Hand Role

Understanding hand roles in bimanual tasks is crucial for advancing robotics, rehabilitation, and human-computer interaction. Current research focuses on identifying and classifying hand use (e.g., manipulation vs. stabilization) and roles during activities of daily living, often employing computer vision techniques like random forest classifiers and neural networks (e.g., SlowFast, Hand Object Detector) to analyze egocentric video data. These studies aim to improve the assessment of hand function, particularly after stroke, and inform the design of assistive technologies. Accurate identification of hand roles promises to enhance the effectiveness of rehabilitation interventions and improve the design of more intuitive and effective human-machine interfaces.