Authors: Sinan Onal; Somaiya Khan Islam
Addresses: Department of Industrial Engineering, Southern Illinois University, Edwardsville 1 Hairpin Dr. Campus Box 1805, 62026, Edwardsville, IL, USA ' Department of Industrial Engineering, Southern Illinois University, Edwardsville 1 Hairpin Dr. Campus Box 1805, 62026, Edwardsville, IL, USA
Abstract: Gait analysis compares the gait characteristics of people with health issues to those of a control group in order to detect gait abnormalities. This comparison is carried out by evaluating a number of gait parameters with discrete values. Gait data, on the other hand, is time-series data and must be assessed using a different approach. The purpose of this study was to develop a quantitative measure that takes into account time-series data for comparing the gait characteristics of two groups of individuals using clustering. The gait data were collected using an optical motion capture system. An adaptive density-peaks clustering technique with a shape-based similarity measure was employed to compare gait characteristics. The results demonstrate that the proposed adaptive density-peaks clustering technique, which employs dynamic derivative time wrapping distance measurement, outperforms three state-of-the-art clustering algorithms for comparing the gait characteristics using time-series gait data.
Keywords: density-peaks clustering; time-series analysis; gait analysis; motion capture system; biomechanics.
International Journal of Knowledge Engineering and Data Mining, 2022 Vol.7 No.3/4, pp.145 - 162
Received: 16 Aug 2021
Accepted: 24 Oct 2021
Published online: 10 Oct 2022 *