From neuromuscular activation to end-point locomotion: An artificial neural network-based technique for neural prostheses

Abstract

Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing non-invasive, closed-loop neuromuscular control strategies for neural prostheses. Our goal was to investigate if an Artificial Neural Network-based (ANN-based) model for closed-loop controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their endpoint gait parameters (such as stride length and step width). We recruited 12 persons with SB (5 females and 7 males) and collected their neuromuscular activation and end-point gait parameters during overground walking. Our results show that the proposed ANN-based technique can achieve a highly accurate prediction (e.g., R-values of 0.92 -0.97, ANN (tansig+tansig) for single composition of data sets) for altered end-point locomotion. Compared to traditional robust regression, this technique can provide up to 80% more accurate prediction. Our results suggest that more precise control of complex neural prostheses during locomotion can be achieved by engaging neuromuscular activity as intrinsic feedback to generate end-point leg movement. This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses.

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

“…ANNs have been used to predict end-point gait parameters from the EMG recorded from the neuromuscular activation of subjects with Spina Bifida (SB; Chang et al, 2009 ), and to achieve realtime dexterous control of a myoelectric prosthetic hand from cortical recordings of rhesus monkeys (Aggarwal et al, 2008 ). Echostate Neural Networks are a Recurrent Neural Network (Sussillo et al, 2012 ) that have been used in non-human primates for a motor BMI, and have been able to outperform the Kalman Filter.…”

Feasibility of Using Neuro-Fuzzy Subject-Specific Models for Functional Electrical Stimulation Induced Hand Movements

“…As an alternative to mathematical models, Artificial Neural Networks (ANN) have been successfully implemented in many FES applications (Chang et al 2009, Hincapie and Kirsch 2009, Yu et al 2002, Malešević et al 2010), due to their ability to learn from and predict the behaviour of complex systems. Similarly, Fuzzy Systems (FS) or Expert Systems for modelling and control have also been proposed in many FES applications, in which previous knowledge from human experts can be transferred to the FS by means of membership functions and fuzzy rules (Abdulla and Tokhi 2013, Miura 2011, Davoodi and Andrews 2004.…”