Abstract: A processing device includes: a first processing unit that refers to function data including a labeling function that labels an input dataset or abstain if it cannot label, to output first output data that associates each input dataset with a probability of corresponding to each label from a result of labeling the input dataset by the labeling function; and an identifying unit that identifies an input dataset in which a variation in the probability of corresponding to each label in the first output data satisfies a predetermined condition among the input datasets. A labeling function newly created for the input dataset identified from the first output data by the identifying unit is inserted into the function data.

Abstract: A learning method includes a step in which a device acquires a plurality of samples, a step in which the device divides the plurality of samples into a plurality of clusters, a step in which the device extracts samples from each of the plurality of clusters according to an effectiveness of each cluster received from a learning apparatus, a step in which the device transmits the extracted samples to the learning apparatus, a step in which the learning apparatus learns the extracted samples, a step in which the learning apparatus calculates, for each cluster, an effectiveness in learning the samples belonging to a cluster from learning results, and a step in which the learning apparatus transmits the effectiveness of each cluster to the device.

Abstract: 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.

Abstract: 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.

Abstract: An electromyography processing apparatus 1 includes a storage device 10 that stores electromyography data 11 of a predetermined muscle, an onset detection unit 23 that detects an onset section of the electromyography data 11 in which an electromyography increases by a repetitive exercise performed by an athlete, and an ON/OFF indicator processing unit 25 that calculates a variance a probability distribution acquired by normalizing a root-mean-square value of the electromyography for each onset section, and outputs the calculated variance as an ON/OFF indicator for each onset section.

Abstract: An electromyography processing apparatus 1 includes a storage device 10 that stores electromyography data 11 of a predetermined muscle, and a balance indicator processing unit 28 that calculates a root-mean-square value of the electromyography for each predetermined time, repeats a process for each of pieces of electromyography data of each of a plurality of muscles, the process being of outputting, as a balance evaluation value of the predetermined muscle, a value acquired by subjecting, to time differentiation, the time course of an average value of the root-mean-square value in a sliding window for calculating balance evaluation value, and outputs a balance indicator based on a balance evaluation value of each of the plurality of muscles.

Abstract: An analysis device according to the present invention includes a memory, and processing circuitry coupled to the memory and configured to analyze data indicating a biosignal by using a predetermined analysis technique and an updated parameter corresponding to the predetermined analysis technique every time the parameter is updated, control a display to display an analysis result obtained together with an interface capable of changing display modes in response to user's operation, and update the parameter based on change in the display modes for the interface.

Abstract: An electromyography processing apparatus 1 includes a storage device 10 that stores electromyography data 11 of a predetermined muscle, a pre-processing unit 22 that calculates a root-mean-square value in the electromyography data 11 for each predetermined time and generates root-mean-square value data including the root-mean-square value for each time, and an onset detection unit 23 that sets a sliding window for onset detection to a predetermined time of the root-mean-square value data and determines that a predetermined time is an onset portion if the average of the root-mean-square values in the sliding window is higher than a threshold value, and the onset detection unit 23 further includes a threshold value determination unit 24 that sets a sliding window for threshold value detection to the predetermined time and determines a threshold value based on the average of the root-mean-square values in the sliding window for threshold value detection.

Abstract: An analysis device according to the present invention includes a memory, and processing circuitry coupled to the memory and configured to analyze data indicating a biosignal by using a predetermined analysis technique and an updated parameter corresponding to the predetermined analysis technique every time the parameter is updated, control a display to display an analysis result obtained together with an interface capable of changing display modes in response to user's operation, and update the parameter based on change in the display modes for the interface.

Abstract: A method for a data analysis system includes measuring, by a sensor terminal of the data analysis system, sensor data; receiving, by a teacher data input terminal of the data analysis system, teacher data input into the teacher data input terminal; and generating, a server by the data analysis system, a classifier according to learning through the sensor data and the teacher data. The sensor terminal transmits the sensor data to the server, receives the classifier generated by the server, analyzes the sensor data according to the classifier, and transmits an analysis result of the analyzing the sensor data to the server. The teacher data input terminal transmits the teacher data to the server. The server generates the classifier, analyzes the sensor data according to the classifier, transmits the classifier to the sensor terminal, and receives the analysis result from the sensor terminal.