Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation

被引:25
|
作者
Joukov, Vladimir [1 ]
Bonnet, Vincent [2 ]
Karg, Michelle [1 ]
Venture, Gentiane [3 ,4 ]
Kulic, Dana [1 ]
机构
[1] Univ Waterloo, Waterloo, ON N2L 3G1, Canada
[2] Univ Paris Est Creteil, Lab Images Signaux & Syst Intelligents EA 3956, F-94400 Vitry Sur Seine, France
[3] Tokyo Univ Agr & Technol, Grad Sch Engn, Tokyo 1848588, Japan
[4] Tokyo Univ Agr & Technol, Fac Engn, Tokyo 1848588, Japan
关键词
Human motion estimation; inertial measurement unit; motion model learning; gait rehabilitation; MODELS;
D O I
10.1109/TNSRE.2017.2659730
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features, such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37%, respectively, estimates joint angles with 2.4 degrees root mean squared error, and segments the motion into repetitions with 96% accuracy.
引用
收藏
页码:407 / 418
页数:12
相关论文
共 50 条
  • [1] Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving
    Wei, Hongjian
    Huang, Yingping
    Hu, Fuzhi
    Zhao, Baigan
    Guo, Zhiyang
    Zhang, Rui
    REMOTE SENSING, 2021, 13 (09)
  • [2] Respiratory Motion Estimation With Hybrid Implementation of Extended Kalman Filter
    Lee, Suk Jin
    Motai, Yuichi
    Murphy, Martin
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2012, 59 (11) : 4421 - 4432
  • [3] Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation附视频
    杨亚非
    Journal of Measurement Science and Instrumentation, 2011, (04) : 307 - 311
  • [4] The motor extended Kalman filter: A geometric approach for rigid motion estimation
    Bayro-Corrochano, E
    Zhang, YW
    JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2000, 13 (03) : 205 - 228
  • [5] The Motor Extended Kalman Filter: A Geometric Approach for Rigid Motion Estimation
    Eduardo Bayro-Corrochano
    Yiwen Zhang
    Journal of Mathematical Imaging and Vision, 2000, 13 : 205 - 228
  • [6] Extended Kalman filter design for motion estimation by point and line observations
    Zhang, YW
    Rosenhahn, B
    Sommer, G
    ALGEBRAIC FRAMES FOR THE PERCEPTION-ACTION CYCLE, PROCEEDINGS, 2000, 1888 : 339 - 348
  • [7] Extended Kalman filter and extended particle Kalman filter for non linear estimation problems
    Sanchez, Luis
    Ordonez, Joan
    Infante, Saba
    INGENIERIA UC, 2013, 20 (01): : 7 - 16
  • [8] MOTION ESTIMATION ALGORITHM WITH KALMAN FILTER
    KUO, CM
    HSIEH, CH
    LIN, HC
    LU, PC
    ELECTRONICS LETTERS, 1994, 30 (15) : 1204 - 1206
  • [9] Extended robust Kalman filter for attitude estimation
    Inoue, Roberto Santos
    Terra, Marco Henrique
    Cerri, Joao Paulo
    IET CONTROL THEORY AND APPLICATIONS, 2016, 10 (02): : 162 - 172
  • [10] Train Velocity Estimation by Extended Kalman Filter
    Pichlik, Petr
    Zdenek, Jiri
    2016 8TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI), 2016,