Multimodal Spatiotemporal Feature-Based Human Motion Pattern Recognition With CNN-Transformer-Attention Framework

Human motion pattern recognition plays a crucial role in applications such as navigation and positioning, health monitoring, disease prevention, smart healthcare, military secu-rity, and human-computer interaction. However, existing recogni-tion methods often face challenges such as reliance on a single sen-sor type, limited data richness, inadequate spatiotemporal feature extraction, and suboptimal algorithm structures, which result in low detection accuracy, limited recognition categories, and poor algorithm robustness. To address these limitations, we propose a novel human motion pattern recognition method based on a CNN-Transformer-Attention framework that leverages multimodal spatiotemporal features. We first developed a lightweight, cost-ef-fective wearable system capable of real-time data collection from multiple body parts (wrist, chest, and foot) using accelerometers, gyroscopes, and magnetometers. The sensor data from different body parts were temporally synchronized using interpolation, fil-tered to reduce noise, and analyzed in the frequency domain to ex-tract precise and useful multimodal spatiotemporal features. Sub-sequently, we designed a CNN-Transformer-Attention framework that integrates multimodal spatiotemporal information enhance-ment strategies for accurate motion pattern detection. Experi-mental results demonstrate that the collaborative enhancement of multi-body sensor data achieves a motion pattern recognition ac-curacy exceeding 98%, outperforming single-sensor systems (wrist, chest, and foot-mounted devices) by 7.98%, 0.43%, and 5.59%, respectively. Furthermore, when compared to models such as BPNN, CNN, LSTM, Transformer and CNN-BiGRU, our pro-posed framework exhibits superior accuracy and generalization capabilities.