Dynamic structural change detection and its effect on dynamic stability in segment synergy during sit-to-stand transition with healthcare robot assistance

This study investigated dynamic structural changes and their implications for dynamic stability during sit–to–stand (STS) transitions with healthcare robot assistance. The STS movement, a fundamental activity for mobility and independence, poses challenges for older adults due to age–related declines in muscle strength and joint integrity, increasing fall risk. Assistive robots, typically categorized into upper– and lower–limb support systems, have been developed to mitigate this risk. However, conventional biomechanical assessments fail to capture the nonlinear, time–varying coordination patterns essential for maintaining dynamic stability in human–robot interaction (HRI). To address this gap, we introduced nonlinear analytical techniques to detect dynamic structural changes during STS transitions under three experimental conditions: self–performed, passive robot–assisted, and proactive robot–assisted STS. Motion capture data from 14 healthy participants were analyzed, with segment synergies quantified through relative phase analysis of the trunk, knee, and ankle. A segment–synergy–based segmentation algorithm based on dynamic correlation exponents was developed to detect phase transitions in synergy dynamics. Complementary nonlinear–dynamics measures–maximum Lyapunov exponent, sample entropy, and detrended fluctuation analysis–were employed to assess dynamic stability. Results showed that passive assistance induced earlier but less synchronized transitions than self–performed STS, whereas proactive assistance preserved natural timing and enhanced intersegmental coordination stability. Temporal dissociation between knee–trunk and ankle–knee synergy transitions emerged as a potential predictor of dynamic stability outcomes. These findings underscore the importance of phase–sensitive control algorithms in assistive robotics for achieving biologically congruent HRI. By bridging biomechanical insights with advanced analytical methods, this study provides a critical step toward the design of healthcare robots that enhance mobility and reduce fall risk.