With the increasing maturity of contactless human pose recognition (HPR) technology, indoor interactive applications have raised higher demands for natural, controller-free interaction methods. However, current mainstream HPR solutions relying on vision or radio-frequency (RF) (including WiFi, radar) still face various challenges in practical deployment, such as privacy concerns, susceptibility to occlusion, dedicated equipment and functions, and limited sensing resolution and range. 5G-based integrated sensing and communication (ISAC) technology, by merging communication and sensing functions, offers a new approach to address these challenges in contactless HPR. We propose a practical 5G-based ISAC system capable of inferring 2D HPR from uplink sounding reference signals (SRS). Specifically, rich features are extracted from multiple domains and employ an encoder to achieve unified alignment and representation in a latent space. Subsequently, low-dimensional features are fused to output the human pose state. Experimental results demonstrate that in typical indoor environments, our proposed 5G-based ISAC HPR system significantly outperforms current mainstream baseline solutions in HPR performance, providing a solid technical foundation for universal human-computer interaction.

翻译：随着非接触式人体姿态识别技术日益成熟，室内交互应用对自然、无控制器的交互方式提出了更高要求。然而，当前依赖视觉或射频（包括WiFi、雷达）的主流人体姿态识别解决方案在实际部署中仍面临诸多挑战，如隐私担忧、易受遮挡、专用设备与功能限制，以及感知分辨率与范围有限等。基于5G的集成感知与通信技术通过融合通信与感知功能，为解决非接触式人体姿态识别中的这些挑战提供了新途径。我们提出了一种实用的基于5G的集成感知与通信系统，能够从上行探测参考信号中推断二维人体姿态。具体而言，系统从多域提取丰富特征，并采用编码器在潜空间实现统一对齐与表征。随后，通过融合低维特征输出人体姿态状态。实验结果表明，在典型室内环境中，我们提出的基于5G的集成感知与通信人体姿态识别系统在性能上显著优于当前主流基线解决方案，为普适人机交互提供了坚实的技术基础。