Abstract: A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Abstract: A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Abstract: A method and system for calibrating a wireless sensor device are disclosed. In a first aspect, the method comprises determining a vertical calibration vector and determining a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine a vertical calibration vector and to determine a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes.

Abstract: A method and system for automated sleep staging are disclosed. The method comprises determining at least one physiological signal during a predetermined time period, extracting at least one feature from the at least one physiological signal, and classifying the at least one feature using a machine learning classifier to output at least one sleep stage. The system includes a sensor to determine at least one physiological signal during a predetermined time period, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to extract at least one feature from the at least one physiological signal and to classify the at least one feature using a machine learning classifier unit to output at least one sleep stage.

Abstract: A method and system for calibrating a wireless sensor device are disclosed. In a first aspect, the method comprises determining a vertical calibration vector and determining a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine a vertical calibration vector and to determine a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes.

Abstract: A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Abstract: A method and wireless sensor device for determining a time period a person is in bed. In one aspect, a method includes utilizing a wireless sensor device to obtain a plurality of acceleration samples in relation to at least one axis associated with a person's body over a predetermined time window. The method also includes calculating a polar angle for each acceleration sample within the predetermined time window. The method also includes calculating a fraction of an amount of time within the predetermined time window that the polar angle is greater than an angle threshold that indicates that the person is lying down, where if the fraction is greater than a predetermined threshold, the predetermined time window is marked as a period the person is lying down.

Abstract: A method and system for determining psychological acute stress are disclosed. In a first aspect, the method comprises detecting a physiological signal using a wireless sensor device, determining a stress feature using a normalized heart rate and a plurality of heart rate variability (HRV) features, wherein the normalized heart rate and the plurality of heart rate variability features are calculated using the detected physiological signal, and determining a stress level using the stress feature to determine the psychological acute stress. In a second aspect, the system comprises a wireless sensor device that includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the wireless sensor device to carry out the steps of the method.

Abstract: A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Abstract: A method and wireless sensor device for determining a time period a person is in bed. In one aspect, a method includes utilizing a wireless sensor device to obtain a plurality of acceleration samples in relation to at least one axis associated with a person's body over a predetermined time window. The method also includes calculating a polar angle for each acceleration sample within the predetermined time window. The method also includes calculating a fraction of an amount of time within the predetermined time window that the polar angle is greater than an angle threshold that indicates that the person is lying down, where if the fraction is greater than a predetermined threshold, the predetermined time window is marked as a period the person is lying down.

Abstract: The present application provides a multi-dimensional light-emitting device electrically connected to a power supply system. The multi-dimensional light-emitting device comprises a substrate, a blue light-emitting diode array and one or more phosphor layers. The blue light-emitting diode array, disposed on the substrate, comprises a plurality of blue light-emitting diode chips which are electrically connected. The multi-dimensional light-emitting device comprises a central area and a plurality of peripheral areas, which are arranged around the central area. The phosphor layer covers the central area. When the power supply system provides a high voltage, the central area and the peripheral areas of the multi-dimensional light-emitting device provide a first light and a plurality of second lights, respectively. The first light and the second lights are blended into a mixed light.

Abstract: A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Abstract: A light-emitting device includes a growth substrate, a plurality of light-emitting diode units formed on the growth substrate and arranged in a closed loop, an electrode directly formed on the growth substrate, an electrical connection structure formed on the growth substrate and connecting the plurality of light-emitting diode units with the electrode, and a plurality of rectifying diodes connecting to respective nodes of the closed loop.

Abstract: A light-emitting device is provided that is capable of being directly connected to an alternative current source, including at least one electronic element; at least one light-emitting diode array chip; at least one bonding pad, a conductive trace, and a submount for supporting the electronic element, the light-emitting diode array chip, the bonding pad, and the conductive trace. The conductive trace is electrically connected to the electronic element, the light-emitting diode array chip, and bonding pad.

Abstract: The present application provides a multi-dimensional light-emitting device electrically connected to a power supply system. The multi-dimensional light-emitting device comprises a substrate, a blue light-emitting diode array and one or more phosphor layers. The blue light-emitting diode array, disposed on the substrate, comprises a plurality of blue light-emitting diode chips which are electrically connected. The multi-dimensional light-emitting device comprises a central area and a plurality of peripheral areas, which are arranged around the central area. The phosphor layer covers the central area. When the power supply system provides a high voltage, the central area and the peripheral areas of the multi-dimensional light-emitting device provide a first light and a plurality of second lights, respectively. The first light and the second lights are blended into a mixed light.

Abstract: A method and system for automated sleep staging are disclosed. The method comprises determining at least one physiological signal during a predetermined time period, extracting at least one feature from the at least one physiological signal, and classifying the at least one feature using a machine learning classifier to output at least one sleep stage. The system includes a sensor to determine at least one physiological signal during a predetermined time period, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to extract at least one feature from the at least one physiological signal and to classify the at least one feature using a machine learning classifier unit to output at least one sleep stage.

Abstract: The present application provides a multi-dimensional light-emitting device electrically connected to a power supply system. The multi-dimensional light-emitting device comprises a substrate, a blue light-emitting diode array and one or more phosphor layers. The blue light-emitting diode array, disposed on the substrate, comprises a plurality of blue light-emitting diode chips which are electrically connected. The multi-dimensional light-emitting device comprises a central area and a plurality of peripheral areas, which are arranged around the central area. The phosphor layer covers the central area. When the power supply system provides a high voltage, the central area and the peripheral areas of the multi-dimensional light-emitting device provide a first light and a plurality of second lights, respectively. The first light and the second lights are blended into a mixed light.

Abstract: A method and system for fall detection using machine learning are disclosed. The method comprises detecting at least one signal by a wireless sensor device and calculating a plurality of features from the at least one detected signal. The method includes training a machine learning unit of the wireless sensor device using the features to create a fall classification and a non-fall classification for the fall detection. The system includes a sensor to detect at least one signal, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to calculate a plurality of features from the at least one detected signal and to train a machine learning unit of the wireless sensor device using the features to create a fall classification and a non-fall classification for the fall detection.

Abstract: A method and system for measuring respiratory rate are disclosed. In a first aspect, the method comprises measuring at least one respiration signal and filtering the respiration signal using a lowpass filter. The method includes peak-picking the respiration signal to determine the respiratory rate and determining a quality metric of the respiratory rate. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

Abstract: A method and wireless sensor device for determining body postures and activities. In one aspect, a method includes receiving sensor data. The method also includes detecting and classifying a body transition of a body based on the sensor data. The method also includes detecting if there is activity of the body based on the sensor data. If there is activity, the method also includes classifying the activity. If there is no activity, the method also includes classifying a rest position of the body based on the sensor data and based on a previous body transition.