Personalized Machine Learning for Exercise Recovery Assessment Using Continuous Wearable Sensor Data

Abstract

Exercise recovery assessment is a central but under-instrumented component of health management, athletic training, and clinical rehabilitation. Subjective readiness questionnaires and infrequent laboratory tests cannot capture the continuous physiological dynamics that determine whether an individual is adequately recovered for subsequent exertion. Wearable sensors now offer an unprecedented opportunity to monitor movement, cardiac activity, respiration, sleep, and related signals over long periods. Personalized machine learning can transform these noisy, heterogeneous, and contextual data streams into meaningful recovery estimates. However, translating algorithmic potential into dependable real-world infrastructure raises a distinct set of systems challenges. This paper presents a cross-disciplinary analysis of personalized machine learning for exercise recovery assessment as a socio-technical infrastructure problem. It examines sensor data acquisition, edge and cloud architectural trade-offs, personalization strategies, model interpretability, robustness, fairness, governance, and long-term sustainability. Rather than proposing a narrow model architecture, the paper argues that effective recovery assessment requires an integrated pipeline in which physiological signal processing, adaptive learning, privacy-preserving aggregation, and human-centered oversight are jointly designed. Structural trade-offs include the balance between local low-latency inference and global model quality, between individual adaptation and population-level generalization, and between rich physiological modeling and energy-efficient deployment. The discussion further addresses fairness risks arising from sensor heterogeneity and demographic underrepresentation, as well as the governance requirements for safe clinical and consumer use. The paper concludes that sustainable personalized recovery systems will depend less on isolated algorithmic gains than on the deliberate alignment of data infrastructure, model lifecycle management, regulatory accountability, and equitable access.