A body motion determination system (100) configured to determine whether or not a subject (S) on a bed (BD) has a body motion includes: a plurality of load detectors (11, 12, 13, 14) each configured to detect the load of the subject on the bed; a respiratory waveform obtaining unit (32) configured to obtain a respiratory waveform of the subject based on a temporal variation of the load of the subject detected by each of the plurality of load detectors; and a body motion determining unit (33) configured to determine whether or not the subject has the body motion based on a comparison between a first threshold value and a standard deviation of the temporal variations in the load of the subject detected by at least one of the plurality of load detectors. The body motion determining unit is configured to compensate the standard deviation to be used in the comparison by an amplitude of the respiratory waveform.

A method is provided for automatically determining the status of a wearable sensor device configured to be worn by a user. The wearable sensor device includes at least one accelerometer that generates device acceleration data indicating an acceleration of the sensor device. The device acceleration data is used for both (a) a sensor device attachment analysis and (b) a turn protocol compliance analysis. The sensor device attachment analysis includes comparing the device acceleration data to stored reference data, determining an attachment or position status of the sensor device with respect to the user based on the comparison, and generating a corresponding alert notification. The turn protocol compliance analysis includes monitoring an orientation of the user based on the device acceleration data, comparing the monitored user orientation to at least one turn parameter defined by a turn protocol, and generating a turn protocol alert notification based on the comparison.

The present invention provides a wearable sensor including a fiber; a self-assembled monolayer formed on at least one surface of the fiber and including a functional group; a carbon nanotube layer formed on the self-assembled monolayer by adsorbing a plurality of carbon nanotubes on the self-assembled monolayer; and an electrode electrically connected to the carbon nanotube layer.

There is provided a biological information monitoring system for monitoring biological information of a subject on a bed, the system comprising: detectors which detect load of the subject; a center of gravity position calculating unit which acquires temporal variation of a center of gravity position of the subject based on the detected load; a body motion information determining unit which acquires information on body motion of the subject based on the acquired temporal variation; and a respiratory rate calculating unit which calculates a respiratory rate of the subject based on the acquired temporal variation and the information on the body motion of the subject. The body motion information includes information on large and small body motions of the subject, and the body motion information determining unit includes first and second body motion information determining units which determine the information on the large and small body motions of the subject, respectively.

There is provided a biological information monitoring system for monitoring biological information of a subject on a bed, the system comprising: detectors which detect load of the subject; a center of gravity position calculating unit which acquires temporal variation of a center of gravity position of the subject based on the detected load; a body motion information determining unit which acquires information on body motion of the subject based on the acquired temporal variation; and a respiratory rate calculating unit which calculates a respiratory rate of the subject based on the acquired temporal variation and the information on the body motion of the subject. The body motion information includes information on large and small body motions of the subject, and the body motion information determining unit includes first and second body motion information determining units which determine the information on the large and small body motions of the subject, respectively.

A computer-implemented method for learning a model to predict movements of a person in bed is presented. The method includes receiving first data from a plurality of first sensors installed on a bed patient support apparatus, receiving second data from a plurality of second sensors installed on the person, and learning a model to predict the second data based on the first data by assuming a sensing range of motion intensity by the plurality of first sensors is greater than a sensing range of motion intensity by the plurality of second sensors.

A computer-implemented method for learning a model to predict movements of a person in bed is presented. The method includes receiving first data from a plurality of first sensors installed on a bed patient support apparatus, receiving second data from a plurality of second sensors installed on the person, and learning a model to predict the second data based on the first data by assuming a sensing range of motion intensity by the plurality of first sensors is greater than a sensing range of motion intensity by the plurality of second sensors.

Methods and systems for sensing a physiological characteristic of a subject. At least one receiver antenna can be provided in proximity to a portion of the subject's body to obtain at least one receiver signal resulting from at least one transmitter signal that has propagated to the receiver antenna and has been reflected, diffracted, scattered, or transmitted by or through the portion of the subject's body. One or more coherent signal pairs can be formed. Then, amplitude and phase information of a plurality of frequency components for each signal pair can be determined. A set of comparison values can be determined for each signal pair by comparing respective frequency component phases and respective frequency component amplitudes of the signals. Physiological characteristics of the subject can then be determined from these comparison values.

Methods and systems for sensing a physiological characteristic of a subject. At least one receiver antenna can be provided in proximity to a portion of the subject's body to obtain at least one receiver signal resulting from at least one transmitter signal that has propagated to the receiver antenna and has been reflected, diffracted, scattered, or transmitted by or through the portion of the subject's body. One or more coherent signal pairs can be formed. Then, amplitude and phase information of a plurality of frequency components for each signal pair can be determined. A set of comparison values can be determined for each signal pair by comparing respective frequency component phases and respective frequency component amplitudes of the signals. Physiological characteristics of the subject can then be determined from these comparison values.

A method for guiding deep breathing may include receiving a request from a user to initiate a deep breathing exercise on a user-controlled device. The method may include monitoring deep breathing using one or more sensors on an ear-worn device in response to initiating the deep breathing exercise. Examples of sensors include at least one of a motion detector, a microphone, a heart rate sensor, and an electrophysiological sensor. The method may further include initiating an end to the deep breathing exercise. The method may be used with various hearing systems including an ear-worn device and optionally a user-controllable device, such as a smartphone.