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.
Highlights
In rehabilitation, upper-extremity motor function evaluation for stroke survivors is important to plan effective rehabilitation intervention [1,2]
From our first attempt to apply a sensor-enabled body tracking to the Fugl–Meyer assessment (FMA) automation [17], we recently reported a sensorbased automated FMA system with a rule-based expert with binary logics originated from the linguistic grading guideline of FMA, which is different to the machine learning methods used in the other existing works [13]
The results showed that FM scale due to the scoring algorithm (FMCA) could be interpreted up to seven scale in all the target FMA tests (Table 6), which implies that motor function could be more sensitively evaluated than the conventional three point FM scale
Summary
Upper-extremity motor function evaluation for stroke survivors is important to plan effective rehabilitation intervention [1,2]. FMA is (1) labor-intensive and time-consuming, and (2) not sensitive enough to fine changes in motor function ability due to the coarse three point grading scheme of the FM scale [4]. This grading scheme results in high inter/intra-rater reliability, it has lower sensitivity than other clinical instruments, such as the medical research council muscle strength scale (six point scale) [4,5,6]. It might be because most work to automate FMA showed inadequate accuracy even though the work focused on predicting the original three point FM scale. Another reason would be that the machine learning methods used in the existing works for FMA are not appropriate to handle this issue because (1) some of them
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