Machine Learning for Movement Pattern Changes during Kinect-Based Mixed Reality Exercise Programs in Women with Possible Sarcopenia: Pilot Study

Abstract

Background: Sarcopenia is a muscle-wasting condition that affects older individuals. It can lead to changes in movement patterns, which can increase the risk of falls and other injuries. Methods: Older women participants aged ≥65 years who could walk independently were recruited and classified into two groups based on knee extension strength (KES). Participants with low KES scores were assigned to the possible sarcopenia group (PSG; n=7) and an 8-week exercise intervention was implemented. Healthy seniors with high KES scores were classified as the reference group (RG; n=4), and a 3-week exercise intervention was conducted. Kinematic movement data were recorded during the intervention period. All participants exercise repetitions were used in the data analysis (number of data points=1,128). Results: The PSG showed significantly larger movement patterns in knee rotation during wide squats compared to the RG, attributed to weakened lower limb strength. The voting classifier, trained on the movement patterns from wide squats, determined that significant differences in overall movement patterns between the two groups persisted until the end of the exercise intervention. However, after the exercise intervention, significant improvements in lower limb strength in the PSG resulted in reduced knee rotation range of motion and max, thereby stabilizing movements and eliminating significant differences with the RG. Conclusion: This study suggests that exercise interventions can modify the movement patterns in older individuals with possible sarcopenia. These findings provide fundamental data for developing an exercise management system that remotely tracks and monitors the movement patterns of older adults during exercise activities.