Abstract: Systems, devices, and methods adapt established concepts from natural language processing for use in gesture identification algorithms. A gesture identification system includes sensors, a processor, and a non-transitory processor-readable memory that stores data and/or instructions for performing gesture identification. A gesture identification system may include a wearable gesture identification device. The gesture identification process involves segmenting signals from the sensors into data windows, assigning a respective “window class” to each data window, and identifying a user-performed gesture based on the corresponding sequence of window classes. Each window class exclusively characterizes at least one data window property and is analogous to a “letter” of an alphabet. Under this model, each gesture is analogous to a “word” made up of a particular combination of window classes.

Abstract: Systems, devices, and methods for laser eye tracking are described. Laser eye tracking involves scanning laser light over the eye and detecting diffuse reflections of the laser light with one or more photodetector(s). While conventional camera-based eye tracking techniques rely on detecting and identifying specific reflections (i.e., Purkinje images such as the “glint”), the laser eye tracking techniques described herein detect and identify a reduction in reflection intensity due to transmission of laser light through the pupil and/or increased diffusivity of reflections from the cornea relative to reflections from the sclera. This effect is referred to herein as the “corneal shadow” effect. Laser eye tracking uses considerably less power than conventional camera-based eye tracking techniques.

Abstract: Systems, articles, and methods for performing gesture identification with improved robustness against variations in use parameters and without requiring a user to undergo an extensive training procedure are described. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable storage medium that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects, determines, and ranks features in the signal data provided by the EMG sensors and generates a digit string based on the ranked features. The permutation of the digit string is indicative of the gesture performed by the user, which is identified by testing the permutation of the digit string against multiple sets of defined permutation conditions. A single reference gesture may be performed by the user to (re-)calibrate the wearable EMG device before and/or during use.

Abstract: Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects and determines features of signals when a user performs a physical gesture, and processes the features by performing a decision tree analysis. The decision tree analysis invokes a decision tree stored in the memory, where storing and executing the decision tree may be managed by limited computational resources. The outcome of the decision tree analysis is a probability vector that assigns a respective probability score to each gesture in a gesture library.

Abstract: Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory storing data and/or instructions for performing gesture identification. The wearable EMG device detects signals when a user performs a physical gesture and characterizes a signal vector {right arrow over (s)} based on features of the detected signals. A library of gesture template vectors G is stored in the memory of the wearable EMG device and a respective property of each respective angle ?i formed between the signal vector {right arrow over (s)} and respective ones of the gesture template vectors {right arrow over (g)}i is analyzed to match the direction of the signal vector {right arrow over (s)} to the direction of a particular gesture template vector {right arrow over (g)}*.

Abstract: Systems, devices, and methods adapt established concepts from natural language processing for use in gesture identification algorithms. A gesture identification system includes sensors, a processor, and a non-transitory processor-readable memory that stores data and/or instructions for performing gesture identification. A gesture identification system may include a wearable gesture identification device. The gesture identification process involves segmenting signals from the sensors into data windows, assigning a respective “window class” to each data window, and identifying a user-performed gesture based on the corresponding sequence of window classes. Each window class exclusively characterizes at least one data window property and is analogous to a “letter” of an alphabet. Under this model, each gesture is analogous to a “word” made up of a particular combination of window classes.

Abstract: Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory storing data and/or instructions for performing gesture identification. The wearable EMG device detects signals when a user performs a physical gesture and characterizes a signal vector {right arrow over (s)} based on features of the detected signals. A library of gesture template vectors G is stored in the memory of the wearable EMG device and a respective property of each respective angle ?i formed between the signal vector {right arrow over (s)} and respective ones of the gesture template vectors {right arrow over (g)}i is analyzed to match the direction of the signal vector {right arrow over (s)} to the direction of a particular gesture template vector {right arrow over (g)}*.

Abstract: Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects and determines features of signals when a user performs a physical gesture, and processes the features by performing a decision tree analysis. The decision tree analysis invokes a decision tree stored in the memory, where storing and executing the decision tree may be managed by limited computational resources. The outcome of the decision tree analysis is a probability vector that assigns a respective probability score to each gesture in a gesture library.

Abstract: Systems, articles, and methods for performing gesture identification with improved robustness against variations in use parameters and without requiring a user to undergo an extensive training procedure are described. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable storage medium that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects, determines, and ranks features in the signal data provided by the EMG sensors and generates a digit string based on the ranked features. The permutation of the digit string is indicative of the gesture performed by the user, which is identified by testing the permutation of the digit string against multiple sets of defined permutation conditions. A single reference gesture may be performed by the user to (re-)calibrate the wearable EMG device before and/or during use.