Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography.

The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this model requires an exhaustive search over all the possible combinations of parameters, leading to computationally inefficient system. Various data-driven optimization methods were investigated and a modified Nelder⁻Mead algorithm using a Latin Hypercube Sampling method was introduced to tune the powered prosthetic. The results of the modified Nelder⁻Mead optimization were compared to the Exhaustive search, Genetic Algorithm, and conventional Nelder⁻Mead method, and the results showed the feasibility of using the presented method, to objectively calibrate the parameters in a time-efficient way using biological evidence.

EMG-based energy expenditure optimization for active prosthetic leg tuning.

In recent years, active prosthetic legs have been developed and deployed commercially that help amputees to initiate gait with less effort and more symmetry in the pattern. However, the process of initial set up and tuning is highly time and cost consuming. It requires prosthetic experts to observe the gait and the feedback from amputees to manually tune the parameters subjectively. In this study, an electromyography (EMG)-based energy expenditure optimization method was presented to automatically tune the prosthetic limb. For this purpose, a wide variety of lower body muscles were observed and the energy expenditure was modeled based on their electrical activity. The tuning optimization was implemented based on a grid-searching protocol designed in this study. This method resulted in a power value comparable to manual tuning, which provided enough force to facilitate gait for amputees. This study shows the feasibility of automatic tuning and removal of the need for referral to an expert.

Electroencephalogram-Based Brain-Computer Interface and Lower-Limb Prosthesis Control: A Case Study.

OBJECTIVE: The purpose of this study was to establish the feasibility of manipulating a prosthetic knee directly by using a brain-computer interface (BCI) system in a transfemoral amputee. Although the other forms of control could be more reliable and quick (e.g., electromyography control), the electroencephalography (EEG)-based BCI may provide amputees an alternative way to control a prosthesis directly from brain. METHODS: A transfemoral amputee subject was trained to activate a knee-unlocking switch through motor imagery of the movement of his lower extremity. Surface scalp electrodes transmitted brain wave data to a software program that was keyed to activate the switch when the event-related desynchronization in EEG reached a certain threshold. After achieving more than 90% reliability for switch activation by EEG rhythm-feedback training, the subject then progressed to activating the knee-unlocking switch on a prosthesis that turned on a motor and unlocked a prosthetic knee. The project took place in the prosthetic department of a Veterans Administration medical center. The subject walked back and forth in the parallel bars and unlocked the knee for swing phase and for sitting down. The success of knee unlocking through this system was measured. Additionally, the subject filled out a questionnaire on his experiences. RESULTS: The success of unlocking the prosthetic knee mechanism ranged from 50 to 100% in eight test segments. CONCLUSION: The performance of the subject supports the feasibility for BCI control of a lower extremity prosthesis using surface scalp EEG electrodes. Investigating direct brain control in different types of patients is important to promote real-world BCI applications.