Shinichi Shimada

The Fugl–Meyer Assessment (FMA) and Range of Motion (ROM) are well-known measures for evaluating finger motor function. However, the finger FMA has a narrow scoring range, and goniometer ROM measurements lack reliability. Therefore, we investigated ROM measurement using a 3D motion capture system (Ultraleap Stereo IR 170, Ultraleap Ltd., Bristol, UK, hereinafter referred to as Ultraleap). This study aimed to evaluate the intra- and inter-rater reliability of finger Active ROM (A-ROM) using Ultraleap, and its criterion-related validity compared to a goniometer, in stroke patients with upper limb hemiplegia. A-ROM was evaluated over a 3-day period. For Ultraleap reliability, flexion intraclass correlation coefficients (ICCs) showed variability (intra-rater: 0.576–0.896; inter-rater: 0.426–0.841), whereas extension ICCs were ≥0.81 for all joints except the thumb interphalangeal (IP) joint. Regarding criterion-related validity, correlation coefficients ranged from −0.0706 to 0.493 for flexion, and 0.698 to 0.814 for extension (excluding the thumb IP joint). Ultraleap extension measurements demonstrated acceptable-to-high reliability and moderate-to-strong correlations with manual goniometry in most joints, except for the thumb IP joint. However, Bland–Altman analyses revealed systematic and proportional biases, indicating limited agreement for absolute ROM values. While the system cannot be directly interchangeable with manual goniometry for absolute ROM assessment, it may serve as an efficient supplementary tool for repeated measurements of finger extension in clinical settings.

The Fugl–Meyer Assessment (FMA) has been used as a functional assessment of upper-limb function in stroke patients. This study aimed to create a more objective and standardized evaluation based on an FMA of the upper-limb items. A total of 30 first-ever stroke patients (65.3 ± 10.3 years old) and 15 healthy participants (35.4 ± 13.4 years old) admitted to Itami Kousei Neurosurgical Hospital were included. A nine-axis motion sensor was attached to the participants, and the joint angles of 17 upper-limb items (excluding fingers) and 23 FMA upper-limb items (excluding reflexes and fingers) were measured. From the measurement results, we analyzed the time-series data of each movement and obtained the correlation between the joint angles of each part. Discriminant analysis showed that 17 and 6 items had a concordance rate of ≥80% (80.0~95.6%) and <80% (64.4~75.6%), respectively. In the multiple regression analysis of continuous variables of FMA, a good regression model was obtained to predict the FMA with three to five joint angles. The discriminant analysis for 17 evaluation items suggests the possibility of roughly calculating FMA scores from joint angles.