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Falls affect a growing number of the population each year. Clinical methodsto identify those at greatest risk for falls usually evaluate individuals whilethey perform specific motions such as balancing or Sit-to-Stand (STS).Unfortunately these techniques have been shown to have poor predictive powerand are unable to identify the magnitude, direction, and timing ofperturbations that can cause an individual to lose stability during motion. Toaddress this limitation, the recently proposed Stability Basin (SB) aims tocharacterize the set of perturbations that will cause an individual to fallunder a specific motor control strategy. The SB is defined as the set ofconfigurations that do not lead to failure for an individual under their chosencontrol strategy. This paper presents a novel method to compute the SB and thefirst experimental validation of the SB with an 11-person perturbative STSexperiment involving forwards or backwards pulls from a motor-driven cable. Theindividually-constructed SBs are used to identify when a trial fails, i.e.,when an individual must switch control strategies (indicated by a step or sit)to recover from a perturbation. The constructed SBs correctly predict theoutcome of trials where failure was observed with over 90% accuracy, andcorrectly predict the outcome of successful trials with over 95% accuracy. TheSB was compared to three other methods and was found to estimate the stableregion with over 45% more accuracy in all cases. This study demonstrates thatSBs offer a novel model-based approach for quantifying stability during motion,which could be used in physical therapy for individuals at risk of falling.

Characterizing the limits of human stability during motion: perturbative experiment validates a model-based approach for the Sit-to-Stand task