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

Achieving the functional grasp by electrical stimulation using surface electrodes is a demanding task. The innervations of muscles come via ulnar, radial and median nerves. The anatomy of nerve branches connecting various muscles in the forearm differs significantly between individuals. We hypothesize that the anatomical differences between the paretic and nonparetic arms are minimal. Based on this assumption we developed a method where the differences of muscle activities (EMG) between the healthy and paretic arms recorded by the 24-contact electrode within an array define the target zones to be stimulated on the affected forearm. We used special electrode where magnetic contacts allow simple change of the stimulation pads. The examiner positions the magnetic contact on the pads where the EMG differences are maximal. The stimulator delivers asynchronous stimulation to the selected pads. We proved that the method is working in stroke patients by measuring joint angles and the grasping force.

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“…Color codes: red -high intensity, orange -middle intensity, yellow -low intensity). Modified from [7].…”

Section: Resultsmentioning

confidence: 99%

“…The testing of the automatic procedures showed that it is not simple enough to be used in the daily clinical use. Popović-Maneski et al [7] introduced the method presented here.…”

Section: Introductionmentioning

confidence: 99%

EMG Map for Designing the Electrode Shape for Functional Electrical Therapy of Upper Extremities

Popović-Maneski

Topalović

2018

Biosystems &Amp; Biorobotics

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“…The primary contributions of Dejan's research are the methods and assistive devices for the rehabilitation and electrophysiological assessment of persons with motor disabilities. Besides already mentioned contributions, he designed several sensors for the assessment of motor function, including instrumented shoe insoles, 18 sensing systems for gait kinematics, 19 assessment of spasticity, 20 and array electrodes with multichannel amplifiers for EMG and EEG analysis 21 . Dejan designed several clinically validated protocols for the assessment of motor performance.…”

Section: Figurementioning

confidence: 99%

Dejan B. Popović (1950–2021)

et al. 2022

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“…FES is able to partially substitute movements such as pedaling movement, walking, or grasping (Heesterbeek et al, 2005; Kordjazi & Kobravi, 2012; Popović Maneski et al, 2016). In addition to these impressive results, the FES technique has an equivalently important role in the preservation of the general physiological state and fitness of the patient.…”

Section: Introductionmentioning

confidence: 99%

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

Radeleczki

Mravcsik

Bozheim

et al. 2022

The Anatomical Record

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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.

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

Cited by 19 publications

References 21 publications

EMG Map for Designing the Electrode Shape for Functional Electrical Therapy of Upper Extremities

EMG Map for Designing the Electrode Shape for Functional Electrical Therapy of Upper Extremities

Dejan B. Popović (1950–2021)

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

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