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

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.