Measuring muscle load in athletic activities, and associated systems and methods are described herein. In an embodiment, a method for monitoring muscle load of an athlete includes: determining a muscle effort (ME) of the athlete by a wearable electromyography (EMG) sensor, and determining at least one inertial measurement unit (IMU) output of the athlete. The method further includes comparing the ME and the IMU output of the athlete, and, based on comparing, determining a performance of the athlete.

Measuring muscle load in athletic activities, and associated systems and methods are described herein. In an embodiment, a method for monitoring muscle load of an athlete includes: determining a muscle effort (ME) of the athlete by a wearable electromyography (EMG) sensor, and determining at least one inertial measurement unit (IMU) output of the athlete. The method further includes comparing the ME and the IMU output of the athlete, and, based on comparing, determining a performance of the athlete.

The present technology relates to a control device and a control method capable of providing a more convenient electroencephalogram input user interface.

Provided is a control device including a detection unit configured to perform detection of a brain wave included in a measured biometric signal of a user and detection of a user action based on information other than the brain wave included in the biometric signal, and a processing unit configured to perform a predetermined process based on the brain wave in a case where the user action is a predetermined action. For example, the present technology can be applied to a measurement device capable of measuring a brain wave signal.

A spinal cord stimulator (SCS) system and method for placing SCS electrodes in a patient for spinal cord stimulation therapy. The SCS system includes a stimulator and a base unit. In conjunction with a machine learning (ML) block, the base unit includes an algorithm module to store and process algorithms for processing data received from recording electrodes placed in a patient's body. The recording electrodes send electromyography (EMG) data to the algorithm module. The algorithm module processes and sends the EMG data to a display device. The displayed data is used, by a surgeon, for lateralization of the SCS electrode. The SCS system further includes algorithms to adjust stimulation parameters related to SCS electrodes based upon the surgeon's workflow. Further, the SCS system allows manual modification of stimulation parameters based upon muscle responses and the EMG data from the recording electrodes.

Electrodes that can be formed in a flexible band of a wrist-worn device to detect hand gestures are disclosed. Multiple rows of electrodes can be configured to detect electromyography (EMG) signals produced by activity of muscles and tendons. The band can include removable electrical connections (e.g., pogo pins) to enable the electrode signals to be routed to processing circuitry in the housing of the wrist-worn device. Measurements between signals from the active electrodes and one or more reference electrodes can be obtained to capture EMG signals at a number of locations on the band. The measurement method and mode of operation (lower power coarse detection or higher power fine detection) can determine the location and number of electrodes to be measured. These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements.

A system for monitoring muscle load, is configured to perform operations including: obtaining, from at least one sensor, measurement data on an exerciser; determining, based on the measurement data, a number of repetitions of a macroscopic movement performed during a physical exercise; determining, based on a conversion entry corresponding to a type of the physical exercise, muscle load coefficient of one or more muscles loaded in the physical exercise; utilizing the muscle load coefficient of the one or more muscles and the number of repetitions in determining muscle load data indicating muscle specific muscle load caused by the physical exercise performed by the exerciser; and outputting the muscle load data.

The embodiment of the present disclosure provides a method and a system for identifying a user action. The method and system may obtain user action data collected from a plurality of measurement positions on a user, the user action data corresponding to an unknown user action, identify that the user action includes a target action when obtaining the user action data based on at least one set of target reference action data, the at least one set of target reference action data corresponding to the target action, and send information related to the target action to the user.

In a method of neurological assessment, multichannel electromyography (EMG) data are acquired for an anatomical region. A pairwise EMG channel-EMG channel similarity matrix is generated from the acquired multichannel EMG data. Network analysis is performed on the similarity matrix to generate a network representing the similarity matrix. One or more metrics of the network are computed. One or more biomarkers are determined for the anatomical region based on the one or more metrics. In another method, EMG data are acquired using an electrode array contacting skin of a target anatomy, the EMG data are processed to produce reduced-dimensionality data; and time-invariant muscle synergies and corresponding time-varying activation functions are determined in the reduced-dimensionality data.

A method for controlling a limb of a virtual avatar by the myoelectric activities of a limb of an individual. The method includes a first step of calibrating and second step of moving the limb of the virtual avatar. Also, a system suitable for implementing the method for controlling a limb of a virtual avatar by the myoelectric activities of a limb of an individual.

A method for controlling a limb of a virtual avatar by the myoelectric activities of a limb of an individual. The method includes a first step of calibrating and second step of moving the limb of the virtual avatar. Also, a system suitable for implementing the method for controlling a limb of a virtual avatar by the myoelectric activities of a limb of an individual.