Upper-limb amputees commonly cite difficulty of control as one of the main reasons why they abandon their prostheses. Combining myoelectric control with autonomous sensor-based control could improve prosthesis control. However, the cognitive and physical impact of shared control and semi-autonomous systems on users has yet to be fully explored. In this study we introduce a novel shared-control algorithm that blends proportional position control predicted from electromyography (EMG) with proportional position control predicted from an autonomous machine using infrared sensors embedded in the prosthetic hand’s fingers to detect the distance to objects. The user’s EMG control is damped in proportion to the machine’s prediction of an object’s position in relation to a given finger. The shared-control algorithm was validated using three intact individuals completing a holding task where they attempted to hold an object for as long as possible without dropping it. Shared control resulted in fewer object drops, 32% less cognitive demand, and 49% less physical effort (measured by EMG) relative to the participant’s EMG control alone. These results indicate that shared control can reduce the physiological burdens on the user as well as increase prosthetic control.
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Shared Control Decreases the Physical and Cognitive Demands of Maintaining a Secure Grip
Upper-limb amputees commonly cite difficulty of control as one of the main reasons why they abandon their prostheses. Combining myoelectric control with autonomous sensor-based control could improve prosthesis control. However, the
cognitive and physical impact of shared control and semi-autonomous systems on users has yet to be fully explored. In this study we introduce a novel shared-control algorithm that blends proportional position control predicted from electromyography
(EMG) with proportional position control predicted from an autonomous machine using infrared sensors embedded in the prosthetic hand’s fingers to detect the distance to objects. The user’s EMG control is damped in proportion to the machine’s prediction of an object’s position in relation to a given finger. The shared-control algorithm was validated using three intact individuals completing a holding task where they attempted to hold an object for as long as possible without dropping it. Shared control resulted in fewer object drops, 32% less cognitive demand, and 49% less physical effort (measured by EMG) relative to the participant’s EMG control alone. These results indicate that shared control can reduce the physiological burdens on the user as well as increase prosthetic control.
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- Award ID(s):
- 1901492
- NSF-PAR ID:
- 10351635
- Date Published:
- Journal Name:
- Proceedngs of the Myoelectric Controls and Upper Limb Prosthetics Symposium
- Page Range / eLocation ID:
- 82-85
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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