Estimation of joint angle from ground reaction force in human gait

Saaveethya Sivakumar, A. A. Gopalai, D. Gouwanda, King Hann Lim

    Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

    3 Citations (Scopus)


    Current research in human biomechanics is trending towards machine learning based gait parameter estimations. This work is a proof of concept in using Artificial Neural Networks (ANNs) to estimate lower limb kinematics from foot kinetics. In this study, we present a three-layer Feed Forward Neural Network (FFNN) to estimate ankle angles from Ground Reaction Forces (GRFs). GRFs are measured from instrumented foot insoles (MOTICON) while ankle angles are measured using an optical motion camera system. Salient input features and target outputs for the ANN are selected based on priori knowledge of five gait event occurrences at its pre-defined gait intervals. These five main gait events are; Heel Strike (HS), Foot Flat (FF), Mid Stance (MS), Heel Off (HO) and Toe Off (TO). Results indicate high correlations between estimated and its ground truth angles (NRMSE < 9.112% and ρ > 0.94). The result from this study shows the possibilities of modelling a dual-purpose foot insole that can measure GRFs while estimating lower limb angles. This will open up countless opportunities for outdoor gait monitoring during acts of daily living.

    Original languageEnglish
    Title of host publication2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages6
    ISBN (Electronic)9781538624715
    Publication statusPublished - 24 Jan 2019
    EventIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2018 - Sarawak, Malaysia
    Duration: 3 Dec 20186 Dec 2018 (Proceedings)


    ConferenceIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2018
    Abbreviated titleIECBES 2018
    Internet address


    • Artificial Neural Networks
    • Estimations
    • Gait
    • Kinematics
    • Kinetics

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