Reliability of Recurrence Quantification Analysis Measures for Sit-to-Stand and Stand-to-Sit Activities in Healthy Older Adults Using Wearable Sensors

Standing up and sitting down are prerequisite motions in most activities of daily living scenarios. The ability to sit down in and stand up from a chair or a bed depreciates and becomes a complex task with increasing age. Hence, research on the analysis and recognition of these two activities can help in the design of algorithms for assistive devices. In this work, we propose a reliability analysis for testing the internal consistency of nonlinear recurrence features for sit-to-stand (Si2St) and stand-to-sit (St2Si) activities for motion acceleration data collected by a wearable sensing device for 14 healthy older subjects in the age range of 78 +/- 4.9 years. Four recurrence features-%recurrence rate, %determinism, entropy, and average diagonal length-were calculated by using recurrence plots for both activities. A detailed relative and absolute reliability statistical analysis based on Cronbach's correlation coefficient (alpha) and standard error of measurement was performed for all recurrence measures. Correlation values as high as alpha = 0.68 (%determinism) and alpha = 0.72 (entropy) in the case of Si2St and alpha = 0.64 (%determinism) and alpha = 0.69 (entropy) in the case of St2Si-with low standard error in the measurements-show the reliability of %determinism and entropy for repeated acceleration measurements for the characterization of both the St2Si and Si2St activities in the case of healthy older adults.

Automated summary

The research focuses on analyzing sit-to-stand and stand-to-sit activities in older adults, proposing a reliability analysis method for nonlinear recurrence features in motion acceleration data to assist in the design of algorithms for assistive devices. High correlation values and low standard error in the measurements show the reliability of determinism and entropy for repeated acceleration measurements in characterizing these activities.