English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Health personnel are often unavailable for supervised robot-aided neurorehabilitation in hospitals, and patients are usually challenged by transportation issues to get to the hospital. Thus, a discontinuity between therapy in the hospital and at home appears slowing down the upper extremity (UE) mobility recovery. The aim of this work was to develop a system, based on wearable devices and EMG armbands, able to assess the quality of the UE joint movements and intelligently guide the patients during home-based rehabilitation. This system fuses a classification model together with a dynamic time-warping (DTW) analysis. The classification model was trained with UE joint movements gathered from clinicians, obtaining more than 80&#x0025; accuracy using only five joint angles. Then, the system was tested on two poststroke patients and a healthy subject. The results suggest that the proposed system can be: 1) a useful tool for clinicians to evaluate the rehabilitation therapy and 2) an intelligent system able to make decisions based on the quality of the activity executed at home.

Feasibility of an Intelligent Home-Based Neurorehabilitation System for Upper Extremity Mobility Assessment