Transforming Healthcare with Motion Capture and AI

Abstract

This chapter presents a comprehensive analysis of the fundamental shift in healthcare driven by the convergence of high-fidelity motion capture (MC) and artificial intelligence (AI). This review is intended for clinicians who recognize the potential benefits of digital motion analysis but seek evidence supporting its application in a wider range. The text details the transition from subjective, episodic clinical observation to objective, continuous, and predictive monitoring. It analyzes the data acquisition process via hardware equipment, highlighting the contrast between methods such as marker-based systems (MBS), inertial measurement units (IMUs), and markerless MC (MMC). Furthermore, it invites readers to explore the new possibilities of “AI engines” (convolutional neural networks (CNNs), long short-term memories (LSTMs), Graph Neural Networks) that translate raw sensor data into clinically significant insights. Applications of these new medical possibilities are examined across a holistic approach to patient care, including sectors such as early diagnosis of Parkinson&s;s disease, remote rehabilitation, and the assessment of surgical skills. Finally, we provide readers with a comprehensive and objective understanding of the variety of challenges regarding validation and privacy issues that must be overcome to fully realize the new horizons of this data-driven revolution in modern healthcare.