Myoelectric neural interface enables accurate control of a virtual multiple degree-of-freedom foot-ankle prosthesis.

Technological advances have enabled clinical use of powered foot-ankle prostheses. Although the fundamental purposes of such devices are to restore natural gait and reduce energy expenditure by amputees during walking, these powered prostheses enable further restoration of ankle function through possible voluntary control of the powered joints. Such control would greatly assist amputees in daily tasks such as reaching, dressing, or simple limb repositioning for comfort. A myoelectric interface between an amputee and the powered foot-ankle prostheses may provide the required control signals for accurate control of multiple degrees of freedom of the ankle joint. Using a pattern recognition classifier we compared the error rates of predicting up to 7 different ankle-joint movements using electromyographic (EMG) signals collected from below-knee, as well as below-knee combined with above-knee muscles of 12 trans-tibial amputee and 5 control subjects. Our findings suggest very accurate (5.3 ± 0.5%SE mean error) real-time control of a 1 degree of freedom (DOF) of ankle joint can be achieved by amputees using EMG from as few as 4 below-knee muscles. Reliable control (9.8 ± 0.7%SE mean error) of 3 DOFs can be achieved using EMG from 8 below-knee and above-knee muscles.

Neuromechanical sensor fusion yields highest accuracies in predicting ambulation mode transitions for trans-tibial amputees.

Advances in battery and actuator technology have enabled clinical use of powered lower limb prostheses such as the BiOM Powered Ankle. To allow ambulation over various types of terrains, such devices rely on built-in mechanical sensors or manual actuation by the amputee to transition into an operational mode that is suitable for a given terrain. It is unclear if mechanical sensors alone can accurately modulate operational modes while voluntary actuation prevents seamless, naturalistic gait. Ensuring that the prosthesis is ready to accommodate new terrain types at first step is critical for user safety. EMG signals from patient's residual leg muscles may provide additional information to accurately choose the proper mode of prosthesis operation. Using a pattern recognition classifier we compared the accuracy of predicting 8 different mode transitions based on (1) prosthesis mechanical sensor output (2) EMG recorded from residual limb and (3) fusion of EMG and mechanical sensor data. Our findings indicate that the neuromechanical sensor fusion significantly decreases errors in predicting 10 mode transitions as compared to using either mechanical sensors or EMG alone (2.3±0.7% vs. 7.8±0.9% and 20.2±2.0% respectively).

Performance of pattern recognition myoelectric control using a generic electrode grid with targeted muscle reinnervation patients.

Targeted muscle reinnervation (TMR) is a surgical technique that creates myoelectric prosthesis control sites for high-level amputees. The electromyographic signal patterns provided by the reinnervated muscles are well-suited for pattern recognition (PR) control. PR control uses more electrodes compared to conventional amplitude control techniques but their placement on the residual limb is less critical than for conventional amplitude control. In this contribution, we demonstrate that classification error and real-time control performances using a generically placed electrode grid were equivalent or superior to the performance when using targeted electrode placements on two transhumeral amputee subjects with TMR. When using a grid electrode layout, subjects were able to complete actions 0.290 sec to 1 sec faster and with greater accuracy as compared to clinically localized electrode placement (mean classification error of 1.35% and 3.2%, respectively, for a 5 movement-class classifier).These findings indicate that a grid electrode arrangement has the potential to improve control of a myoelectric prosthesis while reducing the time and effort associated with fitting the prosthesis due to clinical localization of control sites on amputee patients.