Ai-Guided Wearable Technologies and Smart Healthcare Utility in Physiotherapy

Abstract Physiotherapy is moving from an episodic, clinic-centred model of care towards a continuous, data-informed and patient-centred model. Wearable technologies, which include inertial measurement units, surface electromyography sensors, smart textiles, photoplethysmography-based devices, pressure-sensing insoles and smart garments, now capture movement, muscle activity, physiological load and adherence data outside the clinic. When these data streams are interpreted by artificial intelligence (AI), including machine learning and deep learning, they can be transformed into clinically meaningful information such as exercise quality, gait deviations, fall risk, fatigue, pain-related behaviour and recovery trajectories. This manuscript reviews the technological foundations of AI-guided wearables, describes their utility across musculoskeletal, neurological, cardiopulmonary, sports, geriatric and paediatric physiotherapy, and explains how they fit within a smart healthcare ecosystem built on the Internet of Medical Things, cloud analytics and tele-rehabilitation. The potential benefits include objective assessment, personalised dosing of exercise, improved adherence, remote supervision, early detection of deterioration and wider access to rehabilitation, particularly for under-served populations. The paper also examines the principal limitations: sensor validity, algorithmic bias, data privacy, regulatory uncertainty, cost, digital literacy and the risk of de-skilling. A five-layer implementation framework and a set of recommendations for clinicians, educators, researchers and policy makers are proposed. It is concluded that AI-guided wearables should be positioned as decision-support tools that extend, and never replace, the clinical reasoning and therapeutic relationship at the centre of physiotherapy practice.