ANN for gait estimations: a review on current trends and future applications

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

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

11 Citations (Scopus)


In recent years, gait analysis through estimations has gained significant interest. Through a critical evaluation of recent gait estimations based on Artificial Neural Networks (ANNs), this paper reviews the potential of these methods. It is found that three layer Feed Forward Neural Networks (FFNNs) are widely employed in gait parameter estimations. Traditional kinetic sensors are generally expensive and requires a laboratory environment for data collection. Due to this reason, previous research mainly performed estimations of kinetic parameters from kinematic data thus eliminating the need of traditional kinetic sensors. In comparison to kinematic data, kinetic data are less complex. Moreover, the advancement of wearable kinetic foot sensors offers the advantage of data recording outside laboratory, and are comparatively cost effective. Therefore, estimating kinematics from wearable kinetic sensor data may present to be a better alternative, which has not yet been investigated, thus providing space for future research.

Original languageEnglish
Title of host publicationIECBES 2016 - IEEE-EMBS Conference on Biomedical Engineering and Sciences
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781467377911
Publication statusPublished - 2016
EventIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2016 - Kuala Lumpur, Malaysia
Duration: 4 Dec 20168 Dec 2016 (Proceedings)


ConferenceIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2016
Abbreviated titleIECBES 2016
CityKuala Lumpur
Internet address


  • ANN
  • estimations
  • FFNN
  • global activation functions
  • kinematic
  • kinetics
  • TDNN
  • wavelet activation functions

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