The disclosed system for interacting with objects in an extended reality (XR) environment generated by an XR system may include (1) neuromuscular sensors configured to sense neuromuscular signals from a wrist of a user and (2) at least one computer processor programmed to (a) determine, based at least in part on the sensed neuromuscular signals, information relating to an interaction of the user with an object in the XR environment and (b) instruct the XR system to, based on the determined information relating to the interaction of the user with the object, augment the interaction of the user with the object in the XR environment. Other embodiments of this aspect include corresponding apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Systems and methods for detecting athletic performance and fatigue are disclosed herein. In one embodiment, a method for monitoring athletic performance of an athlete includes: monitoring a heart rate (HR) of the athlete by a wearable electrocardiogram (ECG) sensor carried by the athlete; determining a rate of change of the HR over a period of time; comparing the rate of change to a predetermined threshold; and based on the comparing, determining whether the athlete is fatigued.

A system for ankle rehabilitation includes a motorized platform arranged to hold an ankle of a subject to be rehabilitated; a first sensor module arranged to detect signals representing movement intention of the ankle on the motorized platform; a second sensor module arranged to detect signals representing actual movement of the ankle on the motorized platform; and a processor arranged to process the signals detected by the first sensor module and the signals detected by the second sensor module, for control of movement of the motorized platform.

A system for ankle rehabilitation includes a motorized platform arranged to hold an ankle of a subject to be rehabilitated; a first sensor module arranged to detect signals representing movement intention of the ankle on the motorized platform; a second sensor module arranged to detect signals representing actual movement of the ankle on the motorized platform; and a processor arranged to process the signals detected by the first sensor module and the signals detected by the second sensor module, for control of movement of the motorized platform.

A muscle anti-clenching training device comprising a flexible device body conformable in a range of configurations upon a user’s body, and including: a skin-contact side and a top cover side; electrical contacts mounted in the flexible device body and exposed upon the skin-contact side; an adhesive upon the skin-contact side; an alert device mounted in the flexible device body; sensing circuitry electrically connected to the electrical contacts and structured to monitor an electrical property in a user’s body; and an electronic controller coupled to the sensing circuitry and structured to activate the alert device to produce an alert signal where the monitored electrical property is indicative of a muscular contraction.

A method for generating a filtered EMG signal includes obtaining a combined signal, wherein the combined signal comprises an ECG signal and an EMG signal. A first high pass filter is applied to the combined signal and an ECG model signal is generated, based on the high pass filtered combined signal. The method further includes, generating a partially filtered EMG signal by subtracting the ECG model from the high pass filtered combined signal. A second high pass filter is then applied to the partially filtered EMG signal to generate a second EMG signal and to the ECG model signal to generate a second ECG model signal. A filtered EMG signal is generated based on the second EMG signal and the second ECG model by way of a gating technique.

A method for generating a filtered EMG signal includes obtaining a combined signal, wherein the combined signal comprises an ECG signal and an EMG signal. A first high pass filter is applied to the combined signal and an ECG model signal is generated, based on the high pass filtered combined signal. The method further includes, generating a partially filtered EMG signal by subtracting the ECG model from the high pass filtered combined signal. A second high pass filter is then applied to the partially filtered EMG signal to generate a second EMG signal and to the ECG model signal to generate a second ECG model signal. A filtered EMG signal is generated based on the second EMG signal and the second ECG model by way of a gating technique.

A computer implemented method and system is provided for managing neural stimulation therapy. The method comprises under control of one or more processors configured with program instructions. The method delivers a series of candidate stimulation waveforms having varied stimulation intensities to at least one electrode located proximate to nervous tissue of interest. A parameter defines the candidate stimulation waveforms is changed to vary the stimulation intensity. The method identifies a first candidate stimulation waveform that induces a paresthesia-abatement effect, while continuing to induce a select analgesic effect. The method further identifies a second candidate stimulation waveform that does not induce the select analgesic effect. The method sets a stimulation therapy based on the first and second candidate stimulation waveforms.

A technology is described for determining an intended movement from neuromuscular signals. An example method (800) includes receiving electromyography (EMG) data corresponding to single-ended channels of an electrode array (810), where EMG signals are detected by electrodes comprising the single-ended channels of the electrode array and the EMG signals are converted to the EMG data. Determining differential channel pairs for the single-ended channels of the electrode array (820) and extracting feature data from the EMG data of the differential channel pairs (830). Thereafter a feature data set is selected from the feature data of the differential channel pairs (840) and the feature data set is input to a decode model configured to correlate the feature data set to an intended movement (850). Decode output is received from the decode model indicating the intended movement (860) and the decode output is provided to a device (870).

A technology is described for determining an intended movement from neuromuscular signals. An example method (800) includes receiving electromyography (EMG) data corresponding to single-ended channels of an electrode array (810), where EMG signals are detected by electrodes comprising the single-ended channels of the electrode array and the EMG signals are converted to the EMG data. Determining differential channel pairs for the single-ended channels of the electrode array (820) and extracting feature data from the EMG data of the differential channel pairs (830). Thereafter a feature data set is selected from the feature data of the differential channel pairs (840) and the feature data set is input to a decode model configured to correlate the feature data set to an intended movement (850). Decode output is received from the decode model indicating the intended movement (860) and the decode output is provided to a device (870).