Video-Based Quantitative Assessment of Upper Limb Impairments in Patients with Brain Lesions During Resistance Exercises

This study proposes a video-based approach for quantitatively evaluating upper-limb joint abnormalities in individuals with brain lesions during resistance exercises. While the Fugl–Meyer Assessment (FMA) is a reliable clinical tool, its use is limited by the need for expert involvement and repeated assessments. To address this issue, skeletal joint data were extracted from RGB exercise videos using OpenPose, and joint abnormalities were identified by learning normal movement patterns from non-disabled participants. A total of 26 non-disabled individuals and 12 individuals with brain lesions performed chest press, shoulder press, and arm curl exercises. Joint movement patterns were analyzed using correlation analysis and a long short-term memory (LSTM) autoencoder. Only joints relevant to each exercise were evaluated, and joint-level results were integrated to compute arm-level abnormality rates. The correlation-based abnormality rate showed a significant negative correlation with FMA scores (r = −0.7789, p = 2.83 × 10−3), while the LSTM autoencoder-based abnormality rate exhibited a stronger correlation(r = −0.8454, p = 5.33 × 10−4). In addition, affected-side classification accuracy reached 78.0% and 83.3% for correlation analysis and the LSTM autoencoder, respectively. These results indicate that the proposed method is consistent with clinical assessments and can serve as a non-invasive, cost-effective tool for video-based rehabilitation evaluation.