Investigating the onset of slip in gait by employing probabilistic theory and optimization-based motion prediction

Slips, trips, and falls have serious impact on humans and can cause serious injury or death. There is potential to reduce the likelihood of slips, and thus falls, which could reduce injuries and save money. The likelihood of a slip in gait is related to the available friction and required friction. Previous research has been dedicated to predicting the probability of slip; however, there are major drawbacks to the previous studies. In addition, the studies do not provide probabilities of slip for real world scenarios and ignore high potentials for slip by ignoring certain peaks in the required friction during level gait. Also, no one has extended the theory to include situations such as ramp gait, which in general has higher potential for slip compared to level gait. There are no studies that look at the sensitivity of the probability of slip to the input parameters to determine which parameters have the most influence on the probability of slip. Finally, there are no studies which incorporate simulations to predict gait adaptations that would reduce the probability of slip. This study addresses these drawbacks through the following objectives. First, a systematic method for predicting the probability of slip in gait, both level gait and ramp gait, was developed. It is critical to be able to predict the probability of slip in gait to determine whether a given gait-shoe-floor combination is hazardous or not. Second, a sensitivity analysis was performed to determine which of the input parameters has the highest influence on the probability of slip. Understanding the sensitivity of the input parameters on the probability of slip allows one to determine practical ways to reduce the probability of slip. Finally, a simulation method was developed that predicts gait adaptations that reduce the potential for slip.

This dissertation won 2nd Place in the Texas Tech University Outstanding Thesis and Dissertation Award, Mathematics, Physical Sciences & Engineering, 2014.