Determining intended user movement to control an external device

A system, comprising:

• at least one electrode configured to be located on a limb of a user;
• and
• a controller, coupled to the at least one electrode, comprising:
• a non-transitory memory storing instructions;
• and
• a processor to execute the instruction stored in the memory to at least:
• receive user-specific EMG signals recorded by electrodes located on a limb of a user, wherein the user-specific EMG signals comprise one or more EMG patterns that indicate a single movement;
• decompose the user-specific EMG signals into the one or more EMG patterns in EMG feature space that indicate the single movement;
• update a user-specific model of muscular activity to include the single movement and corresponding one or more primary muscle patterns based on the one or more EMG patterns in EMG feature space;
• and
• predict an intent of the user to move the external device based on the user-specific model of muscular activity by:
• identifying a partition of the EMG feature space where the unknown EMG signal is located;
• bounding the partition of the EMG feature space where the unknown EMG signal is located by known single movements with a corresponding one or more primary muscle patterns;
• and
• determining a ratio of movement vectors that should be combined to determine the direction and the magnitude of the intent of the user to move the external device, wherein the ratio of movement vectors corresponds to the unknown EMG signal,
• wherein the user-specific model of muscular activity is used to control an external device based on muscular activity in the limb of the user.

A method, comprising:

• receiving, by a system comprising a processor, user-specific EMG signals recorded by electrodes located on a limb of a user, wherein the user-specific EMG signals comprise one or more EMG patterns that indicate a single movement;
• decomposing, by the system, the user-specific EMG signals into the one or more EMG patterns in EMG feature space that indicate the single movement;
• and
• updating, by the system, a user-specific model of muscular activity to include the single movement and corresponding one or more primary muscle patterns by:
• extracting a feature from each the one or more EMG patterns in EMG feature space that indicate the single movement, wherein each of the user specific EMG signals is recorded by a different electrode;
• determining, by a fitting method, the one or more primary muscle patterns for the feature;
• generating the EMG feature space based on the feature extracted for the single movement;
• partitioning the EMG feature space into a tetrahedra of N+1 vertices;
• and
• generating a Triangulated Irregular Network based on the tetrahedra of N+1 vertices, wherein the Triangulated Irregular Network corresponds to the user-specific model of muscular activity,
• wherein the user-specific model of muscular activity is used to control an external device based on muscular activity in the limb of the user.