Stimulation map for control of functional grasp based on multi-channel EMG recordings

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.

Personalized protocol and scoring scale for functional electrical stimulation of the hand: A pilot feasibility study

BACKGROUND

Complex personalized Functional Electrical Stimulation (FES) protocols for calibrating parameters and electrode positioning have been proposed, most being time-consuming or technically cumbersome for clinical settings. Therefore, there is a need for new personalized FES protocols that generate comfortable, functional hand movements, while being feasible for clinical translation.

OBJECTIVE

To develop a personalized FES protocol, comprising electrode placement and parameter selection, to generate hand opening (HO), power grasp (PW) and precision grip (PG) movements, and compare in a pilot feasibility study its performance to a non-personalized protocol based on standard FES guidelines.

METHODS

Two FES protocols, one personalized (P1) and one non-personalized (P2), were used to produce hand movements in twenty-three healthy participants. FES-induced movements were assessed with a new scoring scale which comprises items for selectivity, functionality, and comfort.

RESULTS

Higher FES-HSS scores were obtained with P1 for all movements: HO (p= 0.00013), PW (p= 0.00007), PG (p= 0.00460). Electrode placement time was significantly shorter for P2 (p= 0.00003). Comfort scores were similar for both protocols.

CONCLUSIONS

The personalized protocol for electrode placement and parameter selection enabled functional FES-induced hand movements and presented advantages over a non-personalized protocol. This protocol warrants further investigation to confirm its suitability for developing upper-limb rehabilitation interventions with clinical translational potential.

EMG map image processing for recognition of fingers movement

Electromyography (EMG) is the conventional noninvasive method for the estimation of muscle activities. We developed a new image processing method for the recognition of individual finger movements based on EMG maps. The maps were formed from the EMG recordings via an array electrode with 24 contacts connected to a multichannel wireless miniature digital amplifier. The task was to detect and quantify the high activity regions in the EMG maps in persons with no known motor impairment. The results show the temporal and spatial patterns within the images during well-defined finger movements. The average accuracy of the automatic recognition compared with the recognition by an expert clinician in persons involved in the tests was 97.87 ± 0.92%. The application of the technique is foreseen for control for an assistive system (hand prosthesis and exoskeleton) since the interface is wearable and the processing can be implemented on a microcomputer.