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LIU Miao, ZENG Xiaolu, YANG Xiaopeng, et al. Wi-Fi-based indoor human pose estimation using a pyramid dilated convolutional residual network[J]. Journal of Radars, in press. doi: 10.12000/JR26024
Citation: LIU Miao, ZENG Xiaolu, YANG Xiaopeng, et al. Wi-Fi-based indoor human pose estimation using a pyramid dilated convolutional residual network[J]. Journal of Radars, in press. doi: 10.12000/JR26024
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Wi-Fi-based Indoor Human Pose Estimation Using a Pyramid Dilated Convolutional Residual Network

DOI: 10.12000/JR26024 CSTR: 32380.14.J26024
  • 1.

    School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314000, China

Funds:  The National Natural Science Foundation of China (62301042), The National Leading Talents in Scientific and Technological Innovation Program (3050013532502)
More Information
  • Corresponding author: ZENG Xiaolu, xlzeng09@bit.edu.cn
  • Received Date: 2026-01-19
  • Rev Recd Date: 2026-04-14
    • Available Online: 2026-04-21
    Abstract
  • Human pose estimation allows for precise capture of movement and behavioral traits, holding significant potential for applications such as intelligent surveillance, human-computer interaction, and health monitoring. Among emerging approaches, Wi-Fi sensing has gained increasing research interest for contactless human pose detection because of its widespread availability, affordability, and privacy-preserving qualities. However, human activities are multiscale, nonlinear, and highly dynamic, with notable spatiotemporal variations in motion amplitude across different body parts. These characteristics pose high demands on the ability of algorithms to model multiscale features effectively. Current Wi-Fi-based techniques often struggle with excessive parameter complexity and limited feature extraction, which makes it hard to balance computational speed with accuracy in complex situations. To address these issues, this paper introduces a pyramid dilated convolution block that expands the receptive field while maintaining spatial resolution, making it possible to capture multiscale spatial and dynamic details efficiently. The dilated design also lessens computational redundancy, improving overall efficiency. Building on this, a residual network is designed to prevent gradient vanishing and model degradation, ensuring solid feature representation in deep networks. To test the proposed method, a comprehensive multisource data system was built to synchronize Wi-Fi pose data with ground-truth labels. Experimental results show the proposed approach’s superiority, reaching a mean percentage of correct keypoints (MPCK@0.1) of 94.96%, surpassing current leading algorithms. These results confirm the method’s effectiveness for reliable and efficient human pose estimation.

     

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    • References(30)
  • [1]
    CAO Zhe, HIDALGO G, SIMON T, et al. OpenPose: Realtime multi-person 2D pose estimation using part affinity fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(1): 172–186. doi: 10.1109/TPAMI.2019.2929257.
    [2]
    TOSHEV A and SZEGEDY C. DeepPose: Human pose estimation via deep neural networks[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 1653–1660. doi: 10.1109/CVPR.2014.214.
    [3]
    MEHRABAN S, ADELI V, and TAATI B. MotionAGFormer: Enhancing 3D human pose estimation with a Transformer-GCNformer network[C]. The IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2024: 6905–6915. doi: 10.1109/WACV57701.2024.00677.
    [4]
    AN Xiaoqi, ZHAO Lin, GONG Chen, et al. ShaRPose: Sparse high-resolution representation for human pose estimation[C]. The AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2024: 691–699.
    [5]
    ZHAO Mingmin, LI Tianhong, ABU ALSHEIKH M, et al. Through-wall human pose estimation using radio signals[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7356–7365. doi: 10.1109/CVPR.2018.00768.
    [6]
    ZHENG Zhijie, ZHANG Diankun, LIANG Xiao, et al. RadarFormer: End-to-end human perception with through-wall radar and transformers[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(12): 18285–18299. doi: 10.1109/TNNLS.2023.3314031.
    [7]
    ZHANG Rui, GENG Ruixu, LI Yadong, et al. RFMamba: Frequency-aware state space model for RF-based human-centric perception[C]. The Thirteenth International Conference on Learning Representations, Singapore, Singapore, 2025.
    [8]
    SENGUPTA A and CAO Siyang. mmPose-NLP: A natural language processing approach to precise skeletal pose estimation using mmWave radars[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(11): 8418–8429. doi: 10.1109/TNNLS.2022.3151101.
    [9]
    SENGUPTA A, JIN Feng, ZHANG Renyuan, et al. mm-Pose: Real-time human skeletal posture estimation using mmWave radars and CNNs[J]. IEEE Sensors Journal, 2020, 20(17): 10032–10044. doi: 10.1109/JSEN.2020.2991741.
    [10]
    陈彦, 张锐, 李亚东. 等. 基于无线信号的人体姿态估计综述[J]. 雷达学报(中英文), 2025, 14(1): 229–247. doi: 10.12000/JR24189.

    CHEN Yan, ZHANG Rui, LI Yadong, et al. An overview of human pose estimation based on wireless signals[J]. Journal of Radars, 2025, 14(1): 229–247. doi: 10.12000/JR24189.
    [11]
    MA Yongsen, ZHOU Gang, and WANG Shuangquan. WiFi sensing with channel state information: A survey[J]. ACM Computing Surveys (CSUR), 2020, 52(3): 46. doi: 10.1145/3310194.
    [12]
    WEI Bo, SONG Hang, KATTO J, et al. RSSI–CSI measurement and variation mitigation with commodity Wi-Fi device[J]. IEEE Internet of Things Journal, 2023, 10(7): 6249–6258. doi: 10.1109/JIOT.2022.3223525.
    [13]
    HALPERIN D, HU Wenjun, SHETH A, et al. Tool release: Gathering 802.11n traces with channel state information[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(1): 53. doi: 10.1145/1925861.1925870.
    [14]
    WANG Fei, ZHOU Sanping, PANEV S, et al. Person-in-WiFi: Fine-grained person perception using WiFi[C]. The IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 5451–5460. doi: 10.1109/ICCV.2019.00555.
    [15]
    HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. The IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980–2988. doi: 10.1109/ICCV.2017.322.
    [16]
    RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. The 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Munich, Germany, 2015: 234–241. doi: 10.1007/978-3-319-24574-4_28.
    [17]
    YANG Jianfei, ZHOU Yunjiao, HUANG He, et al. MetaFi: Device-free pose estimation via commodity WiFi for metaverse avatar simulation[C]. The IEEE 8th World Forum on Internet of Things, Yokohama, Japan, 2022: 1–6. doi: 10.1109/WF-IoT54382.2022.10152057.
    [18]
    ZHOU Yue, ZHU Aichun, XU Caojie, et al. PerUnet: Deep signal channel attention in UNet for WiFi-based human pose estimation[J]. IEEE Sensors Journal, 2022, 22(20): 19750–19760. doi: 10.1109/JSEN.2022.3204607.
    [19]
    DENG Jie, CHEN Kaiqi, JING Pengsen, et al. CSI-channel spatial decomposition for WiFi-based human pose estimation[J]. Electronics, 2025, 14(4): 756. doi: 10.3390/electronics14040756.
    [20]
    ZHOU Yunjiao, HUANG He, YUAN Shenghai, et al. MetaFi++: WiFi-enabled transformer-based human pose estimation for metaverse avatar simulation[J]. IEEE Internet of Things Journal, 2023, 10(16): 14128–14136. doi: 10.1109/JIOT.2023.3262940.
    [21]
    JIANG Wenjun, XUE Hongfei, MIAO Chenglin, et al. Towards 3D human pose construction using WiFi[C]. The 26th Annual International Conference on Mobile Computing and Networking, London, UK, 2020: 23. doi: 10.1145/3372224.3380900.
    [22]
    GIAN T D, TRAN D T, PHAM Q V, et al. Multi-modal human pose estimation: A Wi-Fi-driven approach with adaptive kernel selection[J]. IEEE Transactions on Artificial Intelligence, 2025. doi: 10.1109/TAI.2025.3631005.
    [23]
    GIAN T D, NGUYEN T H, NGUYEN N T, et al. WiLHPE: WiFi-enabled lightweight channel frequency dynamic convolution for HPE tasks[C]. The Tenth International Conference on Communications and Electronics, Danang, Vietnam, 2024: 516–521. doi: 10.1109/ICCE62051.2024.10634628.
    [24]
    NGUYEN X H, NGUYEN V D, LUU Q T, et al. Robust WiFi sensing-based human pose estimation using denoising autoencoder and CNN with dynamic subcarrier attention[J]. IEEE Internet of Things Journal, 2025, 12(11): 17066–17079. doi: 10.1109/JIOT.2025.3535156.
    [25]
    FANG Haoshu, LI Jiefeng, TANG Hongyang, et al. AlphaPose: Whole-body regional multi-person pose estimation and tracking in real-time[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(6): 7157–7173.
    [26]
    ZHOU Yue, XU Caojie, ZHAO Lu, et al. CSI-Former: Pay more attention to pose estimation with WiFi[J]. Entropy, 2023, 25(1): 20. doi: 10.3390/e25010020.
    [27]
    HUANG Jinyang, FENG Yuanhao, CUI Fengqi, et al. Identifying who you are no matter what you write through abstracting handwriting style[J]. IEEE Transactions on Dependable and Secure Computing, 2026. doi: 10.1109/TDSC.2026.3668275.
    [28]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [29]
    HU Jie, SHEN Li, and SUN Gang. Squeeze-and-excitation networks[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7132–7141. doi: 10.1109/CVPR.2018.00745.
    [30]
    WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7794–7803. doi: 10.1109/CVPR.2018.00813.
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