Neuromuscular control of physical objects in an environment
Methods and apparatus for controlling a physical object in an environment based, at least in part, on neuromuscular signals. The method comprises recording a plurality of neuromuscular signals from a plurality of neuromuscular sensors arranged on one or more wearable devices worn by a user, receiving a selection of a physical object within the environment, and controlling, based at least in part on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, an operation of the selected object within the environment.
1. A computerized system for controlling a physical object in an environment based, at least in part, on neuromuscular signals, the system comprising:
a plurality of neuromuscular sensors configured to record a plurality of neuromuscular signals from a user, wherein the plurality of neuromuscular sensors are arranged on one or more wearable devices; and
at least one computer processor programmed to:
select a physical object within an environment of the user;
provide feedback to the user indicating that the physical object has been selected;
determine a first muscular activation state, based, at least in part, on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals; and
control an operation of the selected physical object based, at least in part, on the determined first muscular activation state, wherein
selection of the physical object uses an inference model to predict the selection of the physical object based on the plurality of neuromuscular signals, and
the inference model is trained to associate the plurality of neuromuscular signals with one or more selectable physical objects in the environment.
2. The computerized system of claim 1 , further comprising:
at least one storage device configured to store information describing an association between one or more selectable physical objects in the environment and a plurality of muscular activation states,
wherein selecting the physical object within the environment is based, at least in part, on the stored information.
3. The computerized system of claim 1 , wherein
the plurality of neuromuscular signals used, at least in part, to determine the first muscular activation state are recorded during a first time period,
the at least one computer processor is further programmed to determine a second muscular activation state based, at least in part, on a plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals recorded during a second time period, and
selecting the physical object within the environment of the user is based, at least in part, on the determined second muscular activation state.
4. The computerized system of claim 1 , wherein selecting the physical object is based, at least in part, on user context and/or user behavior.
5. The computerized system of claim 1 , wherein
the at least one computer processor is further programmed to receive speech input from the user, and
selecting the physical object is based, at least in part, on the received speech input.
6. The computerized system of claim 1 , wherein selecting the physical object is based, at least in part, on a proximity of the user to the physical object.
7. The computerized system of claim 6 , wherein selecting the physical object is based, at least in part, on a proximity of the user to the physical object relative to other selectable physical objects in the environment of the user.
8. The computerized system of claim 1 , wherein providing feedback to the user indicating that the physical object has been selected comprises instructing the selected physical object to provide the feedback.
9. The computerized system of claim 1 , wherein providing feedback to the user indicating that the physical object has been selected comprises providing one or more of auditory feedback and vibratory feedback.
10. The computerized system of claim 1 , further comprising:
at least one storage device configured to store information describing an association between at least one control action for each of one or more selectable physical objects in the environment and a corresponding muscular activation state including the first muscular activation state,
wherein controlling an operation of the selected physical object is based, at least in part, on the stored information describing an association between the at least one control action for the selected physical object and the first muscular activation state.
11. The computerized system of claim 1 , wherein the at least one computer processor is further programmed to:
detect, based at least in part on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, a second muscular activation state indicating a desire to control an operation of the selected physical object.
12. The computerized system of claim 11 , wherein the at least one computer processor is further programmed to:
send an instruction to the selected physical object to enable or disable a user interface of the selected physical object in response to detecting the second muscular activation state, wherein sending an instruction to the selected physical object comprises sending an instruction to enable the user interface; and
control an operation of the selected physical object based, at least in part, on the first muscular activation state after the user interface has been enabled.
13. A method performed by a computerized system for controlling a physical object in an environment based, at least in part, on neuromuscular signals, the method comprising:
recording a plurality of neuromuscular signals from a plurality of neuromuscular sensors arranged on one or more wearable devices;
selecting a physical object within an environment of the user;
providing feedback to the user indicating that the physical object has been selected;
determining a first muscular activation state, based, at least in part, on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals; and
controlling an operation of the selected physical object based, at least in part, on the determined first muscular activation state, wherein
selecting the physical object uses an inference model to predict selection of the physical object based on the plurality of neuromuscular signals, and
the inference model is trained to associate the plurality of neuromuscular signals with one or more selectable physical objects in the environment.
14. The method of claim 13 , further comprising:
storing, on at least one storage device, information describing an association between one or more selectable physical objects in the environment and a plurality of muscular activation states,
wherein selecting the physical object within the environment is based, at least in part, on the stored information.
15. The method of claim 13 , wherein selecting the physical is further based, at least in part, on user context and/or user behavior.
16. The method of claim 13 , further comprising:
receiving speech input from the user,
wherein selecting the physical object is based, at least in part, on the received speech input.
17. The method of claim 13 , wherein selecting the physical object is based, at least in part, on a proximity of the user to the physical object.
18. The method of claim 13 , wherein providing feedback to the user indicating that the physical object has been selected comprises instructing the selected physical object to provide the feedback.
19. The method of claim 13 , further comprising:
storing, by at least one storage device, information describing an association between at least one control action for each of one or more selectable physical objects in the environment and a corresponding muscular activation state including the first muscular activation state,
wherein controlling an operation of the selected physical object is based, at least in part, on the stored information describing an association between the at least one control action for the selected physical object and the first muscular activation state.
20. The method of claim 13 , further comprising:
detecting, based at least in part on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, a second muscular activation state indicating a desire to control an operation of the selected physical object; and
sending an instruction to the selected physical object to enable or disable a user interface of the selected physical object in response to detecting the second muscular activation state.
21. The method of claim 20 , wherein sending an instruction to the selected physical object comprises sending an instruction to enable the user interface, and wherein the method further comprises:
controlling an operation of the selected physical object based, at least in part, on the first muscular activation state after the user interface has been enabled.
22. The method of claim 20 , wherein
the plurality of neuromuscular signals used, at least in part, to determine the first muscular activation state are recorded during a first time period,
the method further comprises determining a second muscular activation state based, at least in part, on a plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals recorded during a second time period, and
selecting the physical object within the environment of the user is based, at least in part, on the determined second muscular activation state.
23. At least one non-transitory computer-readable storage medium storing instructions that, when executed by at least one computer processor, causes the at least one computer processor to perform a method, the method comprising:
recording a plurality of neuromuscular signals from a plurality of neuromuscular sensors arranged on one or more wearable devices;
selecting a physical object within the environment;
providing feedback to the user indicating that the physical object has been selected;
determining a first muscular activation state, based, at least in part, on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals; and
controlling an operation of the selected physical object based, at least in part, on the determined first muscular activation state, wherein
selecting the physical object uses an inference model to predict selection of the physical object based on the plurality of neuromuscular signals without a perceptible movement of the user, and
the inference model is trained to associate the plurality of neuromuscular signals with one or more selectable physical objects in the environment.
24. The at least one non-transitory computer-readable storage medium of claim 23 , wherein the method further comprises:
storing, on at least one storage device, information describing an association between one or more selectable physical objects in the environment and a plurality of muscular activation states,
wherein selecting the physical object within the environment is based, at least in part, on the stored information.
25. The at least one non-transitory computer-readable storage medium of claim 23 , wherein selecting the physical object is further based, at least in part, on user context and/or user behavior.
26. The at least one non-transitory computer-readable storage medium of claim 23 , wherein the method further comprises receiving speech input from the user, and
selecting the physical object is based, at least in part, on the received speech input.
27. The at least one non-transitory computer-readable storage medium of claim 23 , wherein providing feedback to the user indicating that the physical object has been selected comprises instructing the selected physical object to provide the feedback.
28. The at least one non-transitory computer-readable storage medium of claim 23 , wherein the method further comprises:
storing, by at least one storage device, information describing an association between at least one control action for each of one or more selectable physical objects in the environment and a corresponding muscular activation state including the first muscular activation state,
wherein controlling an operation of the selected physical object is based, at least in part, on the stored information describing an association between the at least one control action for the selected physical object and the first muscular activation state.
29. The at least one non-transitory computer-readable storage medium of claim 23 , wherein the method further comprises:
detecting, based at least in part on the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, a second muscular activation state indicating a desire to control an operation of the selected physical object;
sending an instruction to the selected physical object to enable a user interface of the selected physical object in response to detecting the second muscular activation state; and
controlling an operation of the selected physical object based, at least in part, on the first muscular activation state after the user interface has been enabled.
30. The at least one non-transitory computer-readable storage medium of claim 23 , wherein the plurality of neuromuscular signals used, at least in part, to determine the first muscular activation state are recorded during a first time period,
the method further comprises determining a second muscular activation state based, at least in part, on a plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals recorded during a second time period, and
selecting the physical object within the environment of the user is based, at least in part, on the determined second muscular activation state.