A rehabilitation evaluation apparatus includes a sensor signal acquisition unit configured to acquire a sensor signal output from a detection sensor, a selection unit configured to select at least one myoelectric signal having a correlation with the sensor signal acquired by the sensor signal acquisition unit from among the plurality of second myoelectric signals on the second-side part acquired by the myoelectric-signal acquisition unit, and a similarity output unit configured to select a first myoelectric signal that has been output from a myoelectric sensor attached in a place that is left-right symmetric to a place of the myoelectric sensor that has output the second correlated myoelectric signal selected by the selection unit from among a plurality of first myoelectric signals on the first-side part acquired by the myoelectric-signal acquisition unit, calculate a similarity between these correlated myoelectric signals, and outputs the calculated similarity.

A rehabilitation evaluation apparatus includes a sensor signal acquisition unit configured to acquire a sensor signal output from a detection sensor, a selection unit configured to select at least one myoelectric signal having a correlation with the sensor signal acquired by the sensor signal acquisition unit from among the plurality of second myoelectric signals on the second-side part acquired by the myoelectric-signal acquisition unit, and a similarity output unit configured to select a first myoelectric signal that has been output from a myoelectric sensor attached in a place that is left-right symmetric to a place of the myoelectric sensor that has output the second correlated myoelectric signal selected by the selection unit from among a plurality of first myoelectric signals on the first-side part acquired by the myoelectric-signal acquisition unit, calculate a similarity between these correlated myoelectric signals, and outputs the calculated similarity.

Systems and methods for gesture control are disclosed. In some embodiments, a system may include a plurality of electrode pairs, a motion sensor, a controller, and a classifier. The system may be configured to: enter a monitoring state in which the system is configured to receive data; receive a first set of data; determine that the first set of data does not satisfy one or more action criteria; return to the monitoring state without transmitting the first set of data to the classifier; receive a second set of data; determine that the second set of data satisfies the one or more action criteria; transmit the second set of data to the classifier; using the classifier, analyze the second set of data to generate an interpreted output indicating a gesture performed by the person; and based on the interpreted output, generate a machine instruction.

Systems and methods for gesture control are disclosed. In some embodiments, a system may include a plurality of electrode pairs, a motion sensor, a controller, and a classifier. The system may be configured to: enter a monitoring state in which the system is configured to receive data; receive a first set of data; determine that the first set of data does not satisfy one or more action criteria; return to the monitoring state without transmitting the first set of data to the classifier; receive a second set of data; determine that the second set of data satisfies the one or more action criteria; transmit the second set of data to the classifier; using the classifier, analyze the second set of data to generate an interpreted output indicating a gesture performed by the person; and based on the interpreted output, generate a machine instruction.

Methods, systems and computer program products provide for a Risk Assessment Engine that receives body ambient temperature data captured by a sensor in contact with a person. The Risk Assessment Engine characterizes types of activities performed by the person during a time range associated with the body ambient temperature data. The Risk Assessment Engine determines a risk classification individualized for the person based on respective workloads and the corresponding allocations of work and rest experienced by the person during performance of the characterized types of activities.

The present disclosure provides methods for diagnosing and determining the disease progression of neurodegenerative disorders in patients using neurophysiological biomarkers.

The present disclosure provides methods for diagnosing and determining the disease progression of neurodegenerative disorders in patients using neurophysiological biomarkers.

An electromyography processing apparatus 1 includes an electromyography acquiring unit 21 configured to generate electromyography data 11 indicating the time course of an electromyography acquired from an electrode 2 set on each of left and right muscles which are paired of an exerciser performing an exercise in which the left and right muscles are alternately used, and an evaluation unit 23 configured to calculate and output a switching index indicating that left and right muscles are alternately used, from an electromyography of the left muscle and an electromyography of the right muscle acquired at an identical time.

An electromyography processing apparatus 1 includes an electromyography acquiring unit 21 configured to generate electromyography data indicating the time course of an electromyography acquired from an electrode set on a predetermined muscle of an exerciser performing a repetitive exercise, and an evaluation unit 25 configured to calculate and output a reproducibility index indicating the reproducibility of the repetitive exercise from the reproducibility of a transition of the electromyography in the repetitive exercise.

Ventilation system (having a ventilator and having a diagnostic device, wherein the ventilator comprises a ventilation unit for generating a respiratory gas flow for ventilation and a detection unit (for detecting a ventilation signal characteristic for the respiratory gas flow over time. The diagnostic device comprises a sensor unit for detecting a diagnostic signal over time. The synchronization unit is operationally connected to the detection unit and the sensor unit and is suitable and configured for studying a time curve of the ventilation signal and a time curve of the diagnostic signal respectively for a signal change caused by the same event and bringing the curve of the ventilation signal and the curve of the diagnostic signal into chronological correspondence so that the event occurs simultaneously in both signal curves.