Cross-Domain Gesture Recognition via Wrist-Worn Sensing for Resource-Efficient IoT Applications
Shuo Zhang, Ying Ding, Jin Qi, Chengan Hong, Weijie Liu, Duidi Wu, Qianyou Zhao, Jie Hu · IEEE Internet of Things Journal · 2025
Wearable sensing has gained considerable attention for its effectiveness in gesture recognition, offering convenient, natural, and efficient interaction in human-machine systems. However, practical deployment in resource-constrained settings remains challenging due to data-intensive acquisition demands, high training overhead, and limited computing capacity—factors that hinder large-scale applicability. To address these issues, we proposed a lightweight model for fine-grained gesture recognition under sparse-site sensing conditions. Experiments were conducted on a custom multimodal dataset comprising 15 static and 18 dynamic gestures. Modal assessments across diverse gesture types revealed the complementarity of wrist surface electromyography and kinematic signals, validating the effectiveness of multimodal fusion. In addition, we leveraged transfer learning to improve model generalization to unseen subjects while reducing data acquisition burden and computational overhead. To achieve the Accuracy-Cost trade-off, we formulated an objective function and validated its generalizability across diverse sensing configurations, hardware platforms, and standardization strategies. Experimental results showed that an average accuracy sacrifice of 3.8% can yield substantial resource savings, with an average reduction of 87.8% in data collection and fine-tuning time, facilitating resource-efficient allocation of gesture recognition systems in Internet of Things (IoT) environments. Finally, an online gesture recognition prototype was developed and evaluated, demonstrating the method’s robustness and practical utility in cross-domain transfer scenarios. These findings provide both theoretical and practical foundations for deploying gesture recognition-based rehabilitation systems within IoT settings.