Application of AI Products in Wearable Health Monitoring Devices

Abstract

Review of the application of artificial intelligence (AI) in wearable health-monitoring devices and analysis of their development from basic activity monitors to clinical diagnostic instruments. Wearable sensors can collect continuous health data now; therefore, machine learning algorithms need to be introduced to process this complex, high-dimensional data. Analyze the particular algorithmic structures in wearable ecosystems for this study, including supervised learning for anomaly detection and deep learning for time-series forecasting. Review of practical clinical applications: cardiovascular continuous monitoring, management of metabolic disorders and early diagnosis of neurological diseases. In addition, this paper will also investigate the technical and regulatory problems of wearable artificial intelligence, such as data privacy, system interoperability and algorithmic bias. Optimisation strategies are proposed in the paper, such as edge computing and federated learning, to process data locally and reduce privacy risks while maintaining model accuracy. Based on the above analysis, a unified regulatory system and transparent machine-learning protocols need to be established for the incorporation of wearable AI into official medical institutions.