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Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands. Jacob L. Segil, PhD; Richard F. ff . Weir, PhD. Aim

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Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

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  1. Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands Jacob L. Segil, PhD; Richard F. ff. Weir, PhD

  2. Aim • Develop postural control algorithm for myoelectric control system (MEC) of multifunctional prosthetic hand that allows multiple grasps and increased articulation using surface electromyography (EMG). • Relevance • Multifunctional myoelectric prosthetic hands can produce multiple grasping postures using MECs. Current MECs restrict multifunctional devices to single function (i.e., open/close) because of challenge of deciphering user intent from surface EMG. Further development of effective MECs will substantially progress the field of upper-limb prosthetic control.

  3. Methods • All subjects were nondisabled with normal vision and upper-limb function. Experiment was conducted with the dominant limb (11 right-hand dominant subjects). Experimental meeting took approximately 2 hr. • Experiment A consisted of target-acquisition task using various configurations of controller and was completed by 7 subjects to empirically derive best cursor-control scheme and electrode configuration. • Experiment B consisted of posture-matching exercise using various forms of visual feedback over 3 d and was completed by 4 subjects in order to measure ability of users when performing a more clinically oriented task..

  4. Results • Experiment A: All metrics described same 2 findings: velocity control method allowed for better control and number of control sites did not change performance. • Experiment B: Ability of subjects to command a 6 DoF virtual hand into 7 functional postures was quantified using completion rate (CR), movement time (MT), and path efficiency (PE). Average CR, MT, and PE across subjects was 82 ± 4%, 3.5 ± 0.2 s, and 45 ± 3%. • Retention of ability was tested by comparing performance during pretest sessions. CR and PE results were significantly different across days. • Effect of visual feedback on performance was tested by comparing average metrics across days for target (Tar), no target (nTar), and test (T) sessions. CR was significantly greater during Tar and nTar sessions. MT and PE did not change across visual feedback paradigms. Additional visual feedback increased frequency but not speed or precision of successful trials.

  5. Conclusion • Replacement of human hand after amputation requires solving intensive engineering challenges, including development of real-time and intuitive MECs. • Novel MEC was tested in order to determine key design features and measure performance across multiple days. • Use of velocity cursor-control caused performance to increase. • Number of surface electrodes used by subjects (3, 4, or 12) did not cause a change in performance. • Ability to command virtual hand was measured at a high level and did not degrade across multiple days of testing

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