Direct continuous electromyographic control of a powered prosthetic ankle for improved postural control after guided physical training: A case study

Abstract

Despite the promise of powered lower limb prostheses, existing controllers do not assist many daily activities that require continuous control of prosthetic joints according to human states and environments. The objective of this case study was to investigate the feasibility of direct, continuous electromyographic (dEMG) control of a powered ankle prosthesis, combined with physical therapist-guided training, for improved standing postural control in an individual with transtibial amputation. Specifically, EMG signals of the residual antagonistic muscles (i.e. lateral gastrocnemius and tibialis anterior) were used to proportionally drive pneumatical artificial muscles to move a prosthetic ankle. Clinical-based activities were used in the training and evaluation protocol of the control paradigm. We quantified the EMG signals in the bilateral shank muscles as well as measures of postural control and stability. Compared to the participant’s daily passive prosthesis, the dEMG-controlled ankle, combined with the training, yielded improved clinical balance scores and reduced compensation from intact joints. Cross-correlation coefficient of bilateral center of pressure excursions, a metric for quantifying standing postural control, increased to .83(±.07) when using dEMG ankle control (passive device: .39(±.29)). We observed synchronized activation of homologous muscles, rapid improvement in performance on the first day of the training for load transfer tasks, and further improvement in performance across training days (p = .006). This case study showed the feasibility of this dEMG control paradigm of a powered prosthetic ankle to assist postural control. This study lays the foundation for future study to extend these results through the inclusion of more participants and activities.

Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions

“…The prosthesis joint mechanics can be determined by the human feedforward neural output. This method has shown increasing success in improving various activity performance and postural control in a recent study [ 58 ]. Note that direct EMG control here is defined as a myoeletric control method for powered prostheses, which follows antagonistic muscle function around a joint for movement control.…”

“…This study showed a transtibial amputee could produce anticipatory postural adjustments on an EMG-controlled prosthetic ankle to significantly improve stability after a perturbation. They also studied the ability for a transtibial amputee to control a variety of standing postural control tasks like quiet standing on firm and compliant surfaces as well as load transfer tasks [ 58 ]. The results demonstrated the ability for an amputee to significantly improve bilateral EMG activation synchronization and standing postural control with direct EMG control of a prosthesis ankle after extended, guided training with a physical therapist.…”

“…Therefore, it does not impose additional mental load to amputees in using neuromuscular control most of the time in walking. For direct EMG control, extra mental processes while learning how to use EMG to operate a prosthesis joint during task performance are initially needed [ 5 , 58 , 107 ]. However, mental load may reduce after training.…”

Anatomical exoskeleton for movement approximation with neural networks

“…Each armband has 8 sensors in a ring that is designed to slide onto the arm to sense hand gestures. We modified and re-purposed two of these wearable armband arrays by joining them into one ring of 16 sensors to be worn over the major anterior and posterior muscles of the thigh (figure 3,4) [3,4]. A third armband array of 8 sensors is utilized to measure the calf movement and is positioned over the major muscles of the calf and lower leg (figure 5) [1].…”

A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network

“…More direct approaches, in which the EMG signals are explicitly related to the control of the prosthesis have been successfully implemented and provided users with non-weight-bearing volitional control [26] [27]. More recently, an EMG controller has been used to provide volitional control of the knee extension torque provided by a powered prosthesis enabling squatting, lunging, and sit to stand transition under different loading conditions [28][29] [30] [31]. Despite these promising results, voluntary EMG controllers suffer from limitations related to the low signal-to-noise ratio and the lack of muscle specificity typical of surface EMG sensors [32].…”