Comparative Analysis of Gait Features and Freezing of Gait Indicators for Video-Based Parkinson’s Disease Detection

Parkinson’s disease (PD) causes motor control deficiencies, resulting in gait irregularities such as shorter strides, slower walking speed, and irregular step timing. This study presents a video-based deep learning approach for PD classification that extracts skeletal keypoints from Timed Up and Go (TUG) test videos using AlphaPose and the COCO-17 representation. A total of 24 features were generated, comprising 23 conventional gait features and one Freezing of Gait (FoG) feature derived from frequency-domain analysis of ankle velocity signals. This FoG feature was not validated against clinician-confirmed FoG episodes and should be interpreted as a frequency-domain proxy rather than a diagnostic measure. Three feature selection procedures and four LSTM-based architectures were evaluated across full, walking, and turning segments. Experimental results on a self-collected TUG dataset showed that the Standalone FI configuration achieved the numerically highest test accuracy of 77.78% on the turning segment among the evaluated LSTM configurations, while conventional features achieved 66.67% on both the full and walking segments. These test-set metrics provide descriptive estimates derived from a static subject-level test division involving six held-out participants (excluded from training and validation) and should not be viewed as statistically dependable indicators of clinical performance at the population level; in addition, gait-cycle boundaries were not independently validated and fallback usage was not quantified. Turning segments demonstrated higher discriminative power than straight-walking segments. Zero-shot cross-dataset evaluation on Turn-REMAP and PD-Walk revealed a substantial generalization gap, with accuracy falling to 55.12% and 49.53%, respectively, indicating that the present model is not yet suitable for cross-site clinical deployment without adaptation or calibration. The proposed framework provides systematic insights into the comparative role of conventional and FoG-derived gait parameters for non-invasive video-based PD screening.