A comprehensive analysis of sit-to-stand movement in a living space involving principal component analysis

Sit-to-stand (STS) movement is a frequently performed activity of daily living. The analysis of STS movement can lead to the early detection of a decline in physical functioning. There is thus interest in home-based STS monitoring systems. The present study (1) extracted universal differences by age group in common STS movements of daily life and (2) clarified where the differences between age groups lie in the sequence of movements. We experimented to measure the three patterns of STS movement assumed to be performed in living spaces. Ten young adults and seventeen elderly people participated. Data were acquired by an Azure Kinect DK, an RGB-D camera, and converted into angles and angular velocities of the major body segments. Principal component analysis was applied to the overall time series data. From the synthesized principal component space, principal components with significant differences between the elderly and young-adult groups were extracted, and the movements of the elderly participants were evaluated. We found parameters and movement strategies that show the characteristics of each age group for each movement pattern. The results of this study can contribute to the design of home-based STS monitoring systems.