Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

According to one embodiment, a sensor includes an element part, and a control circuit part. The element part includes first and second elements. Each of the first and second elements includes a first magnetic element and a first conductive member. The control circuit part includes a first current circuit, a differential circuit, and a phase detection circuit. The first current circuit is configured to supply a first current to the first conductive member. The differential circuit is configured to output a differential signal corresponding to a difference of a first signal and a second signal. The first signal corresponds to a change in a first electrical resistance of the first magnetic element of the first element. The second signal corresponds to a change in a second electrical resistance of the first magnetic element of the second element. The phase detection circuit is configured to perform a phase detection of the differential signal.

A device (1) for monitoring a response of a subject body (2, 21, 211) comprises an emitter (3) for emitting an input signal (5, 51, . . . ) and a receiver (4) for receiving an output signal (6, 61, . . . ). A first response (R1) of the subject body (2, 21, 211) is evaluated from the comparison between the signals. A further emitter (31, 311, . . . ) evaluates a second response (R2), wherein one of the responses is selected for a further monitoring of the response, and/or at least one further receiver (41, 411, . . . ) evaluates a third response (R3), wherein either the first response (R1) or the third response (R3) is selected for a further monitoring of the response, and/or wherein the input signal (5, 51, . . . ) is an electromagnetic field and the device (1) further comprises a signal modulator (9) which alters the input signal (5, 51, . . . ).

A device (1) for monitoring a response of a subject body (2, 21, 211) comprises an emitter (3) for emitting an input signal (5, 51, . . . ) and a receiver (4) for receiving an output signal (6, 61, . . . ). A first response (R1) of the subject body (2, 21, 211) is evaluated from the comparison between the signals. A further emitter (31, 311, . . . ) evaluates a second response (R2), wherein one of the responses is selected for a further monitoring of the response, and/or at least one further receiver (41, 411, . . . ) evaluates a third response (R3), wherein either the first response (R1) or the third response (R3) is selected for a further monitoring of the response, and/or wherein the input signal (5, 51, . . . ) is an electromagnetic field and the device (1) further comprises a signal modulator (9) which alters the input signal (5, 51, . . . ).

An exemplary wearable sensor unit includes 1) a magnetometer comprising a vapor cell comprising an input window and containing an alkali metal, and a light source configured to output light that passes through the input window and into the vapor cell along a transit path, and 2) a temperature control circuit external to the vapor cell and configured to create a temperature gradient within the vapor cell, the temperature gradient configured to concentrate the alkali metal within the vapor cell away from the transit path of the light.

An exemplary wearable sensor unit includes 1) a magnetometer comprising a vapor cell comprising an input window and containing an alkali metal, and a light source configured to output light that passes through the input window and into the vapor cell along a transit path, and 2) a temperature control circuit external to the vapor cell and configured to create a temperature gradient within the vapor cell, the temperature gradient configured to concentrate the alkali metal within the vapor cell away from the transit path of the light.

A millimeter wave radar apparatus determining a vital sign includes a microprocessor, a millimeter wave radar and an electrocardiogram machine. The millimeter wave radar is configured to detect a human body to obtain a plurality of wireless vital-sign signals. The microprocessor is configured to receive the wireless vital-sign signals. The electrocardiogram machine is configured to detect the human body to obtain a plurality of wired vital-sign signals. The microprocessor is configured to receive the wired vital-sign signals. After the microprocessor receives the wireless vital-sign signals and the wired vital-sign signals, the microprocessor is configured to output the wireless vital-sign signals and the wired vital-sign signals.

A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.