Classifications

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  • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
  • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• • A—HUMAN NECESSITIES
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  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
  • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/024—Measuring pulse rate or heart rate
  • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
  • A61B5/02427—Details of sensor
• • A—HUMAN NECESSITIES
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  • A61B5/026—Measuring blood flow
  • A61B5/0261—Measuring blood flow using optical means, e.g. infrared light
• • A—HUMAN NECESSITIES
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  • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
  • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
  • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
  • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
  • A61B5/14552—Details of sensors specially adapted therefor
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
  • A61B5/6802—Sensor mounted on worn items
  • A61B5/681—Wristwatch-type devices
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient; User input means
  • A61B5/742—Details of notification to user or communication with user or patient; User input means using visual displays
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
  • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
  • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
  • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
  • A61B2562/0233—Special features of optical sensors or probes classified in A61B5/00
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
  • A61B2562/16—Details of sensor housings or probes; Details of structural supports for sensors
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/4806—Sleep evaluation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
  • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

• PPG photoplethysmogram
• the fingertip is an area of the body where there is almost no bone, a wide presence of capillaries, and the opportunity for light to pass directly from the LED through the skin to the PD and where the skin can form a better seal with the LED and PD, resulting in more uniform light scattering effects and more accurate and efficient detection of the pulse wave.
• a fingertip oximeter is an example of such a device.
• a cabled or wireless clip is attached to the finger and LEDs emit Infrared (IR) and Near Infrared (NIR) light with wavelengths between 640 nm and 940 nm.
• IR Infrared
• NIR Near Infrared
• the reflected light is used to accurately detect the pulse wave, create the PPG signal from which the RR-interval (the interval between two successive heartbeats) and other important parameters can be deduced, such as Blood Oxygenation (SpO2), Heart Rate (HR), Heart Rate Variability (HRV) and Blood Pressure (BP).
• SpO2 Blood Oxygenation
• HR Heart Rate
• HRV Heart Rate Variability
• BP Blood Pressure
• PPG technology is well established for fingertip use (where it can capture high quality readings because there is no bone and the fingertip skin forms a good seal with the sensor).
• traditional devices such as pulse oximeters that capture PPG from fingertip insertion
• standalone devices that also capture high quality PPG signals from the fingertip, such as smartphones.
• PPG technology does not appear to have been incorporated on the side of a wrist worn device.
• side sensors on smart watches exist, no devices with IR and NIR sensors incorporated into the side of a wearable device to allow the wearer to place their fingertip on the side sensor to obtain high quality PPG readings exists. Accordingly, there is a need for a wearable device that is not so limited.
• wearable biometric monitors are available, most have limited functionality. For instance, most are limited to measuring steps taken/di stance covered and heart rate. Those interested in a more in-depth profile and understanding of their health must do so with an inconvenient trip to their health care professional which often includes an invasive procedure. Moreover, the reliability of data generated when the device is moving, is questionable and PPG signals can create measurement artifacts. Accordingly, there is a need for a wearable device with multiple sensors, detects movement and that continuously or on demand, provides high quality PPG data conveniently and without the need for invasive procedures.
• the present application provides method(s), wearable device(s), and computer readable media for measuring personal health.
• the method includes detecting a photoplethysmograph (PPG) signal by a sensor, the PPG signals are generated by infra-red, green and/or red lights emitted from one or more emitters of a personal healthcare device, transmitting the PPG signal data to a server, the server processing the PPG signal data to infer biometric statistics based on machine learned correlations generated from a training set of PPG signals and biometric data, receiving the biometric statistics from the server, and generating display data based on the biometric statistics.
• PPG photoplethysmograph
• the non-transitory computer-readable media includes computer program code for detecting a photoplethysmograph (PPG) signal by a sensor, the PPG signals are generated by infra-red, green or red lights emitted from one or more emitters of a personal healthcare device, computer program code for transmitting the PPG signal to a server, the server processing the PPG signal to infer biometric statistics based on machine learned correlations generated from a training set of PPG signals and biometric data, computer program code for receiving the biometric statistics from the server, and computer program code for generating display data based on the biometric statistics.
• PPG photoplethysmograph
• the biometric statistics may include at least one of overall health, changes in health, mood, sleep quality, fatigue, and stress.
• the biometric data may include at least one of heart rate, respiratory rate, steps taken, calories burned, distance covered, sleep quality, ECGZEKG, blood pressure, mood, fatigue, body temperature, glucose levels, blood alcohol, and blood oxygen.
• the machine learned correlations are based on PPG character vectors including a Kaiser-Teager power energy value, a heart rate value, and a spectral entropy value.
• one or more of the emitters and/or sensors are located on a side of the wearable device.
• FIG. l is a block diagram of a personal healthcare device according to at least one embodiment herein.
• FIG. 2 is a block diagram of a personal healthcare device operating in a network environment according to at least one embodiment herein.
• FIG. 3 A is a perspective view of a personal healthcare device according to one embodiment herein.
• FIG. 3B is another perspective view of a personal healthcare device according to one embodiment herein.
• FIG. 4A is a perspective view of a personal healthcare device according to another embodiment herein.
• FIG. 4B is a diagram of a personal healthcare device according to another embodiment herein.
• FIG. 4C is a perspective view of a personal healthcare device in operation according to one embodiment herein.
• FIG. 5 are a set of views of the wrist band for a personal healthcare device according to one embodiment herein.
• FIG. 6A is a diagram of a flat inline sensor (FIS) for a personal healthcare device according to one embodiment herein.
• FIS flat inline sensor
• FIG. 6B is a chart of details for exemplary LEDs that may be used in a preferred embodiment of the FIS according to one embodiment herein.
• FIG. 6C is a chart of details of exemplary photodiodes and photodiode combinations that may be used in a preferred embodiment of the FIS according to one embodiment herein.
• FIG. 6D is a diagram showing a functional implementation of the FIS adjacent to skin according to one embodiment herein.
• FIG. 7 is a flowchart of a method for training a learning machine to interpret data from a personal healthcare device according to one embodiment herein.
• FIGS. 8-15 are a set of interface screens displayed on a mobile device app associated with the personal healthcare device according to one embodiment herein.
• FIGS. 16A-F are various views of a personal healthcare device according to another embodiment herein.
• FIGS. 17A-F are various views of a personal healthcare device with a charging dock according to another embodiment herein.
• Fig. 18 is a table showing status signals produced according to an exemplary embodiment of the personal healthcare devices herein.
• a wearable personal healthcare device 100 includes a processor 202 coupled to a computer memory 204.
• the device 100 may be a smartwatch, smart band, or other wearable electronic device. Although the device 100 may be shown and described as a watch, it is understood that the functionality may be implemented in other devices, including a smartphone or tablet, or any other device capable of performing the functionality disclosed herein, and the meaning of a device as used herein is therefore not limited thereto.
• the memory stores therein software that when executed causes the device 100 to perform the functions discussed herein.
• the processor 202 is preferably further coupled to a transmitter/receiver 206 that enables communication between the device 100 and other devices as discussed below.
• the device 100 preferably includes at least one emitter 208 and one or more sensors 210.
• the emitter 208 is generally a device that emits energy that is received by sensor 210 in a transformed state.
• the emitter 208 and sensor 210 are controlled by the processor 202 to emit energy and process the transformed energy, received by the sensor 210 into usable biometric data, as discussed herein.
• One or more sensors [and emitters] may include a sensor package that is directly connected to the main board of the device for acquisition and processing of signals.
• Various types of emitters may be used with device 100, including light (visible and invisible spectrum), heat, sound, conduction, etc.
• Device 100 may include a plurality of each of the emitters/sensors, such as a combination of infra-red and red lights, and corresponding sensors.
• the device may further include one or more sensors 210 operable to gather hemodynamic and other data which device 100 uses signal processing in processor 202 and/or other improvements to reduce the signal noise and then this data is transmitted for further processing remotely into more meaningful parameters such as heart rate, respiratory rate, fat percentage, steps taken, ECG/EKG, blood pressure, body temperature, glucose levels, blood alcohol, blood oxygen, etc.
• the noise may be reduced mechanically, with a raised edge on the border of the sensor glass (as shown in Figs. 4A-4B) to provide a better seal between the LED, PD, and the fingertip.
• the device may detect ambient light (e.g., with a separate sensor) at the time of the reading and erase or otherwise cancel interference or noise attributable to the ambient light.
• the raw data collected by the device from these sensors 210 may be processed and/or collected remotely on a server to infer, for example, overall health, changes in health, mood, sleep quality, fatigue or stress, etc.
• the device 100 is therefore operable to collect data to enable a wealth of personal health data that includes one or more of the following: heart rate, respiratory rate, steps taken, calories burned, distance covered, sleep quality, ECG/EKG, arrhythmia detection, bioimpedance, (BIA), acceleration plethysmogram (APG), blood pressure, mood, fatigue, body temperature, glucose levels, blood alcohol, blood oxygen, etc.
• the device 100 may also include one or more of the following features: iPhone/ Android connectability, or as a standalone loT (internet of things) device to allow for remote monitoring of vitals, for example, by a health professional, panic button (that plays audio and visual alarm, communicates GPS position and message to preconfigured address, etc.), accommodate germanium stones, provide a mosquito shield, display location based air quality, detect noxious gasses, etc.
• device 100 may automatically measure certain biometric data through an internal timer.
• the rate at which measurements are taken may be preset or set remotely by the wearer, carer or an authorized third party. For example, the rate may be every 30 min, 60 min, etc., selected from a drop-down menu of available rates.
• the device 100 may further collect data continually, for use, for example, for inferring some of the conclusions therefrom while still displaying and charting the periodic measurements. For example, the device 100 may collect heart rate data continuously and use that to determine heart rate variability, while still only charting hourly measurements.
• the device 100 may further or alternatively include sensors that assess biometric data on demand, i.e., when a user elects to take a measurement.
• device 100 includes at least one sensor to collect information regarding environmental conditions at the location of the device 100, such as temperature, humidity, weather conditions, e.g., rain or snow, as well as air quality.
• Air quality may be assessed with a gas sensor, for example, that is configured to determine the existence and levels of hazardous or unhealthy materials and/or conditions.
• the sensor may monitor for low or high levels of temperature, humidity, oxygen, ozone, carbon monoxide, VOCs, TVOCs, odor, sulfur, flammable gases, air quality, etc., or monitoring any other air quality standard.
• the device 100 may determine the level on a scale ranging, for example, from 1-5, where level one may indicate normal conditions and level five unacceptable conditions.
• the level may be based on one or a plurality of the environmental readings, for example, a combination of medium VOC and medium carbon monoxide levels may be combined to a higher combined level of 4 or 5, for example.
• top and bottom sensors may be used in which the user touches a top sensor 308 (and preferably 309) with one or two fingers to complete the circuit therebetween and/or with a bottom sensor 402 touching the wrist to gather additional data which enables biometric impedance to be measured and body composition to be deduced in near real-time. Fingers on sensors 308 and 309 may further detect the electrical signals produced by the user’s heart to generate ECG suitable for clinical and fitness applications, as shown in Fig. 15.
• the environmental sensor may be located anywhere on the device, preferably on an outside face so that interference from contaminants may be minimized.
• the device 100 may further include a sensor/emitter pair, preferably inline, on the lateral side or the rear of the device 100, arranged to reflect signals between each other as with the other inline sensor/emitter embodiments discussed herein.
• the sensors 310 and 404 may be a flat inline sensor (FIS) 600 that may be used to obtain biometric data via the photoplethysmography (PPG) signals, such as heart rate, respiratory rate, ECGZEKG, blood pressure, glucose levels, blood alcohol, blood oxygen, etc.
• PPG photoplethysmography
• the FIS may be mechanically applied adjacent to the body at the surface of the skin 608, below which there are blood vessels. By applying the FIS adjacent to the body at the surface of the skin, it is possible to obtain valid PPG signals at different frequencies, which can be used to determine or otherwise derive blood glucose levels in these blood vessels.
• the user of device 100 may initiate measurement directly on the menu of the device via the touchscreen display or on a menu of a connected device in communication with the device 100.
• the user will place his or her fingertip on sensor 310 for few seconds (usually from 30 to 60 seconds) until a light is emitted, as shown in Fig. 4C.
• the flow for the side sensor operation may, in certain embodiments, include selecting the desired function on the device or a connected device, placing a fingertip on the side sensor, holding the fingertip on the sensor for the necessary amount of time, view the results on the display of the device, and sync the results, e.g.
• Fig. 6C outlines details of exemplary photodiodes and photodiode combinations that may be used according to a preferred embodiment of the FIS.
• the bottom or underside of the device 100 is shown. This side may include one or more sensors thereon in or around sensor 402.
• the bottom of the device 100 preferably includes a plurality of sensors, including at least a synchronous red light and near infrared light emitters/sensors.
• the band includes a plurality of apertures, equally spaced to accommodate the pedestals having stones thereon.
• the cross section of the apertures is preferably hourglass shaped to retain similarly shaped legs on the pedestal.
• the pedestals are inserted from the inside of the band through the apertures therein.
• the pair of legs of the pedestals fit flush with the outside of the band, as shown.
• the tops of the pedestals include at least one stone.
• a stone is generally a material that is believed to have beneficial properties, such as gold, silver, copper, germanium, magnets, salt, etc.
• the pedestals beneficially bring the stones into contact with the wearer's skin.
• Fig. 7 presents a flowchart of a method for training a learning machine to interpret data from a personal healthcare device according to one embodiment herein according to one embodiment herein.
• the device 100 employs synchronous red light and near infrared light to generate a photopl ethysmograph (PPG) signal that is analyzed to determine a biometric measure, such as blood glucose level (BGL) or any of the other biometrics discussed herein.
• PPG photopl ethysmograph
• a PPG signal is received by an analytical server from device 100, step 702.
• the steps in assessing biometrics using PPG may be premised on the correlation between PPG and BGL, blood pressure, etc.
• the correlation between PPG and key nutrition elements and/or blood vessel endothelium health status, as well as environmental data may also be analyzed and correlated. .
• This analysis may be achieved by sampling, for example, 1000 persons for PPG signal data, standard BGL, blood pressure, environmental, etc., to produce training data for machine learning.
• the test may be undertaken, for example, before breakfast every day for 14 consecutive days.
• Obtaining biometric data - biometric data is received, step 704 - using, for example, a personal healthcare device with a flat inline sensor with 660nm red light and 940nm near infrared light to get PPG data, the device may take 2 readings allowing 1 minute for each reading. Then the BGL may be tested using a medical level micro trauma blood glucose monitor. Blood pressure may also be taken with a cuff sphygmomanometer, again ensuring that two readings are taken. For each person, the test will continue for 2 weeks, 2 times every day around the same time each day with the first time in the morning before breakfast and the second time in the afternoon, 1 hour after eating lunch.
• a person’s sex, age, height, weight, country, ethnicity, cardiovascular and cerebro-vascular diseases history, metabolism diseases history, family diseases history, continuo and any ongoing medication or history of medical conditions may be provided along with the biometric data.
• the location, amount of caffeine taken, smoking and if so, to what extent, emotion, fatigue, environmental conditions, and so on may also be recorded.
• the PPG data generated may be processed to get the clean signal.
• Character vector data is extracted, step 706 - the PPG signal may be filtered with a band pass filter, allowing signals of about 0.5Hz to about 5 Hz and then an adaptive noise canceller may be applied using the recursive least squares or similar method.
• the key to usable data is to find the effective reference signal and extract the character vectors. From the clean signal, character vectors may be distinguished and extracted, and then supervised machine learning may be applied to compute a correlation. The resulting formula may be assessed against a subset of the test data to predict validity of the algorithm.
• KTE ⁇ x(n) — x(n + l)x(n — 1), where x is the electromyographic value and n is the sample number, segmented real-time power energy value: KTE , mean value KTE k , mean square deviation KTE quarter distance KTE a , n n 1 n 1 n slewness KTE ⁇ , and corresponding segments KTE 11 , KTE , KTE a , KTE ⁇ may be obtained.
• Heart rate value from the PPG wave, the corresponding HR 11 , HR , HR a , HR ⁇ may be computed.
• Spectral entropy can be useful and to be considered, for determining the FFT (fast Fourier transform) for the segmented signal, means «- FFT(x(n), L)followed
• Tl regularization Knowing the probability mass function Px , then the entropy may be computed, H «- Px n Log(Ex n
• the segmented data may be: If computing overflow happens, conduct log function, LogE ⁇ - Log x(n)), knowing LogE and LogE a .
• Red light and near infrared light peak values, Pr, Pi may also be computed independent of the power value.
• the segmented time duration can be 5s to 10s, the signal is x(n), and the corresponding matrix is Xi.
• new valid vector elements may be added and trivial impact signals removed.
• a correlation between PPG signal and biometric data may be determined using supervised machine learning, step 708.
• the vector dimension may be from 10 to 20 from the PPG data. Randomly, 90% of the data may be placed into the training set, another 10% into the test set.
• the device s high-quality photoplethysmogram (PPG) obtained using the side sensor 310 illuminates the fingertip and measures changes in light absorption due to blood volume changes in the microvascular bed of tissue.
• PPG photoplethysmogram
• the second derivative of this PPG can be used to determine vascular aging and the degree of atherosclerosis, which can be presented on a 1-7 scale on your interface screen, as shown in Fig. 14.
• Fig. 12 presents an interface screen for displaying sleep data of a user that is determined from a personal healthcare device according to one embodiment herein.
• Sleep data may include total sleep duration, duration of deep sleep, duration of light sleep, and how many times during sleep did the user wake up.
• One or more data points for sleep may be plotted on a chart over a given period.

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• 2023
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