Proceedings on Automation in Medical Engineering
Vol. 2 No. 1 (2023): Proc AUTOMED

Rehabilitation technology, ID 735

Machine learning-based assistance for electrode position validation in tSCS

Main Article Content

Eva Kastenbauer (Control Systems Group, Technische Universität Berlin), Eira Lotta Spieker (Department of Neurology, Charité-Universitätsmedizin Berlin), Christina Salchow-Hömmern (Department of Neurology, Charité-Universitätsmedizin Berlin), Nikolaus Wenger (Department of Neurology, Charité-Universitätsmedizin Berlin), Thomas Schauer (Control Systems Group, Technische Universität Berlin)


Spinal cord injury and multiple sclerosis can affect a patient’s walking ability and can be accompanied by spasticity. Transcutaneous spinal cord stimulation (tSCS) aims at reduction of spasticity and improvement of locomotion. This work investigates a machine learning approach to evaluate a chosen dorsal electrode position. If the position is classified as unsuitable for therapy, a recommendation for displacement is made. Classified EMG data of the posterior root muscles, evoked by a series of double-stimulation pulses with increasing intensity, and anthropometric data from 18 subjects were used to train a decision tree classifier. An average accuracy of 78% was observed.

Article Details

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.