The exercise load of the elderly with different physical capacity based on PCA and K-means clustering
C. C. Lin, T. S. Cho, Y. S. Lin, J. J. Lien, L. C. Kuo, F. C. Su
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AbstractPopulation aging is an emerging global issue. For the older adults, physical function inevitably declines with age, while exercise is convinced to improve physical functioning and performance in activities of daily living (Chou, Hwang, & Wu, 2012; de Vries et al., 2012). American College of Sports Medicine (ACSM) recommends moderate to vigorous exercise for older adults. The exercise intensity can be detected objectively by percentage maximal heart rate (%HRmax) (Meyer, Gabriel, & Kindermann, 1999). In practice, fitness practitioners evaluate the physical capacity of the elders by the senior fitness test (SFT) (Rikli & Jones, 1999) and provide further health promotion courses for elders. However, the performance of SFT and the reflected exercise intensity during exercise intervention are rarely studied. K-means clustering is a data mining method to classify objects into different categories based on their similarity. The unsupervised machine learning has used for medical applications, such as medical image classification (Gray et al., 2011), the link between dietary patterns and diseases (Stricker et al., 2013), etc. This study elucidated the correlation between physical fitness performance and reflected exercise intensity during a 11-week training course by K-means clustering.Keywords: senior fitness test, percentage maximal heart rate (%HRmax), PCA, K-means clustering
C. C. Lin, T. S. Cho, Y. S. Lin, J. J. Lien, L. C. Kuo, F. C. Su (2022). The exercise load of the elderly with different physical capacity based on PCA and K-means clustering. Gerontechnology, 21(s),1-1
https://doi.org/10.4017/gt.2022.21.s.553.opp3