Score rectification for online assessments in robot-assisted arm rehabilitation
| Autor: | Michael Sommerhalder, Yves Zimmermann, Manuel Knecht, Zelio Suter, Robert Riener, Peter Wolf |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | at-Automatisierungstechnik, 70 (11) Special issue: AUTOMED 2021: Automation in Medical Technology |
| ISSN: | 0178-2312 2196-677X |
| Popis: | Relative comparison of clinical scores to measure the effectiveness of neuro-rehabilitation therapy is possible through a series of discrete measurements during the rehabilitation period within specifically designed task environments. Robots allow quantitative, continuous measurement of data. Resulting robotic scores are also only comparable within similar context, e.g. type of task. We propose a method to decouple these scores from their respective context through functional orthogonalization and compensation of the compounding factors based on a data-driven sensitivity analysis of the user performance. The method was validated for the established accuracy score with variable arm weight support, provoked muscle fatigue and different task directions on 6 participants of our arm exoskeleton group on the ANYexo robot. In the best case, the standard deviation of the assessed score in changing context could be reduced by a factor of 3.2. Therewith, we paved the way to context-independent, quantitative online assessments, recorded autonomously with robots. at - Automatisierungstechnik, 70 (11) ISSN:0178-2312 ISSN:2196-677X Special issue: AUTOMED 2021: Automation in Medical Technology |
| Databáze: | OpenAIRE |
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