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

Goulermas, JY, Findlow, AH ORCID:, Nester, CJ ORCID:, Liatsis, P, Zeng, XJ, Kenney, LPJ ORCID:, Tresadern, P, Thies, SB ORCID: and Howard, D ORCID: 2008, 'An instance-based algorithm with auxiliary similarity information for the estimation of gait kinematics from wearable sensors.' , IEEE Transactions on Neural Networks, 19 (9) , pp. 1574-1582.

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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.

Item Type: Article
Themes: Subjects / Themes > R Medicine > R Medicine (General)
Health and Wellbeing
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Schools > School of Health and Society > Centre for Health Sciences Research
Journal or Publication Title: IEEE Transactions on Neural Networks
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
ISSN: 1045-9227
Depositing User: SBA Thies
Date Deposited: 04 Jan 2011 14:41
Last Modified: 27 Aug 2021 19:55

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