Arm cycling is a bi-manual motor task used in medical rehabilitation and in sports training. Understanding how muscle coordination changes across different biomechanical constraints in arm cycling is a step towards improved rehabilitation approaches. This exploratory study aims to get new insights on motor control during arm cycling. To achieve our main goal, we used the muscle synergies analysis to test three hypotheses: 1) body position with respect to gravity (sitting and supine) has an effect on muscle synergies; 2) the movement size (crank length) has an effect on the synergistic behavior; 3) the bimanual cranking mode (asynchronous and synchronous) requires different synergistic control. Thirteen able-bodied volunteers performed arm cranking on a custom-made device with unconnected cranks, which allowed testing three different conditions: body position (sitting versus supine), crank length (10cm versus 15cm) and cranking mode (synchronous versus asynchronous). For each of the eight possible combinations, subjects cycled for 30 seconds while electromyography of 8 muscles (4 from each arm) were recorded: biceps brachii, triceps brachii, anterior deltoid and posterior deltoid. Muscle synergies in this 8-dimensional muscle space were extracted by non-negative matrix factorization. Four synergies accounted for over 90% of muscle activation variances in all conditions. Results showed that synergies were affected by body position and cranking mode but practically unaffected by movement size. These results suggest that the central nervous system may employ different motor control strategies in response to external constraints such as cranking mode and body position during arm cycling.

Walking, swimming, cycling, and running are cyclic movements that are often performed in training programs or rehabilitation protocols by athletes or people with neuromotor disorders. The muscular and kinematic activities that are acquired during cyclic movements reveal control principles, especially for the optimization and stabilization of motor performance, for a given criterion in rehabilitation processes. The influence of external loads and resistive forces on limb movements should be considered in rehabilitation protocols and when assessing physical activity levels or defining activity patterns for the artificial control of limb movements. This chapter focuses on special cyclic limb movements: lower and upper limb cycling. Two aspects of this research and applications are discussed. First, variances of movement patterns are examined at different levels of the motor system (endpoint, joint configuration, muscle) during unimanual right and left arm cycling and bimanual cycling movements. Second, it is shown that the muscle activity patterns that are acquired during lower and upper limb cycling in able-bodied people may be used to define and improve stimulation patterns for functional electrical stimulation-driven cycling movements in spinal cord-injured individuals. This report also discusses the advantages of the application and control of these types of movements for the rehabilitation of people with paralyzed limbs.

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.