Projects per year
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.
|Number of pages||6|
|Publication status||Published - 31 Aug 2017|
|Event||IEEE International Conference on Multimedia and Expo - Harbour Grand Kowloon hotel, Hong Kong, China|
Duration: 10 Jul 2017 → 14 Jul 2017
Conference number: 18
|Conference||IEEE International Conference on Multimedia and Expo|
|Period||10/07/17 → 14/07/17|
- feature extraction
- gait phase classification
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- 1 Finished
SENSIBLE: SENSors and Intelligence in BuiLt Environment (SENSIBLE) MSCA RISE
Stankovic, L., Glesk, I., Gleskova, H. & Stankovic, V.
European Commission - Horizon 2020
1/01/17 → 31/12/20
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)File