Methods and apparatus for autocalibration of a wearable electrode sensor system
Methods and systems used in calibrating the position and/or orientation of a wearable device configured to be worn on a wrist or forearm of a user, the method comprises sensing a plurality of neuromuscular signals from the user using a plurality of sensors arranged on the wearable device, and providing the plurality of neuromuscular signals and/or signals derived from the plurality of neuromuscular signals as inputs to one or more trained autocalibration models, determining based, at least in part, on the output of the one or more trained autocalibration models, a current position and/or orientation of the wearable device on the user, and generating a control signal based, at least in part, on the current position and/or orientation of the wearable device on the user and the plurality of neuromuscular signals.
1. A system comprising:
a plurality of sensors configured to continuously sense neuromuscular signals from a body part of a user; and
at least one computer processor programmed to:
determine based, at least in part, on the neuromuscular signals sensed from the body part of the user, a current position and/or orientation of the plurality of sensors on the user's body part;
present a user interface to the user indicating a current position and/or orientation of the plurality of sensors; and
generate a control signal based, at least in part, on the current position and/or orientation of the plurality of sensors on the user's body part, wherein the control signal causes the user interface to instruct the user to variably reposition the plurality of sensors to a different position on the user's body part based on the determined current position and/or orientation and further based on which position and/or orientation of the user's body part is associated with a specific set of training data generated from a specific input model.
2. The system of claim 1 , wherein the plurality of sensors comprises a wearable device configured to be worn on a wrist or forearm of the user.
3. The system of claim 2 , further comprising providing the plurality of neuromuscular signals as inputs to one or more trained autocalibration models to calibrate the position and/or orientation of the wearable device.
4. The system of claim 1 , wherein the current position and/or orientation are determined based on one or more specified neuromuscular signals or patterns that indicate a distal or proximal position of the user's body part on which the plurality of sensors is located.
5. The system of claim 4 , wherein the control signal causes the user interface to instruct the user to reposition the plurality of sensors to a more distal or more proximal location on the user's body part based on the determined current position and/or orientation.
6. The system of claim 5 , wherein the instruction to reposition the plurality of sensors to a more distal or more proximal location on the user's body part is based on and is specific to the neuroanatomy of the user's body part.
7. The system of claim 4 , further comprising:
determining that an increased amount of data is needed from the plurality of sensors; and
causing the user interface, via the control signal, to instruct the user to reposition the plurality of sensors to a more proximal location on the user's body part.
8. The system of claim 4 , further comprising:
determining that additional training data is available for the plurality of sensors at a distal location on the user's body part; and
causing the user interface, via the control signal, to instruct the user to reposition the plurality of sensors to a more distal location on the user's body part.
9. The system of claim 1 , wherein the at least one computer processor is programmed to determine the current position and/or orientation of the plurality of sensors on the user without the user performing a particular pose or gesture during sensing of the plurality of neuromuscular signals.
10. The system of claim 1 , wherein the at least one programmed processor is further programmed to: generate, using an autocalibration model, one or more calibrated neuromuscular signals based, at least in part, on the current position and/or orientation of the plurality of sensors, wherein generating the control signal comprises generating a control signal based, at least in part, on the calibrated neuromuscular signals.
11. The system of claim 10 , wherein the autocalibration model used to generate the calibrated neuromuscular signals comprises a neural network.
12. The system of claim 1 , wherein the control signal is configured to control one or more operations of or within a virtual reality system or an augmented reality system.
13. A computer-implemented method comprising:
determining based, at least in part, on one or more neuromuscular signals sensed from a body part of a user wearing a plurality of sensors, a current position and/or orientation of the plurality of sensors on the user's body part;
presenting a user interface to the user indicating a current position and/or orientation of the plurality of sensors; and
generating a control signal based, at least in part, on the current position and/or orientation of the plurality of sensors on the user's body part, wherein the control signal causes the user interface to variably instruct the user to reposition the plurality of sensors to a different position on the user's body part based on the determined current position and/or orientation and further based on which position and/or orientation of the user's body part is associated with a specific set of training data generated from a specific input model.
14. The method of claim 13 , wherein the plurality of sensors comprises a wearable device configured to be worn on a wrist or forearm of the user.
15. The method of claim 14 , further comprising providing the plurality of neuromuscular signals as inputs to one or more trained autocalibration models to calibrate the position and/or orientation of the wearable device.
16. The method of claim 13 , wherein the current position and/or orientation are determined based on one or more specified neuromuscular signals or patterns that indicate a distal or proximal position of the user's body part on which the plurality of sensors is located.
17. The method of claim 16 , wherein the control signal causes the user interface to instruct the user to reposition the plurality of sensors to a more distal or more proximal location on the user's body part based on the determined current position and/or orientation.
18. The method of claim 17 , wherein the instruction to reposition the plurality of sensors to a more distal or more proximal location on the user's body part is based on and is specific to the neuroanatomy of the user's body part.
19. The method of claim 16 , further comprising:
determining that an increased amount of data is needed from the plurality of sensors; and
causing the user interface, via the control signal, to instruct the user to reposition the plurality of sensors to a more proximal location on the user's body part.
20. A wearable device comprising:
a plurality of sensors configured to continuously sense neuromuscular signals from a body part of a user; and
at least one computer processor programmed to:
determine based, at least in part, on the neuromuscular signals sensed from the body part of the user, a current position and/or orientation of the plurality of sensors on the user's body part;
present a user interface to the user indicating a current position and/or orientation of the plurality of sensors; and
generate a control signal based, at least in part, on the current position and/or orientation of the plurality of sensors on the user's body part, wherein the control signal causes the user interface to variably instruct the user to reposition the plurality of sensors to a different position on the user's body part based on the determined current position and/or orientation and further based on which position and/or orientation of the user's body part is associated with a specific set of training data generated from a specific input model.