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

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 movement-simpler control modalities or further consideration of applying machine-learning techniques has to be taken into account to improve voluntary upper limb-controlled FES assisted leg cycling.

Effect of FES controlled cycling training on cardiovascular and pulmonary systems in a spinal cord injured patient

Introduction: The results of two spiroergometric measurements are presented that were taken from a spinal cord injured patient who participated in FES cycling training sessions for 11 months. Methods: The two measurements were taken 4 and 11 months after starting the training program. We investigated the respiratory exchange ratio (RER) and ventilation (VE/VO2, VE’/VCO2) ratios and the cycling cadence in the two investigated training sessions. Results: In the first assessment, the RER was below 1, which is the anaerobic training limit. Seven months later, in the second assessment, the RER value exceeded the anaerobic limit a few times and remained above it at the end of the session. The VE /VO2 and VE/VCO2 curves did not intersect during the first assessment. In the second one, the VE/VO2 and VE/VCO2 curves intersected several times and the oxygen quotient curve exceeded the carbon dioxide quotient curve. The patient achieved a low but similar cycling speed during the two assessments. Conclusion: Through the activation of paralyzed muscles with FES cycling we are able to train the paraplegic patient in aerobic and anaerobic training zones. This is shown by the value of RER, which reached the anaerobic training limit during the second assessment and remained in the anaerobic range for longer time.

Effects of gravity and kinematic constraints on muscle synergies in arm cycling

Arm cycling is a bimanual 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 toward 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 vs. supine), crank length (10 cm vs. 15 cm), and cranking mode (synchronous vs. asynchronous). For each of the eight possible combinations, subjects cycled for 30 s while electromyography of eight muscles (four from each arm) were recorded: biceps brachii, triceps brachii, anterior deltoid, and posterior deltoid. Muscle synergies in this eight-dimensional muscle space were extracted by nonnegative 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.NEW & NOTEWORTHY Recent studies analyzed muscle synergies in lower limb cycling. Here, we examine upper limb cycling and specifically the effect of body position with respect to gravity, movement size, and cranking mode on muscle coordination during arm cranking tasks. We show that altered body position and cranking mode affects modular organization of muscle activities. To our knowledge, this is the first study assessing motor control through muscle synergies framework during upper limb cycling with different constraints.

The Effect of Crank Resistance on Arm Configuration and Muscle Activation Variances in Arm Cycling Movements

Arm cycling on an ergometer is common in sports training and rehabilitation protocols. The hand movement is constrained along a circular path, and the user is working against a resistance, maintaining a cadence. Even if the desired hand trajectory is given, there is the flexibility to choose patterns of joint coordination and muscle activation, given the kinematic redundancy of the upper limb. With changing external load, motor noise and changing joint stiffness may affect the pose of the arm even though the endpoint trajectory is unchanged. The objective of this study was to examine how the crank resistance influences the variances of joint configuration and muscle activation. Fifteen healthy participants performed arm cranking on an arm-cycle ergometer both unimanually and bimanually with a cadence of 60 rpm against three crank resistances. Joint configuration was represented in a 3-dimensional joint space defined by inter-segmental joint angles, while muscle activation in a 4-dimensional "muscle activation space" defined by EMGs of 4 arm muscles. Joint configuration variance in the course of arm cranking was not affected by crank resistance, whereas muscle activation variance was proportional to the square of muscle activation. The shape of the variance time profiles for both joint configuration and muscle activation was not affected by crank resistance. Contrary to the prevailing assumption that an increased motor noise would affect the variance of auxiliary movements, the influence of noise doesn't appear at the joint configuration level even when the system is redundant. Our results suggest the separation of kinematic- and force-control, via mechanisms that are compensating for dynamic nonlinearities. Arm cranking may be suitable when the aim is to perform training under different load conditions, preserving stable and secure control of joint movements and muscle activations.

Jerk Decomposition during Bimanual Independent Arm Cranking

The relationship between the smoothness of the upper limb endpoint movement and multi joint angular motion is a function of the individual joint angular velocities, accelerations, and jerks as well as the instantaneous arm configuration and its rate of change during movement execution. We compared the contribution of jerk components to the total endpoint jerk in able bodied participants who performed arm cranking movements on an arm cranking device where the two arms could crank independently. The results of this investigation suggest that the most dominant components of the end effector jerk are related to both the angular jerks and to the change of arm configuration pose. This jerk partitioning is much stronger as it was found previously for both reaching arm movements and single hand cranking. This shows the task specificity of the decomposition of endpoint jerk and the effect that bi-manual tasks can have on the smoothness of movements. The proposed decomposition may give useful information in why certain bi-manual rehabilitation processes are more useful than others.

Control of Cycling Limb Movements: Aspects for Rehabilitation

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.