Adaptive density-peaks clustering for gait analysis. Issue 3 (27th September 2022) - Onal, Sinan; Islam, Somaiya Khan
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.