IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis

Sci Rep. 2026 Feb 28;16(1):11441. doi: 10.1038/s41598-026-41195-x.

ABSTRACT

The integration of wearable sensors and IoT technology provides new technical means for sports activity monitoring. However, existing solutions still have deficiencies in joint spatiotemporal feature modeling, edge-side lightweight deployment, and secure transmission of sensitive data. This paper proposes an IoT sports activity monitoring framework based on CRNN spatiotemporal analysis and secure transmission mechanisms, adopting a layered architecture to achieve end-to-end processing from data acquisition to intelligent services. The spatiotemporal analysis module extracts spatial features of motion signals through convolutional neural networks, models temporal dependencies using long short-term memory networks, and introduces self-attention mechanisms to enhance the representation capability of key motion patterns. Addressing the resource constraints of edge devices, 8-bit quantization and knowledge distillation techniques are employed for lightweight model compression, reducing parameters from 2.34 M to 0.58 M with embedded inference latency of only 47.3ms. The secure transmission module adopts AES-128 encryption algorithm and HMAC-SHA256 message authentication code to ensure confidentiality and integrity of data transmission. A comparative analysis of encryption-based security and data-driven privacy protection methods such as differential privacy is provided, clarifying the applicability and complementary relationships of different privacy protection strategies in resource-constrained wearable scenarios. Experimental results on four datasets (UCI-HAR, PAMAP2, WISDM, and self-built sports activity dataset) demonstrate that the proposed method achieves an average recognition accuracy of 95.99%, improving by 2.85 to 3.52% points over baseline methods. The end-to-end latency of secure transmission is 23.6ms, with energy consumption increase controlled within 12.4%. This framework provides a feasible technical solution for the design of intelligent sports monitoring systems with promising application prospects.

PMID:41764268 | PMC:PMC13056921 | DOI:10.1038/s41598-026-41195-x