Adaptive system for deriving control signals from measurements of neuromuscular activity

1. A system for adapting a control mapping associating sensor signals with control signals for controlling an operation of a device, the system comprising:

a plurality of sensors including a plurality of neuromuscular sensors arranged on one or more wearable components, wherein the plurality of neuromuscular sensors are configured to continuously record neuromuscular signals from a user;

at least one computer hardware processor;

at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

obtaining first state information for an operation of the device, wherein the first state information includes information relating to the state of a task;

providing the first state information as input to an intention model associated with the operation of the device and obtaining a first intention model output including an estimate of an intended control signal;

providing the neuromuscular signals and/or signals derived from the neuromuscular signals as inputs to a first control mapping and obtaining a first control mapping output;

comparing the first control mapping output with the estimate of the intended control signal;

updating the first control mapping using the inputs to the first control mapping and the first intention model output to obtain a second control mapping, and

controlling the device using a control signal based on the second control mapping.

2. The system of claim 1 , wherein the processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

obtaining a new set of neuromuscular signals from the plurality of neuromuscular sensors;

providing the new set of neuromuscular signals and/or signals derived from the new set of neuromuscular signals as inputs to the second control mapping to obtain a second control mapping output;

comparing the the second control mapping output with a second estimate of the intended control signal, and determining a third control mapping; and

controlling the device using the third control mapping.

3. The system of claim 1 , wherein the processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

obtaining second state information for an at least one user-interactive application;

providing the second state information as input to the intention model associated with the operation of the device and obtaining a second intention model output; and

updating the second control mapping using the inputs provided to the second control mapping and the second intention model output to obtain a third control mapping.

4. The system of claim 1 , wherein updating the first control mapping using the inputs provided to the first control mapping and the first intention model output to obtain the second control mapping is performed using a recursive least squares (RLS) technique.

5. The system of claim 4 , wherein the RLS technique is configured to fit a linear model that predicts velocity control signals indicating the user's control intentions from the neuromuscular signals.

6. The system of claim 1 , wherein the intention model is a trained statistical model implemented as a recurrent neural network.

7. The system of claim 6 , wherein the recurrent neural network is a long short-term memory (LSTM) neural network.

8. The system of claim 1 , wherein the intention model is trained statistical model implemented as a non-linear regression model.

9. The system of claim 1 , wherein the neuromuscular sensors comprise electromyography (EMG) sensors, mechanomyography (MMG) sensors, sonomyography (SMG) sensors, or a combination of EMG, MMG and SMG sensors.

10. The system of claim 1 , wherein the one or more wearable components consists of a single wearable component, and wherein all of the plurality of sensors are arranged on the single wearable component configured to be worn on or around a body part of the user.

11. The system of claim 1 , wherein at least one of the one or more wearable components comprises a flexible or elastic band configured to be worn around a body part of the user.

12. The system of claim 11 , wherein the flexible or elastic band comprises an armband configured to be worn around an arm of the user.

13. The system of claim 1 , wherein the plurality of sensors further includes at least one Inertial Measurement Unit (IMU) sensor configured to continuously record IMU signals, wherein the processor-executable instructions, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

providing the IMU signals and/or information based on the IMU signals as inputs to the first control mapping.

14. The system of claim 13 , wherein the at least one IMU sensor and the plurality of neuromuscular sensors are arranged on a same wearable component.

15. The system of claim 1 , wherein updating the first control mapping further comprises updating the first control mapping using the first control mapping output.

16. The system of claim 1 , wherein the intention model is generated based on heuristic or intuitive approximation of user interactions with the device or observed user interactions by one or more users with the device.

17. The system of claim 1 , wherein the device comprises a computer executing a user-interactive application, and wherein the operation of the device comprises an operation of the user-interactive application.

18. The system of claim 1 , wherein the processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:

controlling the device using the second control mapping.

19. A method of adapting a control mapping associating sensor signals with control signals for controlling an operation of a device, the method comprises;

obtaining a plurality of sensor signals from a plurality of sensors including a plurality of neuromuscular sensors arranged on one or more wearable components, wherein the plurality of neuromuscular sensors are configured to continuously record neuromuscular signals from a user;

obtaining first state information for an operation of the device, wherein the first state information includes information relating to the state of a task;

providing the first state information as input to an intention model associated with the operation of the device and obtaining a first intention model output including an estimate of an intended control signal;

providing the neuromuscular signals and/or signals derived from the neuromuscular signals as inputs to a first control mapping and obtaining a first control mapping output;

updating the first control mapping using the inputs provided to the first control mapping and the first intention model output to obtain a second control mapping, wherein the second control mapping is based at least in part on a comparison between the first control mapping output and the first intention model output, and

controlling the device using a control signal based on the second control mapping.

20. A non-transitory computer-readable storage medium encoded with a plurality of instructions that, when executed by at least one computer hardware processor perform a method comprising:

obtaining a plurality of sensor signals from a plurality of sensors including a plurality of neuromuscular sensors arranged on one or more wearable components, wherein the plurality of neuromuscular sensors are configured to continuously record neuromuscular signals from a user;

obtaining first state information for an operation of a device, wherein the first state information includes information relating to the state of a task;

providing the first state information as input to an intention model associated with the operation of the device and obtaining a first intention model output including an estimate of an intended control signal;

providing the neuromuscular signals and/or signals derived from the neuromuscular signals as inputs to a first control mapping and obtaining a first control mapping output;

updating the first control mapping using the inputs provided to the first control mapping and the first intention model output to obtain a second control mapping, and

controlling the device using a control signal based on the second control mapping.