Abstract
As wearables are becoming an increasingly important part of wellness and everyday life for many people, their potential in healthcare is also expanding, particularly in personalized and remote healthcare. However, many wearables lack sophistication, relying on simple sensors such as accelerometers and pulse meters to measure heart rate, body composition, and daily activity. Such basic metrics are insufficient for musculoskeletal disease diagnosis, which requires more detailed, multimodal neuromusculoskeletal monitoring. A major challenge in wearables development is the need for precise electromechanical signal measurements, which are difficult to obtain with low-cost systems. Artificial intelligence (AI) holds promise in addressing these analytical challenges and enabling the creation of affordable, sophisticated wearables. While AI has been used for decades in engineering, its clinical application is still emerging, creating an opportunity for the development of AI-enhanced wearables capable of clinical diagnosis. AI can enhance data generated by various sensor types in wearable devices (such as accelerometers, electrical, optical, and acoustic sensors), enabling clinicians to monitor and diagnose complex conditions that require multiple sensing modalities. This review explores current wearable technologies, ongoing research in AI-enhanced wearables, the potential for AI to advance wearable technologies in healthcare, and the future directions in the development of multimodal wearables.