Gait phase classification for in-home gait assessment

Minxiang Ye, Cheng Yang, Vladimir Stankovic, Lina Stankovic, Samuel Cheng

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)
177 Downloads (Pure)


With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy.
Original languageEnglish
Number of pages6
Publication statusPublished - 31 Aug 2017
EventIEEE International Conference on Multimedia and Expo - Harbour Grand Kowloon hotel, Hong Kong, China
Duration: 10 Jul 201714 Jul 2017
Conference number: 18


ConferenceIEEE International Conference on Multimedia and Expo
Abbreviated titleICME
CityHong Kong
Internet address


  • feature extraction
  • gait phase classification


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  • Finalist of the World first 10K Best paper Award (Top 3%)

    Ye, Minxiang (Recipient), Yang, Cheng (Recipient), Stankovic, Vladimir (Recipient), Stankovic, Lina (Recipient) & Cheng, Samuel (Recipient), 12 Jul 2017

    Prize: Prize (including medals and awards)


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