A reliable technology for determining the masticatory side of the user is provided. First and second electromyographic waveforms respectively originating from left and right muscles related to masticatory actions of a user are acquired; a coefficient of correlation between pieces of information respectively extracted from the first and the second electromyographic waveforms is calculated as a first feature value; a second feature value is calculated from a power spectrum obtained by performing frequency analysis on the first electromyographic waveform; a third feature value is calculated from a power spectrum obtained by performing frequency analysis on the second electromyographic waveform; a learning model is generated by associating the first, second, and third feature values with a plurality of labels; and the masticatory side of the user is determined based on first, second, and third feature values calculated from a newly acquired electromyographic waveform and the learning model.

A system and method for determining parameters of stimulation electrical signals for vagus nerve stimulation is discussed. Initial parameters of the signals are selected to provide reliable response to stimulation in physiological measurements of a subject. One or more physiological and neurological indices are determined based on a vagus nerve response model. For a selected vagus nerve activation, the electrical parameters of the signals are varied while monitoring changes in physiological parameters and values of the indices. The electrical parameters are varied until desired response in the physiological measurements and the values of the indices is observed. The electrical parameters are then stored as preferred parameters and can be used to activate the selected vagus nerve of the subject.

A method of treating spasticity uses a garment worn on a target anatomy so as to arrange electrodes on an inner surface of the garment contacting the skin of the target anatomy. Using an electronic processor, a spasticity treatment cycle is performed. The spasticity treatment cycle is initiated by providing a human-perceptible prompt to initiate a spastic event, or by triggering the spastic event by applying electrical stimulation to at least a portion of the target anatomy using the electrodes. Thereafter, electromyography (EMG) signals are measured from the target anatomy using the electrodes. One or more spasm regions in the target anatomy are identified based on the EMG signals. Targeted treatment of the one or more spasm regions is performed using neuromuscular electrical stimulation (NMES), or is directed to be performed by displaying a representation of the target anatomy with the one or more spasm regions indicated on the representation.

An exercise rating and improvement system comprising: a feature amount extraction unit for extracting, from information obtained from a subject, a feature amount about exercise efficiency; an rating unit for rating exercise efficiency of the subject by comparing the extracted feature amount and a feature amount to be compared; and an improvement information providing unit for providing, on the basis of the rating result, information such that the feature amount of the subject approaches the feature amount to be compared.

A therapeutic or diagnostic device comprises a wearable electrodes garment including electrodes disposed to contact skin when the wearable electrodes garment is worn, and an electronic controller operatively connected with the electrodes. The electronic controller is programmed to perform a method including: receiving surface electromyography (EMG) signals via the electrodes and extracting one or more motor unit (MU) action potentials from the surface EMG signals. The method may further include identifying an intended movement based at least on features representing the one or more extracted MU action potentials and delivering functional electrical stimulation (FES) effective to implement the intended movement via the electrodes of the wearable electrodes garment. The method may further include generating a patient performance report based at least on a comparison of features representing the one or more extracted MU action potentials and features representing expected and/or baseline MU action potentials for a known intended movement.

A therapeutic or diagnostic device comprises a wearable electrodes garment including electrodes disposed to contact skin when the wearable electrodes garment is worn, and an electronic controller operatively connected with the electrodes. The electronic controller is programmed to perform a method including: receiving surface electromyography (EMG) signals via the electrodes and extracting one or more motor unit (MU) action potentials from the surface EMG signals. The method may further include identifying an intended movement based at least on features representing the one or more extracted MU action potentials and delivering functional electrical stimulation (FES) effective to implement the intended movement via the electrodes of the wearable electrodes garment. The method may further include generating a patient performance report based at least on a comparison of features representing the one or more extracted MU action potentials and features representing expected and/or baseline MU action potentials for a known intended movement.

A step of extracting components of one or more frequency bands from a first section of an EEG; a step of calculating a first index for each of the components of one or more frequency bands, wherein the first index is calculated based on a degree to which a magnitude of each of the components of one or more frequency bands with respect to a magnitude of a predetermined reference component in the first section exceeds a predetermined threshold value; a step of calculating a probability value for each of one or more patient statuses from the first index for each of the components of one or more frequency bands using a trained artificial neural network; and a step of determining the consciousness level of the patient based on the probability value for each of the one or more calculated patient statuses.

A step of extracting components of one or more frequency bands from a first section of an EEG; a step of calculating a first index for each of the components of one or more frequency bands, wherein the first index is calculated based on a degree to which a magnitude of each of the components of one or more frequency bands with respect to a magnitude of a predetermined reference component in the first section exceeds a predetermined threshold value; a step of calculating a probability value for each of one or more patient statuses from the first index for each of the components of one or more frequency bands using a trained artificial neural network; and a step of determining the consciousness level of the patient based on the probability value for each of the one or more calculated patient statuses.

A method for processing physiological signals (S.sub.EMG; S.sub.EEG) acquired from a subject (S) allows the detection of an imminent loss of balance of the subject and the generation of a signal (Aout) indicating the imminent loss of balance. The method comprises: the reception of a plurality of electromyographic signals (S.sub.EMG) representative of a detected muscle activity of a plurality of selected muscles of the subject, as well as a plurality of brain signals (S.sub.EEG) acquired by means of an electroencephalogram and representative of a cortical activity of the subject during said muscle activity; the analysis and processing of the electromyographic signals (S.sub.EMG) in order to extract a muscle activity pattern, MAP, and generate an indicator of normality/abnormality of the detected muscle activity pattern; the analysis and processing of the brain signals (S.sub.EEG) in order to generate one or more cortical response indicators of the subject upon occurrence of said detected muscle activity (I.sub.EGg; LF(k)); and a classification step, wherein at least one indicator (MA(k)) of normality/abnormality of the MAP and one or more of said cortical response indicators are correlated to generate a signal (Aout) indicating an imminent loss of balance.

A method for processing physiological signals (S.sub.EMG; S.sub.EEG) acquired from a subject (S) allows the detection of an imminent loss of balance of the subject and the generation of a signal (Aout) indicating the imminent loss of balance. The method comprises: the reception of a plurality of electromyographic signals (S.sub.EMG) representative of a detected muscle activity of a plurality of selected muscles of the subject, as well as a plurality of brain signals (S.sub.EEG) acquired by means of an electroencephalogram and representative of a cortical activity of the subject during said muscle activity; the analysis and processing of the electromyographic signals (S.sub.EMG) in order to extract a muscle activity pattern, MAP, and generate an indicator of normality/abnormality of the detected muscle activity pattern; the analysis and processing of the brain signals (S.sub.EEG) in order to generate one or more cortical response indicators of the subject upon occurrence of said detected muscle activity (I.sub.EGg; LF(k)); and a classification step, wherein at least one indicator (MA(k)) of normality/abnormality of the MAP and one or more of said cortical response indicators are correlated to generate a signal (Aout) indicating an imminent loss of balance.