Deep Learning For Signal Processing in Athlete Activity Sensor Systems

Abstract

Integrating deep learning into athlete activity sensor systems offers transformative potential for understanding and enhancing human performance. By interpreting multidimensional time-series data from wearable sensors, these systems enable real-time monitoring, adaptive feedback, and data-driven decision-making across diverse athletic contexts. Signal preprocessing, feature extraction, and spatial-temporal modelling form the foundation for accurate pattern recognition, skill evaluation, and fatigue tracking. Convolutional and recurrent neural architectures contribute unique capabilities for handling localized and sequential dependencies, while hybrid models improve generalizability and resilience. Practical deployment involves harmonizing hardware design with software optimization, supported by robust model deployment strategies. Case studies demonstrate successful applications in elite training, rehabilitation, and fitness contexts, highlighting system scalability and personalization. Emerging directions emphasize privacy-aware federated learning and multimodal fusion for holistic performance assessment. Ethical concerns related to consent, data security, and algorithmic fairness remain critical to responsible innovation. As these technologies evolve, athlete monitoring systems become increasingly adaptive, collaborative, and human-centred. This chapter explores these innovations comprehensively, offering a detailed framework for future research and implementation.