Adaptive density-peaks clustering for gait analysis. Issue 3 (27th September 2022)

• Record Type: Journal Article
• Authors: Onal, Sinan; Islam, Somaiya Khan
• Is Part Of: International journal of knowledge engineering and data mining. Volume 7:Issue 3/4(2022)
• Page Start: 145
• Page End: 162
• Publication Date: 2022-09-27
• Subjects: density-peaks clustering -- time-series analysis -- gait analysis -- motion capture system -- biomechanics; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

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.