A patient support apparatus comprises a plurality of load cells, a frame supported on the load cells, a mattress, a plurality of air pressure sensors, and a control system. The mattress includes a plurality of inflatable zones positioned on the frame, the mattress and frame cooperating to direct any patient load through the mattress and frame to the load cells. Each of the plurality of air pressure sensors measures the pressure in a respective inflatable zone of the mattress. The control system includes a controller operable to receive a separate signal from each of the plurality of load cells and each of the plurality of air pressure sensors and process the signals to identify motion of the patient. A motion classifier assesses major and minor motions based on the amplitude and frequency of patient movement and determines a mobility score for the patient.

An information processing apparatus acquires projection data obtained by dividing a subject into first and second divided areas and capturing the first and second divided areas, the projection data including first projection data obtained by capturing a dynamic state of the subject in a first capturing range including the first divided area and second projection data obtained by capturing the dynamic state of the subject in a second capturing range including the second divided area. The apparatus acquires similarity relating to the dynamic state of the subject on a basis of projection data of a first partial area and projection data of a second partial area, and acquires a first timing for reconstructing an image of the first divided area and a second timing for reconstructing an image of the second divided area, on a basis of the similarity.

System and method for capturing a movement sequence of a person. The method comprises capturing a plurality of images of the person executing a movement sequence by means of a contactless sensor, the plurality of images representing the movements of the body elements of the person, generating at least one skeleton model having limb positions for at least some of the plurality of images, and calculating the movement pattern from the movements of the body elements of the person by comparing changes in the limb positions in the at least one skeleton model generated. In addition, vital signs and/or signal processing parameters of the person can be acquired and evaluated.

A method for assessing movement of a body portion includes, via one or more machine learning models, analyzing a sensor signal indicative of movement of the body portion to determine a movement of the body portion; determining a sensor confidence level based, at least in part, on a characteristic of the sensor signal; receiving a series of images indicative of movement of the body portion; measuring an angle of movement of the body portion; determining a vision confidence level based, at least in part, on a quality of an identification the body portion; selecting the sensor signal, the measured angle of movement, or a combination thereof as an input into a machine learning model based on the sensor confidence level and the vision confidence level, respectively; analyzing the input to determine a movement pattern of the body portion; and outputting the movement pattern to a user.

A signal representing heartbeat behavior is accurately restored. The present signal restoration system includes: a signal acquirer configured to acquire a first heartbeat signal representing heartbeat behavior; a first band-pass filter configured to generate a first signal by performing first band-pass filter processing on the first heartbeat signal; an integral calculator configured to calculate an integral value by integrating frequency intensity of the heartbeat represented by the first signal; a second band-pass filter configured to generate a third signal by performing second band-pass filter processing on a second signal representing the integral value with respect to time; and a restored signal generator configured to generate a restored signal representing heartbeat behavior based on first data generated by dividing the third signal at intervals of a predetermined time.

A method for adapting, per cardiac cycle, the parameters governing interpolation of varying and non-interpolation of fixed fractions of each individual cardiac cycle is provided. A time series of data values associated with a cardiac cycle is received, and the time series is scaled to a reference cardiac cycle, wherein the scaling includes applying a model to the time series to generate a scaled time series of data values associated with the first cardiac cycle. The model is trained using the scaled time series.

Disclosed is an exercise posture correction assistance method using working muscle information depending on motion performed by at least one server, including acquiring first image information containing movement of a user corresponding to a specific practice motion from a user terminal, deriving skeleton information corresponding to the movement of the user based on the first image information, generating second image information by indicating the movement of the user using a skeleton shape based on the first image information and the skeleton information, deriving muscle information corresponding to the specific practice motion from a database, generating muscle movement image information corresponding to the movement of the user based on the skeleton information and the muscle information, and providing the second image information and the muscle movement image information to the user terminal.

Various embodiments concern video patient monitoring with detection zones. Various embodiments can comprise a camera, a user interface, and a computing system. The computing system can be configured to perform various steps based on reception of a frame from the camera, including: calculate a background luminance of the frame; monitor for a luminance change of a zone as compared to one or more previous frames, the luminance change indicative of patient motion in the zone; and compare the background luminance to an aggregate background luminance, the aggregate background luminance based on the plurality of frames. If the background luminance changed by more than a predetermined amount, then the aggregate background luminance can be set to the background luminance, luminance information of the previous frames can be disregarded, and motion detection can be disregarded.

Aspects relate to an energy harvesting device adapted for use by an athlete while exercising. The device may utilize a mass of phase-change material to store heat energy, the stored heat energy subsequently converted into electrical energy by one or more thermoelectric generator modules. The energy harvesting device may be integrated into an item of clothing, and such that the mass of phase change material may store heat energy as the item of clothing is laundered.

A multimodal, contactless vital sign monitoring system is configured to perform the following operations. Images are received from a video capture device. An image of a subject is identified within the images. The image of the subject is segmented into a plurality of segments. A first analysis is performed on the plurality of segments to identify a color feature. A second analysis is performed of the plurality of segments to identify a motion feature. Using a combination of the color feature and the motion feature a plurality of vital signs for the subject are determined. The first analyzing and the second analyzing are performed in parallel. The plurality of vital signs include one or more of heart rate, respiration rate, oxygen saturation, heart rate variability, and atrial fibrillation.