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Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., et al. (2022) Multi-Sensor Information Fusion Based on Machine Learning for Real Applications in Human Activity Recognition: State-of-the-Art and Research Challenges. Information Fusion, 80, 241-265.
https://doi.org/10.1016/j.inffus.2021.11.006

has been cited by the following article:

• TITLE: Integrated Wearable Sensor Technologies for Real-Time Sports Performance Evaluation

  AUTHORS: Pravakshya Srivastava, Sananjay Biswas

  KEYWORDS: Wearable Sensors, Sports Performance Monitoring, Multimodal Sensing, Artificial Intelligence, Machine Learning

  JOURNAL NAME: Wireless Sensor Network, Vol.18 No.2, September 16, 2026

  ABSTRACT: Wearable sensor technologies have revolutionized sports performance evaluation by enabling continuous, non-invasive, and real-time monitoring of athletes during training and competition. This narrative review synthesizes recent advances in integrated wearable sensor technologies for sports performance assessment, focusing on multimodal sensing, intelligent data integration, and artificial intelligence (AI)-driven analytics. The review covers key wearable sensor types, including biomechanical, physiological, biochemical, and textile-based sensors, along with their integration into smart wearable platforms supported by wireless communication and Internet of Things (IoT) technologies. Recent developments in machine learning for performance optimization, fatigue detection, injury prevention, and personalized training are also discussed. The findings highlight that integrating multiple sensing modalities with AI significantly enhances the accuracy and effectiveness of athlete monitoring. However, challenges such as motions, power limitations, user comfort, data privacy, and standardization remain. Future research should emphasize self-powered wearable systems, flexible electronics, edge AI, digital twins, and secure data management to enable next-generation intelligent sports monitoring.