Algorithm that facilitates graded and seamless movement of an upper limb prosthetic through enhanced electromyographic signal processing.

There are over 2 million amputees in the United States, all of whom face challenges that impact their quality of life. Innovations in myoelectric prosthetics are emerging that enable better prosthetic-amputee control and interaction, and displace passive, older prosthetics. Yet, current models of myoelectric prosthetics rely on low-degree-of-freedom decoding methods that create obstacles to graded movements and require significant contractile force to stimulate.

Software designed using a Kalman filter enables individual digit, wrist, elbow, and shoulder control for transhumoral and above amputees. The algorithm allows real-time, proportional, intuitive control of the prosthetic, with no need for recalibration. This gives prosthetics capabilities more akin to natural limbs and improves users’ quality of life.

Proof of Concept

• Can be used with any number and type of electromyographic electrodes.
• Trainable algorithm responds to a simple calibration routine.
• Output is user-adjustable without the assistance of a prosthetist.

Wendelken, S., Page, D. M., Davis, T., Wark, H. A., Kluger, D. T., Duncan, C.,… Clark, G. A. (2017). Restoration of motor control and proprioceptive and cutaneous sensation in humans with prior upper-limb amputation via multiple Utah Slanted Electrode Arrays (USEAs) implanted in residual peripheral arm nerves. Journal of NeuroEngineering and Rehabilitation, 14(1). doi: 10.1186/s12984-017-0320-4

Publication Number: US 2022 0022801 A1
Patent Title: Signal Processing for Decoding Intended Movements from Electromyographic Signals
Jurisdiction/Country: United States
Application Type: Non-Provisional

Huy Tran
(801) 581-7792
huy.tran@utah.edu