An instance-based algorithm with auxiliary similarity information for the estimation of gait kinematics from wearable sensors.

Goulermas, JY; Findlow, AH; Nester, CJ; Liatsis, P; Zeng, XJ; Kenney, LPJ; Tresadern, P; Thies, SB; Howard, D

Abstract

Wearable human movement measurement systems
are increasingly popular as a means of capturing human movement data in real-world situations. Previous work has attempted to estimate segment kinematics during walking fromfoot acceleration and angular velocity data. In this paper, we propose a novel neural network [GRNN with Auxiliary Similarity Information (GASI)] that estimates joint kinematics by taking account of proximity and gait trajectory slope information through adaptive weighting.
Furthermore, multiple kernel bandwidth parameters are used that can adapt to the local data density. To demonstrate the value of the GASI algorithm, hip, knee, and ankle joint motions are estimated from acceleration and angular velocity data for the foot and shank, collected using commercially available wearable sensors. Reference hip, knee, and ankle kinematic data were obtained using
externally mounted reflective markers and infrared cameras for subjects while they walked at different speeds. The results provide further evidence that a neural net approach to the estimation of joint kinematics is feasible and shows promise, but other practical issues must be addressed before this approach is mature enough for
clinical implementation. Furthermore, they demonstrate the utility of the new GASI algorithm for making estimates from continuous periodic data that include noise and a significant level of variability.

Citation

Goulermas, J., Findlow, A., Nester, C., Liatsis, P., Zeng, X., Kenney, L., …Howard, D. (2008). An instance-based algorithm with auxiliary similarity information for the estimation of gait kinematics from wearable sensors. IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, 19(9), 1574-1582. https://doi.org/10.1109/TNN.2008.2000808

• Journal Article Type: Article
• Publication Date: Jan 1, 2008
• Deposit Date: Jan 4, 2011
• Journal: IEEE Transactions on Neural Networks
• Print ISSN: 1045-9227
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 9
• Pages: 1574-1582
• DOI: https://doi.org/10.1109/TNN.2008.2000808
• Publisher URL: http://dx.doi.org/10.1109/TNN.2008.2000808