ArticleSensors (Basel, Switzerland)2021
Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Automated EMG-Based Classification of Upper Extremity Motor Impairment Levels in Subacute Stroke.Sensors (Basel, Switzerland) · 2025Article
- Data-Driven Quantitation of Movement Abnormality after Stroke.Bioengineering (Basel, Switzerland) · 2023Article
- Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children.Sensors (Basel, Switzerland) · 2022Article
- Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot.Sensors (Basel, Switzerland) · 2022Article
- An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.Frontiers in neurorobotics · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
In rehabilitation, the Fugl-Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.
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Registered trials
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