Multilayer perceptron neural network classification for human vertical ground reaction forces

Kwang Leng Goh, King Hann Lim, Alpha Agape Gopalai, Yu Zheng Chong

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    6 Citations (Scopus)


    In this paper, human motion classification using multilayered neural network is proposed to classify motion signal based on vertical ground resultant force (VGRF). VRGF readings were acquired using an instrumented treadmill. The work presented in this paper seeks to classify six activities i.e. standing to walking, walking, walking to jogging, jogging, jogging to running and running, based on the measured VGRF. The data set involved 229 healthy Asians aged between 20 and 24, yielding a total of 740 activity classes. All activities varied as a result of subjects desired speed. However, it was observed that the VGRF of the last five strides reaction forces was sufficient to achieve 83 classification rate for the training set and 73 for testing set. The influence of number of hidden neurons was also analyzed to obtain optimal classification performance.
    Original languageEnglish
    Title of host publicationProceedings of the 2014 IEEE International Conference on Biomedical Engineering and Sciences
    EditorsFatimah Ibrahim
    Place of PublicationNew Jersey USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages536 - 540
    Number of pages5
    ISBN (Print)9781479940844
    Publication statusPublished - 2015
    EventIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2014 - University of Malaya, Kuala Lumpur, Malaysia
    Duration: 8 Dec 201410 Dec 2014


    ConferenceIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2014
    Abbreviated titleIECBES 2014
    CityKuala Lumpur

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