Using Marker-less Pose Estimation for the Detection and Classification of FES-induced Tremor

Tremor is a significant movement disorder characterized by involuntary, rhythmic, oscillatory movement of body parts. Traditional methods for tremor detection and analysis rely on visual evaluation and the use of rating scales. However, marker-less pose estimation (MPE) holds great potential for advancing tremor research by enabling the collection of objective and feature-rich data using a simple camera-based setup. This article aims to demonstrate the applicability of MPE for the extraction of relevant tremor features from hand kinematics and the automation of tremor detection and classification. We conducted an experiment involving three healthy subjects performing movements while a weak tremor was induced with functional electrical stimulation (FES). From multi-perspective camera data, we computed the trajectories of 20 key-points of the hand using the marker-less estimator Anipose. After extracting features from these key-point trajectories we trained machine-learning models to assess their validity in differentiating between tremor and non-tremor signals (detection) and between intention and constant tremor (classification). Despite a small intensity of FES-induced tremor, our system could detect tremor with 70.73% accuracy and classify between intention tremor and non-intention tremor with 79.28% accuracy. In conclusion, this research provides a foundation for the development of an MPE-based method for automated tremor assessment at home, using simple camera-based equipment.