Reconstruction of angular kinematics from wrist-worn inertial sensor data for smart home healthcare

Article English OPEN
Villeneuve, Emma ; Harwin, William ; Holderbaum, William ; Janko, Balazs ; Sherratt, R. Simon (2017)

This article tackles the problem of the estimation of simplified human limb kinematics for home health care. Angular kinematics are widely used for gait analysis, for rehabilitation and more generally for activity recognition. Residential monitoring requires particular sensor constraints to enable long-term user compliance. The proposed strategy is based on measurements from two low-power accelerometers placed only on the forearm, which makes it an ill-posed problem. The system is considered in a Bayesian framework, with a linear-Gaussian transition model with hard boundaries and a nonlinear-Gaussian observation model. The state vector and associated covariance are estimated by a post-Regularized Particle Filter (Constrained-Extended-RPF or C-ERPF), with an importance function whose moments are computed via an Extended Kalman Filter (EKF) linearization. Several sensor configurations are compared in terms of estimation performance, as well as power consumption and user acceptance. The proposed CERPF is compared to other methods (EKF, Constrained-EKF and ERPF without transition constraints) on the basis of simulations and experimental measurements with motion capture reference. The proposed C-ERPF method coupled with two accelerometers on the wrist provides promising results with 19% error in average on both angles, compared to the motion capture reference, 10% on velocities and 7% on accelerations. This comparison highlights that arm kinematics can be estimated from only two accelerometers on the wrist. Such a system is a crucial step toward enabling machine monitoring of users health and activity on a daily basis.
  • References (42)
    42 references, page 1 of 5

    [1] A. Godfrey, R. Conway, D. Meagher, and G. ÓLaighin, ``Direct measurement of human movement by accelerometry,'' Med. Eng. Phys., vol. 30, no. 10, pp. 1364 1386, Dec. 2008.

    [2] C. Chen, R. Jafari, and N. Kehtarnavaz, ``Improving human action recognition using fusion of depth camera and inertial sensors,'' IEEE Trans. Human-Mach. Syst., vol. 45, no. 1, pp. 51 61, Feb. 2015.

    [3] A. Pantelopoulos and N. G. Bourbakis, ``A survey on wearable sensor-based systems for health monitoring and prognosis,'' IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 1 12, Jan. 2010.

    [4] K. Liu, T. Liu, K. Shibata, Y. Inoue, and R. Zheng, ``Novel approach to ambulatory assessment of human segmental orientation on a wearable sensor system,'' J. Biomech., vol. 42, no. 16, pp. 2747 2752, Dec. 2009.

    [5] A. T. M. Willemsen, C. Frigo, and H. B. K. Boom, ``Lower extremity angle measurement with accelerometers-error and sensitivity analysis,'' IEEE Trans. Biomed. Eng., vol. 38, no. 12, pp. 1186 1193, Dec. 1991.

    [6] R. Takeda, S. Tadano, M. Todoh, M. Morikawa, M. Nakayasu, and S. Yoshinari, ``Gait analysis using gravitational acceleration measured by wearable sensors,'' J. Biomech., vol. 42, no. 3, pp. 223 233, Feb. 2009.

    [7] M. D. Djuri¢-Jovi£i¢, N. S. Jovi£i¢, and D. B. Popovi¢, ``Kinematics of gait: New method for angle estimation based on accelerometers,'' Sensors, vol. 11, no. 11, pp. 10571 10585, Jan. 2011.

    [8] T. Seel, J. Raisch, and T. Schauer, ``IMU-based joint angle measurement for gait analysis,'' Sensors, vol. 14, no. 4, pp. 6891 6909, Jan. 2014.

    [9] F. Alonge, E. Cucco, F. D'Ippolito, and A. Pulizzotto, ``The use of accelerometers and gyroscopes to estimate hip and knee angles on gait analysis,'' Sensors, vol. 14, no. 5, pp. 8430 8446, Jan. 2014.

    [10] V. Bonnet, C. Mazzà, P. Fraisse, and A. Cappozzo, ``Real-time estimate of body kinematics during a planar squat task using a single inertial measurement unit,'' IEEE Trans. Biomed. Eng., vol. 60, no. 7, pp. 1920 1926, Jul. 2013.

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    57
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Central Archive at the University of Reading - IRUS-UK 0 57
Share - Bookmark