International Journal of Knowledge Engineering and Data Mining
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International Journal of Knowledge Engineering and Data Mining (1 paper in press)
Adaptive Density-Peaks Clustering for Gait Analysis by Sinan Onal, Somaiya Khan Islam 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. DOI: 10.1504/IJKEDM.2022.10043817