Prediction of leg muscle activities from arm muscle activities in arm and leg cycling

Abstract

Functional electrical stimulation (FES) driven leg cycling is usually controlled by previously established stimulation patterns. We investigated the potential utilization of a particular computational method for controlling electrical stimulation of lower limb muscles by real-time electromyography (EMG) signals of arm muscles during hybrid arm and leg cycling. In hybrid arm and leg cycling, arm cranking is performed voluntarily, while leg cycling is driven by FES. In this study, we investigate arm and leg cycling movements of able-bodied persons when both arm and leg cycling is performed voluntarily without FES. We present a neural network-based model in which the input of the neural network is given by a time series of upper limb muscle activities (EMG), and the output provides potential lower limb muscle activities. The particular neural network was a nonlinear autoregressive exogen (NARX) neural network. The measured EMG signals of the lower limb muscles were compared to the signals that were predicted by the neural network. The neural network was trained with data recorded from four participants. Our preliminary results show notable differences between the predicted and the experimentally measured lower limb muscle activities. The prediction was good only for 60% of the movement time. We conclude that-while including arm cycling in the movementsimpler control modalities or further consideration of applying machinelearning techniques has to be taken into account to improve voluntary upper limb-controlled FES assisted leg cycling.

Neurorobotics and neuroprostheses: Towards a new anatomy

“…The activities of the human muscles have been predicted during simultaneous arm and leg cycling movements (Radeleczki et al, 2023, this volume). This article describes a neural network‐based model (specifically, a nonlinear autoregressive exogen neural network) to predict lower limb muscles activity using upper limb muscle activity as input to the model.…”

Artificial anatomy

“…The works by Radeleczki et al (2023), Martín-Caro Álvarez et al ( 2022), and Okajima et al (2023) analyze knowledge about what humans do with our arms and legs, always with an eye toward learning the most relevant aspects to be mimicked in artificial devices. The work by Figueiredo et al (2023) is a perfect example of how an exoskeleton is fed back with the user's experience and changing external circumstances to produce a more adequate performance of the exoskeleton, and finally, in Demofonti et al (2023) we assist the analysis of how our knowledge of the anatomy and physiology of the human upper limb is combined with the design of a prosthetic system that imitates it.…”