that the measured DC level is close to zero when placed in a dark environment, according to an exemplary embodiment. (b) is a diagram showing that the measured DC level is saturated (i.e., equal to the supply voltage) when placed directly facing ambient light, according to an exemplary embodiment. (c) is a diagram showing that the measured DC level may be above zero and below the saturation value when placed exposed to indirect ambient light, according to an exemplary embodiment. (d) is a diagram showing that the DC level has high dispersion in unstable light as a result of the device being placed in a state where there is shadow due to body movement (i.e., a list-off state). [Figure 6](a) is a diagram showing that, according to an exemplary embodiment, the AC frequency component range of the PPG signal when the device is worn is usually 0.5 Hz to 8 Hz. (b) is a diagram showing that, according to an exemplary embodiment, when noise exists, that is, when the output signal has other single or multiple frequencies (other than 0.5 Hz to 8 Hz), the noise may be caused by the ambient light such as a neon lamp or a display monitor in the list-off state. [Figure 7] It is a diagram showing that, according to an exemplary embodiment, the typical frequency components of PPG are in the range of 0.5 Hz to 8 Hz, assuming that the range is the PPG signal, while assuming that the rest of the measured range is noise, specifically 8 Hz to 50 Hz. [Figure 8] It is a scatter diagram showing, according to an exemplary embodiment, the characteristics of 1) the PPG DC level and 2) the SNR of PPG_AC (the SNR is based on rms) in the list-on and list-off reference / training data. [Figure 9] It is a scatter diagram (with an enlarged inserted diagram) of the variance (PPG AC) and the mean (PPG DC) for the reference / training data according to an exemplary embodiment. [Figure 10] (a) is a scatter diagram of the variance (PPG DC) and the mean (PPG DC) according to an exemplary embodiment. (b) is a scatter diagram of sumACSatZero and the mean (PPG DC) according to an exemplary embodiment. [Figure 11] It is a flowchart showing the list-off state detection according to an exemplary embodiment. [Figure 12] (a) is a diagram showing that, according to an exemplary embodiment, the list-off state is determined based on seven consecutive time windows of the PPG data. (b) is a diagram showing that, according to an exemplary embodiment, the counter threshold is set based on seven consecutive 4-second time windows of the PPG data, and the list-off state output is counted in each of the seven windows. [Figure 13]A diagram showing results obtained from the evaluation of data of 21 subjects, which brings about list-off state classification with an accuracy exceeding 90% according to an exemplary embodiment. [Figure 14] (a) is a diagram showing exemplary zones of high motion and low motion based on different TAT thresholds and PIM thresholds according to an exemplary embodiment. (b) is a diagram showing exemplary zones of high motion and low motion based on different PIM thresholds according to an exemplary embodiment. [Figure 15] A high-level flowchart showing a tuning and list-off detection method / algorithm according to an exemplary embodiment. [Figure 16] An overall schematic diagram of a device according to an exemplary embodiment. [Figure 17] A detailed diagram of a specific component of the device of FIG. 16 according to an exemplary embodiment. [Figure 18] A detailed diagram of a specific component of the device of FIG. 16 according to another exemplary embodiment. [Figure 19] A diagram showing that dispersion (PPG DC) and counting the sum of the number of saturations and zeros of the PPG AC signal (sumACSatZero) are useful for list-off / off state classification according to an exemplary embodiment. [Figure 20] A flowchart showing a method for tuning a wearable device to collect physiological data of a wearer according to an exemplary embodiment. <0000​​​​​​​​​​​​​Figure 1 shows a flowchart illustrating the overall algorithm flow according to an exemplary embodiment. This algorithm generally receives its measurement input from a wearable device 102 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 their respective ACC sensor components). The signal 106 may be a combination of xy, yz, xz, and / or x, y, and z axes.

[0015] Both the PPG signal 104 and the ACC signal 106 are inputs to the algorithm's tuning stage 108. 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 failure in the form of “list-off,” i.e., the device 102 is not worn or is not properly worn, while failure cases 4-8 are used to determine a hypothetical “list-on” scenario, i.e., a general tuning failure in the 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 list-off determination. Also, “not properly worn” here means that the user is wearing the device 102, but it is loosely worn, such as the PPG and / or ACC sensors not making contact with the skin surface or being misplaced.

[0016] Examples of failure cases 1 to 3 in this exemplary embodiment are as follows:

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

[0018] Failure Case 2. The PPG sensor's light-emitting device (LED) drive current is at its maximum value (e.g., ≥20mA), but pulses containing information about cardiac cycles within a specific range (e.g., at least three pulses with rise times of 10% ≤ RT ≤ 50%, where RT represents the percentage of rise time in a single cardiac cycle) are not identified from the PPG signal.

[0019] Failure Case 3. PPG signal amplitude < For example, 28% of the acceptable amplitude (the acceptable amplitude can be set within the range of approximately 20mV to 70mV).

[0020] In this exemplary embodiment, if any one of the three failure cases described above is detected, the device is considered to be in a list-off status. In response to the determination of a list-off status, the LED sensor of device 102 is turned off until device 102 detects motion by checking the ACC signal 106 against a time (TAT) threshold and / or proportional-integral mode (PIM) threshold that exceeds the threshold. Figure 14(a) shows exemplary zones for high and low motion based on different TAT and PIM thresholds according to an exemplary embodiment. The high motion threshold may be 100 for TAT and 12 for PIM, and the low motion threshold may be 5 for TAT and 12 for PIM. When high motion is determined based on the TAT / PIM thresholds, the LED is turned back on for the next 5 minutes, and tuning in tuning stage 108 is resumed. Figure 14(b) shows another example with high and low motion zones based on different PIM thresholds (only), and in some exemplary embodiments, the PIM threshold may be approximately 12 to 85.

[0021] Examples of failure cases 4 to 8 in this exemplary embodiment are as follows:

[0022] Failure Case 4: Average DC level > For example, 83% of the supply voltage.

[0023] Failure Case 5: The LED drive current is at its maximum value, but the PPG amplitude is not acceptable when determined from the allowable amplitude.

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

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

[0026] Failure Case 8: The movement detected by ACC is high-intensity movement for a specific period, e.g., 10 seconds continuously. Note that any other predetermined period may be set and considered.

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

[0028] If the tuning determination stage 108 is successful, the motion artifact removal (MAR) stage 112 algorithm is processed. The output of the MAR stage 112 is the flagged motion, filtered PPG, and motion intensity (MS) value.

[0029] Furthermore, if the tuning determination stage 108 is successful, the algorithm of the list-off stage 114 is processed. An exemplary embodiment of the list-off stage 114 algorithm is described in detail below. Even if tuning is successful, the result from list-off (list detection flag_4s) is used to determine whether further processing results of a further algorithm, indicated as stage 115 such as HR, are shown / displayed, and whether the sensor LED is turned off. In this embodiment, the output from list-off stage 114 is used to control switch 116, which either supplies the output from MAR stage 112 to a further algorithm stage 115, or supplies a zero signal from zero signal generator stage 118. Note that, depending on the purpose of the further algorithm or the wavelength of light, the signal may not need to enter the MAR stage after passing through the tuning stage in different exemplary embodiments.

[0030] Through the execution of the algorithm, step stage 120 outputs activity results and measured TAT / PIM every minute. In short, step stage 120 monitors user activity by counting steps. An exemplary step counting algorithm may be one like the one described in International Publication No. 2015 / 183193.

[0031] Details of the tuning stage algorithm by an exemplary embodiment Figure 2 shows a flowchart illustrating the algorithm performed during the tuning phase in an exemplary embodiment. After the device is turned on, the system begins tuning. In one exemplary embodiment, there may be a task schedule that can be up to 5 minutes long. For example, if the device is turned on at 10:02 AM, a device configured to start the tuning algorithm at X:05, X:10, X:15, etc., would begin tuning at 10:05 AM. Generally, the tuning process involves adjusting the sensor LED levels and verifying the detected signals.

[0032] Specifically, in this exemplary embodiment, the collection stage 202 collects the PPG signal and the ACC signal for 2 seconds, and the low-exercise stage 204 checks whether the criteria for failure case 8, for example, the intensity and / or frequency intensity of the ACC signal representing exercise, is less than or equal to, for example, walking at 4 km / h or running at 6.5 km / h. If not, the criteria for failure case 8 for high exercise is considered to have been triggered, i.e., "NO" is output from the low-exercise stage 204.

[0033] Next, the non-pulsating component (DC) level stage 206 checks for the failure case 4 criterion. For example, if the DC level of the PPG signal is below a threshold portion of the sensor LED supply voltage, tuning proceeds. In this exemplary embodiment, the threshold portion may be approximately 83% of the sensor LED supply voltage. On the other hand, if the DC level is above the threshold portion, the high DC level failure case 4 criterion is considered triggered, i.e., the DC level stage 206 outputs "NO".

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

[0035] Next, the PPG pulse stage 210 in this exemplary embodiment checks whether at least three PPG pulses were found in the pulsating component (AC component) of the PPG signal having a reference rise time of 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 the PPG amplitude stage 212. If "no," the sensor LED drive current is increased, as shown as stage 214, and a determination is made as to whether the sensor LED drive current is at the applicable maximum value, e.g., 20 mA, as shown as stage 216. If the sensor LED is at the maximum drive current (or LED intensity) and at least three PPG pulses were not found, it is considered that failure case 2 has been triggered. Otherwise, tuning loops back as shown in "A" in Figure 2.

[0036] The first PPG amplitude stage 212 checks whether the PPG AC amplitude exceeds a threshold portion of the sensor LED supply voltage and whether the sensor LED drive current exceeds a minimum value, e.g., 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 reduced, as shown in stage 220, and a determination is made as to whether the sensor LED drive current is equal to (or below) the applicable minimum value, as shown in stage 222. In stage 222, if the sensor LED is at the minimum drive current (or LED intensity), it is considered that a failure case 6 has been triggered. Otherwise, tuning loops back as shown in "A" in Figure 2.

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

[0038] In step 227, if the sensor LED is at maximum drive current (or LED intensity), failure case 5 is considered triggered. Otherwise, as shown in step 228, it is determined whether the PPG amplitude is smaller than the threshold portion of the acceptable amplitude. The threshold portion may be 28% in this exemplary embodiment. If "yes," failure case 3 is considered triggered. If "no," the tuning loops back as shown in "A" in Figure 2.

[0039] In PPG pulse pairing 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 correlation of PSD (power spectral density) 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 PPG pulse stage 224 is "yes", tuning proceeds to HR stage 230. If the output from PPG pulse stage 224 is "no", tuning loops back as shown in "A" in Figure 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 an exemplary embodiment, about 20% to 35% 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 has been triggered. Otherwise, the tuning is considered to be qualified, and the PPG template is constructed for use in other parts of the entire algorithm. s

[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] Details of the List-Off Phase Algorithm in an Exemplary Embodiment The algorithm performed during the list-off phase (refer to number 114 in Figure 1) according to an exemplary embodiment identifies the list-off status if the user is not wearing the device (or is not properly wearing the device), and identifies the list-on status if 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 resting or moving, and resting may include sleep. Moving includes irregular movements such as coding (i.e., typing on a computer / keyboard), lectures, discussions, etc., and regular movements such as running, walking, cycling, etc.

[0048] During the list-off state, the user is not wearing the device and may be in a stable or mobile state, but is not limited to the following examples:

[0049] Stable conditions include: 1) being placed on a table in a bright environment (neon lights, display monitors, sunlight, etc.), 2) being placed on a table in a place where shadows from body movements are cast, and 3) being placed in a dark environment (for example, a dark room or inside a bag).

[0050] Examples of movement include 1) being placed in a dark environment while moving (e.g., walking with the device in a bag), and 2) being placed in a bright environment while moving (e.g., walking with the device in your hand).

[0051] Figure 3 shows a diagram illustrating feature extraction for list-off detection according to a non-limiting, exemplary embodiment.

[0052] The PPG signal 300 is separated into 4-second windows, e.g., 302, each having 332 data points, as shown as steps, and the data is mapped to a new dimension, i.e., extracted features (five features in one exemplary embodiment of further computation), as shown as steps. Note that the duration of each window, e.g., 302, can be any predetermined duration, and preferably, two beats / pulses, e.g., are contained within each window. Generally, accuracy can vary due to the window size, with larger window sizes usually resulting in higher accuracy and smaller window sizes usually resulting in less processing.

[0053] Generally, as shown in the steps, feature data = (feat1, feat2, feat3, ...) is extracted, classified as list-on or list-off, and the corresponding output signals are generated as shown in the steps.

[0054] The following lists the calculated features for each window, e.g., 302, according to a non-limiting exemplary embodiment. 1) Average DC level of PPG signal 2) Signal-to-noise ratio of the AC component of the PPG signal in the frequency domain 3) Variance of the DC level of the PPG signal in the time domain 4) Dispersion of the AC component of the PPG signal in the time domain 5) The sum of the number of saturation points and zeros of the AC component 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 component in the frequency domain, while 3) the variance of the DC level, 4) the variance of the AC component, and 5) the sum of the number of saturation and zero points of the AC component are optional features used in particular cases. The details are described below.

[0056] 1) PPG DC level Figure 4 shows a comparison of DC levels measured while wearing the device at rest (left) or during high-intensity exercise (right). As can be seen from the results in Figure 4, body movement does not have an identifiable effect on DC levels.

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

[0058] Therefore, according to exemplary embodiments, the DC level of the PPG signal was found to be a useful parameter for identifying the list-off state when the device is placed in a dark environment or directly exposed to 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. This may not be useful in distinguishing it from a list-on state.

[0060] Figure 5(d) shows that the DC level exhibits high dispersion due to unstable light when the device is placed in a position where there is shadow due to body movement (i.e., the wrist-off state). However, it was found that the dispersion of the DC level in the wrist-off state in Figure 5(d) can be similar to that in the wrist-on state, and that the reflected light can vary depending on the state of the user's anatomical structure (skin, tissue, or bone), with the DC level being between 0.1 Vdc and 2.2 Vdc.

[0061] 2. PPG_AC

[0062] The signal-to-noise ratio (SNR) of the AC component of the PPG signal in the frequency domain was found to be effective in distinguishing ambient light noise during the list-off phase.

[0063] When installed, the AC frequency component range of the PPG signal is typically 0.5Hz to 8Hz, as shown in Figure 6(a). As shown in Figure 6(b), it has been found that if there is noise, i.e., if the output signal has one or more other frequencies (other than 0.5Hz to 8Hz), this noise may be caused by a list-off state with ambient light from a neon lamp or display monitor as the light source.

[0064] In an exemplary embodiment, the characteristics of the signal transmitter and signal receiver, i.e., the characteristics of the sensor LED and photodetector, respectively, are checked by comparing the level (or power) of a desired signal with the level (or power) of noise using the SNR. It has been found that analyzing the signal in the frequency domain (amplitude vs. frequency) is preferable to analyzing the signal in the time domain (amplitude vs. time) to calculate the signal-to-noise ratio, since the signal in the frequency domain exhibits dominant frequencies, i.e., the signal can be classified according to those frequencies.

[0065] In an exemplary embodiment, the root mean square (rms) of the AC frequency domain amplitude within the PPG signal range is compared with the rms of the amplitude within the noise range. As described above, according to the frequency components of a typical PPG range 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 the exemplary embodiment, it is assumed to be 8 Hz to 50 Hz, as shown in Figure 7.

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

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

[0068] While the two main features, the average (PPG DC level) and SNR PPG_AC, allow for accurate classification of data according to exemplary embodiments, these two main features may not be sufficient or sufficiently accurate to identify the list-on / list-off state.

[0069] One case may be a list-off state when the device is placed in direct sunlight without electronic light. On the other hand, it has been found that under direct sunlight conditions, the PPG_AC signal has a very low amplitude. Therefore, this case can be classified based on a threshold of variance (PPG_AC), which in one exemplary embodiment is less than 0.15mV to 0.25mV. Figure 9 shows a scatter plot (with enlarged inset) of variance (PPG AC) and mean (PPG DC) for the reference / training data, where the circles with dark lines represent the list-off state when the device is placed in direct sunlight, from which a threshold can be set according to the exemplary embodiment.

[0070] Another case may be a list-off state where the device is held in the hand, in which case it was found that there is high variance in both the DC and AC PPG signal components up to the limit of the supply voltage, including a low (zero) boundary equal to zero supply voltage. When the limit of the supply voltage is reached (or exceeded), it was found that the signal saturates at the high (saturated) boundary of the supply voltage and becomes zero at the low (zero) boundary of the supply voltage, and the AC signal shows both saturated and zero values. Therefore, the variance (PPG DC) and the count of the sum of the number of saturations and zeros of the PPG AC signal (sumACSatZero) are useful in exemplary embodiments. In particular, the inventors found that the sum of the number of saturations and zeros of the PPG AC signal (sumACSatZero) is very characteristic between the list-off and list-on states, as shown in Figure 19.

[0071] Figures 10(a) and 10(b) show scatter plots of variance (PPG DC) and mean (PPG DC), and sumACSatZero and mean (PPG DC), respectively. Hand list-off holds in the reference / training data are shown as circles with light-colored lines, which allow for setting thresholds, e.g., variance (DC) ≥ 0.1 to 1.0 and / or sumACSatZero ≥ 100 to 200, according to exemplary embodiments.

[0072] Figure 11 shows a flowchart illustrating the detection of a list-off state according to an exemplary embodiment.

[0073] In step 1102, raw PPG DC and AC data are collected within a 4-second window, i.e., without filtering such as finite impulse filtering.

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

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

[0076] In step 1108, if the output from step 1106 was "yes", the PPG AC signal is converted to the frequency domain.

[0077] In step 1110, the rms values ​​in the signal range and noise range are first calculated in the frequency domain as part of a subsequent calculation of the SNR (see step 1116 below). The values ​​in the range 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 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 the respective thresholds for the list-on state, and if either of the conditions is not met, the state is determined to be list-off. The threshold values ​​are shown according to an exemplary embodiment.

[0080] In step 1116, the SNR is calculated based on the ratio of the rms of the PPG AC signal to the rms of the 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 minimum value 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 threshold value is shown according to an exemplary embodiment.

[0084] In an exemplary embodiment, a counter for determining the list-off state is implemented. This ensures that the display of PPG / ACC data analysis algorithm results, such as HR results, is disabled only during a "true" list-off state, which can help turn off the sensor LED rather than displaying an inappropriate result. In other words, in an exemplary embodiment, accuracy can be improved by using the output results of a predetermined number of list-off states that should be reached before disabling the display of PPG / ACC data analysis algorithm results, rather than relying on each individual output result.

[0085] Note that in different exemplary embodiments, the number, types, and / or order of decision-making steps and associated processes for classifying the list-on / list-off state may be modified compared to the flowchart shown in Figure 11.

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

[0087] Results of list-on / list-off classification according to an exemplary embodiment Figure 13 shows the results obtained from evaluating data from 21 subjects, which resulted in a list-off state classification accuracy of over 90%.

[0088] The list-on criteria used for evaluation are 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, The number of PPG AC saturations + the number of PPG AC zeros < 100 to 200 points.

[0089] Figure 15 shows a high-level flowchart illustrating a tuning and list-off detection method / algorithm according to an exemplary embodiment. Tuning stage 1502 is performed every hour in this exemplary embodiment, and after passing through tuning stage 1502, other algorithms are executed as shown in stage 1504, and list-off detection stage 1506 is performed every 5 minutes in this exemplary embodiment. Loop arrows 1508 and 1510 indicate the periodic operation of tuning stage 1502 and list-off detection stage 1506, respectively. Note that the timing may vary in various exemplary embodiments and is not limited to the exemplary timing shown in Figure 15.

[0090] The following provides a general description of a wearable device 102 (Figure 1) according to an exemplary embodiment. Figure 16 shows an overall schematic diagram of the device 102. PPG sensors (may be multiple) 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 list-off detection algorithms according to an exemplary embodiment. The CPU 1610 is also connected to motion sensors (may be multiple) 1650, a display 1690, a power management unit 1660, a memory 1670, and a communication module 1680.

[0091] Figure 17 is a detailed diagram of components 1610, 1620, 1630, and 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 digital values ​​commanded by the MCU to analog values ​​for the current control circuit 1621. The PPG sensor (may be multiple) 1630 includes a light source (may be multiple) 1631 and a photodetector (may be multiple) 1632. The light source (may be multiple) 1631 is connected to the current control circuit 1621 and the DAC circuit 1622 to control its intensity. The photodetector (may be multiple) 1632 detects light, and the detected signal is processed by a transimpedance amplifier and noise filtering component 1641 to obtain a non-pulsating signal (DC component), and further processed by an analog filter (BPF) and amplifier 1642 to obtain a pulsating signal (AC component). The AC and DC components of the signal are transmitted to the control and processing unit 1610 for further processing and calculations.

[0092] In an alternative embodiment shown in Figure 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 signal to obtain non-pulsating (DC) and pulsating (AC) signals in digital form and transmit them to a control and processing unit 1610 for further processing and calculation.

[0093] Figure 22 shows a diagram illustrating the determination of the rise and / or fall times of the cardiac cycle 2200 contained in the PPG signal 2202, according to an exemplary embodiment. The CPU 1610 (Figure 16) is configured to detect the first trough position 2204, the systolic peak position 2206, and the second trough position 2210 of the cardiac cycle 2200 (and thus the PPG pulse 2208). The filtered PPG signal 2202 also includes overlapping notches 2212 and diastolic peaks 2214, which, in an exemplary embodiment, may be used to determine whether the collected PPG signal could be attributable to the wearer being human or animal.

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

[0095] In other words, in the tuning algorithm according to the exemplary embodiment, determining whether the collected PPG signal may be attributable to the wearer being human or animal may include determining information about at least one cardiac cycle 2200 based on the PPG pulse 2208, more specifically, determining one or more of the group consisting of rise time, fall time, time information about overlapping notches 2212, time information about diastolic peaks 2214, and information about the shape of the PPG pulse 2208 or the shape of one or more parts of the PPG pulse 2208. Similarly, determining whether the difference in information about at least one cardiac cycle is below a threshold may include determining the difference in one or more of the group consisting of information about rise time, fall time, time information about overlapping notches 2212, time information about diastolic peaks 2214, and information about the shape of the PPG pulse 2208 or the shape of one or more parts of the PPG pulse 2208.

[0096] Figure 20 shows a flowchart 2000 illustrating a method for 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 attributable to the wearer being human or animal, and in step 2006, if the collected PPG signal cannot be attributable to the wearer being human or animal, the tuning is stopped, and determining whether the collected PPG signal can be attributable to the wearer being human or animal includes determining information about at least one cardiac cycle based on at least one pulse in the PPG signal.

[0097] Information about at least one cardiac cycle may include one or more of the following: rise time, fall time, time information regarding overlapping notches, time information regarding diastolic peaks, and information regarding the shape of at least one pulse in the PPG signal or the shape of one or more portions of at least one pulse in the PPG signal.

[0098] This method may include increasing the intensity of the PPG light source if it is determined that information regarding at least one cardiac cycle is not within a predetermined range. This method may include determining whether the drive current of the light source has reached its maximum value and, if so, determining that tuning has failed. This method may include repeating the acquisition of the PPG signal using the device if the drive current of the light source has not reached its maximum value.

[0099] This method may include determining whether the amplitude of the PPG signal is greater than a first threshold and whether the drive current of the light source exceeds a minimum value, and reducing the intensity of the light source if both conditions are met. This method may also include determining whether the drive current of the light source has reached a minimum value and determining that tuning has failed if it has reached a minimum value. If the drive current of the light source has not reached a minimum value, this method may also include repeatedly acquiring the PPG signal using the device.

[0100] This method may include determining 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 increasing the intensity of the light source if both conditions are met. This method may include determining whether the drive current of the light source is greater than or equal to a maximum value and whether the PPG amplitude is less than a third threshold, and determining that tuning has failed if at least one of these conditions is met. This method may include repeatedly collecting the PPG signal using the device if neither condition is met.

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

[0102] This method may include determining whether the correlation of power spectral densities in multiple pairs of pulses in the PPG signal exceeds a fourth threshold, whether the difference in information for 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 the normal resting heart rate, and repeating the process of collecting the PPG signal using the device if at least one of the three conditions is not met.

[0103] This method may include determining whether the HR is within the normal range for humans and / or animals.

[0104] In one embodiment, a wearable device is provided for collecting physiological data of a wearer, the device comprising: a PPG sensor; a signal acquisition unit coupled to the PPG sensor and using the PPG sensor to collect a PPG signal for tuning the device; and a processor coupled to the signal acquisition unit and determining whether the collected PPG signal could be attributable to the wearer being human or animal, the processor being configured to discontinue tuning if the collected PPG signal could not be attributable to the wearer being human or animal, and determining whether the collected PPG signal could be attributable to the wearer being human or animal includes determining information about at least one cardiac cycle based on at least one pulse in the PPG signal.

[0105] Information about at least one cardiac cycle may include one or more of the following: rise time, fall time, time information regarding overlapping notches, time information regarding diastolic peaks, and information regarding the shape of at least one pulse in the PPG signal or the shape of one or more portions of at least one pulse in the PPG signal.

[0106] The processor may be configured to increase the intensity of the light source of the PPG sensor for PPG measurement if it determines that information regarding at least one cardiac cycle of pulses in the PPG signal is not within a predetermined range. The processor may be configured to determine whether the drive current of the light source has reached its maximum value and, if so, determine that tuning has failed. The signal acquisition unit may be configured to repeat the acquisition of the PPG signal if the drive current of the light source has not reached its 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 exceeds a minimum value, and to reduce the intensity of the light source if both conditions are met. The processor may also be configured to determine whether the drive current of the light source has reached a minimum value, and if so, to determine that tuning has failed. The signal acquisition unit may be configured to repeat the acquisition of the PPG signal if the drive current of the light source has not reached a 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 the 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 the maximum value and whether the PPG amplitude is less than a third threshold, and to determine that tuning has failed if at least one of these conditions is met. The signal acquisition unit may be configured to repeatedly acquire the PPG signal if neither condition is met.

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

[0110] The processor may be configured to determine whether the correlation of power spectral densities in multiple pairs of pulses in the PPG signal exceeds a fourth threshold, whether the difference in information for 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 the normal resting heart rate, and the signal acquisition unit is configured to repeat the acquisition of 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 the normal range for humans and / or animals.

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

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

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

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

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

[0117] The signal-to-noise ratio of the PPG signal can be determined based on the root mean square of the PPG signal in the signal range and the noise range, respectively. This method may include determining that the device is not installed 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] This method may include determining that a device is not installed if the calculated signal-to-noise ratio is below a fourth threshold.

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

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

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

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

[0123] The signal-to-noise ratio of a 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 the signal range and the noise range, respectively. The processor may be configured to determine that the device is not installed 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 installed if the calculated signal-to-noise ratio is below a fourth threshold.

[0126] In one embodiment, a computer-readable medium is provided that, when executed by a computing device, contains instructions for instructing 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, logic 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 storage media, magnetic storage media, or semiconductor storage media) and carriers 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-formatted data and / or instructions include, but are not limited to, transfers over the Internet and / or other computer networks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP, etc.) (uploads, downloads, email, 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 processing entities within the computer system (e.g., one or more processors) in conjunction with the execution of one or more other computer programs.

[0128] The embodiments 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 the embodiments of the systems include microcontrollers with memory (such as electrically erasable programmable read-only memory (EEPROM)), embedded microprocessors, firmware, and software. Furthermore, the embodiments of the systems may be embodied in microprocessors having 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. Naturally, the underlying device technologies can be provided in various component types, such as metal-oxide-semiconductor field-effect transistor (MOSFET) technologies like complementary metal-oxide-semiconductor (CMOS), bipolar technologies like emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymers and metal-conjugated polymer-metal structures), and analog-digital hybrids.

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

[0130] Those skilled in the art will understand that numerous modifications and / or changes can be made to the invention as shown in specific embodiments without departing from the spirit or scope of the invention as comprehensively described. Therefore, these embodiments are considered illustrative and not limiting in all respects. Furthermore, the invention includes any combination of features, in particular any combination of features in the claims, even if those features or combinations of features are not expressly described in the claims or these embodiments.

[0131] Changes to the system and methods may be made in light of the detailed description above.

[0132] In general, the terms used in the following claims should not be construed as limiting the systems and methods to the specific embodiments disclosed herein and in the abstract, but rather as encompassing all processing systems operating under the abstract. Therefore, the systems and methods are not limited by the disclosures in the exemplary embodiments described herein.

[0133] Unless the context clearly indicates otherwise, throughout this specification and the claims, words such as “comprise,” “comprising,” and “including” should be interpreted in a comprehensive sense, as opposed to an exclusive or exhaustive sense, i.e., “including, but not limited to.” Words used in singular or plural also include plural or singular forms, respectively. In addition, the words “herein,” “hereunder,” “above,” “below,” and words of similar meaning refer to the entire application and not to any particular part thereof. When the word “or” is used in reference to a list of two or more items, the word encompasses all interpretations of the word, i.e., any item in the list, all items in the list, and any combination of items in the list.

Claims

1. A method for tuning a wearable device that collects physiological data from the wearer, The steps include: collecting photoplethysmography (PPG) signals using the wearable device; The steps include determining three conditions for multiple pairs of pulses in the collected PPG signal: whether the correlation of power spectral density exceeds a fourth threshold, whether the difference in information for at least one cardiac cycle is less than a fifth threshold, and whether the difference in heart rate (HR) based on the collected PPG signal is less than a sixth threshold relative to the normal resting heart rate. The steps include determining whether the collected PPG signal may be attributable to the wearer being a human or an animal, If the collected PPG signal cannot be attributed to the wearer being a human or an animal, the tuning is terminated. Includes, A method for determining whether the collected PPG signal may be attributable to the wearer being a human or an animal, comprising determining that the wearer is a human or an animal if the information relating to at least one cardiac cycle based on only one or more pulses in the collected PPG signal falls within a predetermined range.

2. The method according to claim 1, wherein the information relating to the at least one cardiac cycle includes one or more of the group consisting of rise time, fall time, time information relating to overlapping notches, time information relating to diastolic peaks, and information relating to the shape of the at least one pulse in the collected PPG signal or the shape of one or more portions of the at least one pulse in the collected PPG signal.

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

4. The method according to any one of claims 1 to 3, comprising determining whether the amplitude of the collected PPG signal is greater than a first threshold and whether the driving current of the light source is greater than a minimum value, and reducing the intensity of the light source if both conditions are met.

5. The method according to claim 1, comprising determining whether the amplitude of the collected PPG signal is greater than or equal to a second threshold and whether the driving current of the light source is less than a maximum value, and increasing the intensity of the light source if both conditions are met.

6. The method according to claim 1, comprising repeatedly collecting a PPG signal using the wearable device if at least one of the three conditions is not met.

7. The method according to 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 from the wearer, Photoplethysmography (PPG) sensor, A signal acquisition unit coupled to the PPG sensor and using the PPG sensor to collect PPG signals for tuning the wearable device, A processor coupled to the signal acquisition unit, which determines whether the acquired PPG signal may be attributable to the wearer being a human or an animal, Equipped with, The processor is configured to determine three conditions for multiple pairs of pulses in the collected PPG signal: whether the correlation of power spectral density exceeds a fourth threshold, whether the difference in information for 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 the normal resting heart rate. The processor is configured to discontinue tuning if the collected PPG signal cannot be attributed to the wearer being a human or an animal. A wearable device that determines whether the collected PPG signal may be attributable to the wearer being a human or an animal, by determining that the wearer is a human or an animal if the information relating to at least one cardiac cycle based on only one or more pulses in the collected PPG signal falls within a predetermined range.

9. The wearable device according to claim 8, wherein the information relating to the at least one cardiac cycle includes one or more of the group consisting of rise time, fall time, time information relating to overlapping notches, time information relating to diastolic peaks, and information relating to the shape of the at least one pulse in the collected PPG signal or the shape of one or more portions of the at least one pulse in the collected PPG signal.

10. The wearable device according to claim 9, wherein the processor is configured to increase the intensity of the light source of the PPG sensor for PPG measurement when it is determined that the information relating to at least one cardiac cycle is not within the predetermined range.

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

12. The wearable device according to claim 9, wherein the processor is configured to determine whether the amplitude of the collected PPG signal is greater than or equal to a second threshold and whether the drive current of the light source is less than or equal to a maximum value, and to increase the intensity of the light source if both conditions are met.

13. The wearable device according to claim 8, wherein the signal acquisition unit is configured to repeatedly acquire a PPG signal using the wearable device when at least one of the three conditions is not met.

14. A method for controlling a wearable device that collects physiological data from the wearer, The steps include: collecting photoplethysmography (PPG) signals using the wearable device; The steps include processing the PPG signal and obtaining the analysis result, If the information regarding the collected PPG signals alone does not satisfy at least one condition, the step of determining that the wearable device is not being worn by the wearer, If the collected PPG signal may be due to the wearable device not being worn by the wearer, the steps include disabling the display of the analysis results by the wearable device and / or turning off the light source of the wearable device. Includes, Determining whether the collected PPG signal may be due to the wearable device not being worn by the wearer includes determining the mean and variance of the DC component of the collected PPG signal and the variance of the AC component of the collected PPG signal. method.

15. The method according to claim 14, further comprising determining whether the collected PPG signal may be due to the wearable device not being worn by the wearer, the number of times the amplitude of the AC component has saturated and the number of times the amplitude of the AC component has been zero.

16. The method according to claim 14 or 15, wherein determining whether the collected PPG signal may be due to the wearable device not being worn by the wearer includes calculating the signal-to-noise ratio of the collected PPG signal.

17. A wearable device that collects physiological data from the wearer, A signal acquisition unit for collecting photoplethysmography (PPG) signals, A processor coupled to the signal acquisition unit, which processes the PPG signal and obtains analysis results, Equipped with, The processor is configured to determine that the wearable device is not being worn by the wearer if the information regarding the collected PPG signal alone does not satisfy at least one condition, and to disable the display of the analysis results and / or turn off the light source of the wearable device if the collected PPG signal may be due to the wearable device not being being worn by the wearer. The processor is configured to determine whether the collected PPG signal may be due to the wearable device not being worn by the wearer, based on determining the mean and variance of the DC component of the collected PPG signal and the variance of the AC component of the collected PPG signal. Wearable devices.

18. The wearable device according to claim 17, wherein the processor is further configured to determine whether the collected PPG signal may be due to the wearable device not being worn by the wearer, based on determining the number of times the amplitude of the AC component has saturated and the number of times the amplitude of the AC component is zero.

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

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

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