Abstract: A device and method for sleep monitoring, in particular to a device and method for determining time-to-sleep and wake periods during sleep and to a device and method for determining rapid eye movement (REM) sleep and non REM (NREM) sleep. The method for determining time-to-sleep and wake periods during sleep comprising the steps of obtaining motion data representative of motion of a user; detecting the time-to-sleep from the motion data based on a first time-above-threshold (TAT) threshold and a first proportional integration method (PIM) threshold; and detecting the wake periods during sleep from the motion data based on a second TAT threshold and a second PIM threshold.

Abstract: A method and related apparatus for deriving mean arterial pressure of a subject, including receiving data relating to at least one cardiac cycle of a bio-signal from the subject; normalizing the received data relating to the at least one cardiac cycle; calculating an area enclosed by the normalized received data to obtain a normalized area; calculating a heart rate of the subject from the at least one cardiac cycle; and deriving the mean arterial pressure from the normalized area and heart rate.

Abstract: A method (1500) for deriving a mental state of a subject is disclosed. The method comprises receiving (1502) a bio-signal from the subject; calculating respective statistical variations of at least two physiological parameters derived from the bio-signal; determining (1504) an arousal level of the subject based on the calculated statistical variations of the at least two physiological parameters; deriving a time-domain heart rate variability signal from the bio-signal for calculating at least two heart rate variability parameters; determining (1506) a valence level of the subject based on the at least two heart rate variability parameters; and deriving (1508) the mental state from the arousal level and valence level. A related apparatus is also disclosed.

Abstract: A method (1500) for deriving a mental state of a subject is disclosed. The method comprises receiving (1502) a bio-signal from the subject; calculating respective statistical variations of at least two physiological parameters derived from the bio-signal; determining (1504) an arousal level of the subject based on the calculated statistical variations of the at least two physiological parameters; deriving a time-domain heart rate variability signal from the bio-signal for calculating at least two heart rate variability parameters; determining (1506) a valence level of the subject based on the at least two heart rate variability parameters; and deriving (1508) the mental state from the arousal level and valence level. A related apparatus is also disclosed.

Abstract: A method and device for removal of artifacts in physiological measurements. The method comprising the steps of obtaining a physiological signal of a user; obtaining corresponding motion data representative of motion of the user; detecting two or more motion cycles in the motion data; constructing a noise reference based on segments of the physiological signal corresponding to the two or more motion cycles respectively, and filtering the physiological signal with the noise reference.

Abstract: Device and method for removing artifacts in physiological measurements. The method can comprise the steps of obtaining a physiological signal of a user; obtaining corresponding motion data representative of motion of the user; determining whether the physiological signal is distorted; and if the physiological signal is determined to be distorted, identifying a noise reference and filtering the physiological signal with the noise reference.

Abstract: Device and method for classifying the activity and/or counting steps of a user. A method for classifying the activity of a user can comprise measuring accelerometer data for a plurality of axes; identifying a most active one of the plurality of axes based on the accelerometer data; and classifying the activity of the user based on a signal amplitude of the accelerometer data for the most active axis and one or more threshold values.

Abstract: Device and method for classifying the activity and/or counting steps of a user. A method for classifying the activity of a user can comprise measuring accelerometer data for a plurality of axes; identifying a most active one of the plurality of axes based on the accelerometer data; and classifying the activity of the user based on a signal amplitude of the accelerometer data for the most active axis and one or more threshold values.

Abstract: A method of deriving mean arterial pressure of a subject is disclosed. The method comprises (i) receiving (202) data relating to at least one cardiac cycle of a bio-signal from the subject; (ii) normalizing the received data relating to the at least one cardiac cycle; (iii) calculating (206) an area enclosed by the normalized received data to obtain a normalized area; (iv) calculating (208) a heart rate of the subject from the at least one cardiac cycle; and (v) deriving (210) the mean arterial pressure from the normalized area and heart rate. A related apparatus is also disclosed.

Abstract: A method and device for removal of artifacts in physiological measurements. The method comprising the steps of obtaining a physiological signal of a user; obtaining corresponding motion data representative of motion of the user; detecting two or more motion cycles in the motion data; constructing a noise reference based on segments of the physiological signal corresponding to the two or more motion cycles respectively, and filtering the physiological signal with the noise reference.

Abstract: A device and method for sleep monitoring, in particular to a device and method for determining time-to-sleep and wake periods during sleep and to a device and method for determining rapid eye movement (REM) sleep and non REM (NREM) sleep. The method for determining time-obtaining motion data representative of motion of a user to-sleep and wake periods during sleep comprising the steps of obtaining motion data representative of motion of a user; detecting the time-to-sleep from the motion data based on a first time-above-threshold (TAT) threshold and a first proportional detecting the time-to-sleep from the motion data based integration method (PIM) threshold; and detecting on a first time-above-threshold (TAT) threshold and a the wake periods during sleep from the motion data first proportional integration method (PIM) threshold based on a second TAT threshold and a second PIM threshold.

Abstract: A method (1500) for deriving a mental state of a subject is disclosed. The method comprises receiving (1502) a bio-signal from the subject; calculating respective statistical variations of at least two physiological parameters derived from the bio-signal; determining (1504) an arousal level of the subject based on the calculated statistical variations of the at least two physiological parameters; deriving a time-domain heart rate variability signal from the bio-signal for calculating at least two heart rate variability parameters; determining (1506) a valence level of the subject based on the at least two heart rate variability parameters; and deriving (1508) the mental state from the arousal level and valence level. A related apparatus is also disclosed.

Abstract: Device and method for classifying the activity and/or counting steps of a user. A method for classifying the activity of a user can comprise measuring accelerometer data for a plurality of axes; identifying a most active one of the plurality of axes based on the accelerometer data; and classifying the activity of the user based on a signal amplitude of the accelerometer data for the most active axis and one or more threshold values.

Abstract: A device and method for removal of ambient noise signal from a photoplethysmographic measurement is provided. The method comprises obtaining a first signal waveform based on detecting light based on a first light illumination; obtaining a second signal waveform based on detecting light based on a second light illumination; tuning the first light and second light illumination such that the maximum amplitudes of the first and second signal waveforms are maximized and within a predetermined saturation range, such that ambient light interference for the first and second signal waveforms is reduced; obtaining a third signal waveform based on detecting ambient light; obtaining respective maximum and minimum values of the first and the second signal waveforms; and deriving signal values of the first and second signal waveforms with the removal of ambient noise by subtracting AC and DC average values of the third signal waveform from the first and second signals.

Abstract: A method 100 and apparatus for determining SpO2, of a subject from an optical measurement is disclosed herein. In a described embodiment, the method 100 includes obtaining a PPG(red) signal and a PPG(IR) signal at steps 102 and 104, and pairing the PPG(red) and PPG(IR) signals at step 106 in which an amplitude of each cardiac rhythm cycle of the first signal is aligned to an amplitude of a respective cardiac rhythm cycle of the second signal to form a plurality of paired windows. At step 108, the method further includes, for each paired window, calculating values of a ratio R from the paired first and second signals and based on the calculated R values, binning the calculated R values into predetermined frequency bins at step 110. At steps 112 and 1 14, at least one of the frequency bins is selected to derive a revised ratio, Rrev and at step 1 16, Sp02 is derived from the revised ratio Rrev. A zoning schema for deriving SpO2 is also disclosed.

Abstract: Method 100 and device for quantifying heart rate variability coherence of a subject is disclosed herein. The method 100 comprises obtaining a bio-signal (such as a PPG signal) from the subject at 102 and deriving a time-domain heart rate variability signal from the bio-signal at 108. Further, at 112, the method 100 further includes correlating the time-domain heart rate variability signal with a sine wave representing a time domain reference heart rate variability signal to obtain a correlated heart rate variability signal, and this includes adjusting frequency of the sine wave and performing cross-correlation between the sine wave at each of the adjusted frequencies and the heart rate variability signal to obtain the correlated heart rate variability signal. Further, at 116, the method includes quantifying the heart rate variability coherence based on the correlated heart rate variability signal.

Abstract: A device and method for removal of ambient noise signal from a photoplethysmographic measurement is provided. The method comprises obtaining a first signal waveform based on detecting light based on a first light illumination; obtaining a second signal waveform based on detecting light based on a second light illumination; tuning the first light and second light illumination such that the maximum amplitudes of the first and second signal waveforms are maximised and within a predetermined saturation range, such that ambient light interference for the first and second signal waveforms is reduced; obtaining a third signal waveform based on detecting ambient light; obtaining respective maximum and minimum values of the first and the second signal waveforms; and deriving signal values of the first and second signal waveforms with the removal of ambient noise by subtracting AC and DC average values of the third signal waveform from the first and second signals.