Hybrid YOLOv8-seg Model for Hand Gesture Segmentation

Parkinson’s Disease (PD) is a neurological disorder that causes patients with Parkinson’s Disease (PPD) to have difficulty with body balancing. Thus, PPD rely on caretakers to fulfill their daily needs. Vision-based assistive systems could be useful for PPD to communicate with caretakers. The work presented here is a hybrid YOLOv8n-seg framework with DETR. In this framework, the traditional YOLOv8n-seg model’s head is replaced with DETR as the head for hand gesture (HG) segmentation for PPD. Since no public dataset exists for PPD gestures, a dataset of 4,583 raw hand gesture images was collected with a webcam under realistic home and clinical environments (such as poor light, cluttered background, and motion blur) and expanded via augmentation to 11,230 gestures. This dataset was divided into an 80% training set, a 15% validation set, and a 5% testing set with 9 classes (e.g., hungry, attention, call, toilet) to ensure robust evaluation. The baseline YOLOv8n-seg model and Transformer-based variant, DETR (DEtection TRansformer), were tested on the custom PPD hand gesture dataset. Compared to the baseline YOLOv8n-seg, the implemented hybrid model achieved superior performance across all evaluation metrics, with ~1% improvement in precision (99% vs. 98%), recall (97% vs. 96%), F1 score (98% vs. 97%), and dice score (98% vs. 97%), with almost the same mAP@50 (97% vs. 97% for all), while improving inference speed by +3.0% (55.1 FPS vs. 53.5 FPS). On the same custom dataset, the conventional U-Net achieved 88% precision, 92% recall, and a 0.9 dice score, whereas the proposed hybrid model reached 99% precision, 98% recall, and a 0.98 dice score. This confirms the superior performance of the hybrid model over the conventional U-Net architecture for HG segmentation. The Raspberry Pi 4B is used as an edge device for HGR of PPD. These enhancements demonstrate that the hybrid approach achieves both higher accuracy and faster real-time performance, which is useful for assistive systems deployment on the embedded edge device. To our knowledge, this is the first work combining YOLOv8n-seg with a DETR head for PPD hand gesture segmentation.