[The instrumental assessment of visuomotor coordination of stroke patients after neurorehabilitation]
Year: 2025
Type: article
Study design: Observational Study
Abstract: Instumented movement analysis allows to obtain quantitative and hence objective data about the mobility and the visuomotor coordination of patients with stroke. It also provides the possibility to predict the outcome of neurorehabilitation even in terms of identification of patients able to return to work. The wide amount of data provided by instrumental movement analysis could be an opportunity on one hand, but also required a great computational effort on the other hand. The development of artificial neural network could help clinicians in managing these data. In this study we analysed the visuomotor coordination of 16 patients with stroke and 18 age-matched healthy subjects during a task separately performed by each upper limb and assessing the asymmetry index for each one of the parameters extracted by an inertial measurement unit placed on the dorsal part of the hand. The artificial neural network showed an accuracy of 94.1% in identifying the subjects able to work. The combined used of light wearable sensors developed for human movement analysis and artificial neural networks could provide an accurate prognosis of the possibility to return to work of patients with stroke after neurorehabilitation and probably also of the monitoring of working activities in terms of ergonomics.
DOI: 10.4081/gimle.545
Authors: Marco Iosa, Edda Capodaglio, Benedetta Persechino, Silvia Pelà, Sheida Ghambari Ghooshchy, Marco Tramontano, Giovanni Morone, Stefano Paolucci, Domenico Antonio De Angelis, Monica Panigazzi
Language: English
Subfield: Rehabilitation
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4407779160