SolePoser: Real-Time 3D Human Pose Estimation using Insole Pressure Sensors

被引:0
|
作者
Wu, Erwin [1 ,2 ]
Khirodkar, Rawal [1 ]
Koike, Hideki [2 ]
Kitani, Kris [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Tokyo Tech, Meguro Ku, Tokyo, Japan
关键词
Insole sensor; motion capture; pose estimation; foot pressure;
D O I
10.1145/3654777.3676418
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose SolePoser, a real-time 3D pose estimation system that leverages only a single pair of insole sensors. Unlike conventional methods relying on fxed cameras or bulky wearable sensors, our approach ofers minimal and natural setup requirements. The proposed system utilizes pressure and IMU sensors embedded in insoles to capture the body weight's pressure distribution at the feet and its 6 DoF acceleration. This information is used to estimate the 3D full-body joint position by a two-stream transformer network. A novel double-cycle consistency loss and a cross-attention module are further introduced to learn the relationship between 3D foot positions and their pressure distributions. We also introduced two diferent datasets of sports and daily exercises, ofering 908k frames across eight diferent activities. Our experiments show that our method's performance is on par with top-performing approaches, which utilize more IMUs and even outperform third-person-view camera-based methods in certain scenarios.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Rapid Skin: Estimating the 3D Human Pose and Shape in Real-Time
    Straka, Matthias
    Hauswiesner, Stefan
    Ruether, Matthias
    Bischof, Horst
    SECOND JOINT 3DIM/3DPVT CONFERENCE: 3D IMAGING, MODELING, PROCESSING, VISUALIZATION & TRANSMISSION (3DIMPVT 2012), 2012, : 41 - 48
  • [32] Real-time head tracking and 3D pose estimation from range data
    Malassiotis, S
    Strintzis, MG
    2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 2, PROCEEDINGS, 2003, : 859 - 862
  • [33] Real-time upper body detection and 3D pose estimation in monoscopic images
    Micilotta, Antonio S.
    Ong, Eng-Jon
    Bowden, Richard
    COMPUTER VISION - ECCV 2006, PT 3, PROCEEDINGS, 2006, 3953 : 139 - 150
  • [34] Binocular Multi-CNN System for Real-Time 3D Pose Estimation
    Niemirepo, Teo T.
    Viitanen, Marko
    Vanne, Jarno
    MM '20: PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, 2020, : 4553 - 4555
  • [35] Robust real-time 3D head pose estimation from range data
    Malassiotis, S
    Strintzis, MG
    PATTERN RECOGNITION, 2005, 38 (08) : 1153 - 1165
  • [36] REAL-TIME 3D HAND-OBJECT POSE ESTIMATION FOR MOBILE DEVICES
    Yin, Yue
    McCarthy, Chris
    Rezazadegan, Dana
    2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2021, : 3288 - 3292
  • [37] Multi-Task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition
    Luvizon, Diogo C.
    Picard, David
    Tabia, Hedi
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (08) : 2752 - 2764
  • [38] 3D Human Pose Estimation Using Pressure Images on a Smart Chair
    Zhao, Mingjie
    Xie, Fangting
    Wu, Ziyu
    Liang, Zhen
    Cai, Xiaohui
    2024 2ND ASIA CONFERENCE ON COMPUTER VISION, IMAGE PROCESSING AND PATTERN RECOGNITION, CVIPPR 2024, 2024,
  • [39] Research on Real-time Estimation for Human Pose
    Li, Beibei
    Zhao, Zhihong
    2013 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), 2013, : 301 - 305
  • [40] G2O-Pose: Real-Time Monocular 3D Human Pose Estimation Based on General Graph Optimization
    Sun, Haixun
    Zhang, Yanyan
    Zheng, Yijie
    Luo, Jianxin
    Pan, Zhisong
    SENSORS, 2022, 22 (21)