Modeling, sensing, and control of human bipedal walking with foot slip
Creators: name: Trkov, Mitja
Publisher: name: No Publisher Supplied
Year: 2016

description: Human walking is a fundamental motor skill that is developed at an early stage in  our lives. Maintaining stable walking capability demands a substantial effort and requires  synchronization and coordination of many neurological, sensorimotor and musculoskeletal  systems. Moreover, disturbances such as foot slip require even more demanding  walking control strategies for successful balance recovery and fall prevention.  However, it is challenging to capture and model human motion and reaction to foot  slip. Most of the existing slip-and-fall studies focus on clinical human experiments  and few use control systems approaches to analyze the slip dynamics and human recovery  mechanisms. Further challenges arise as few real-time sensing and robotic assistive  technologies are currently available for reliably detecting foot slips and assisting  human balance for slip-induced fall prevention.    The goal of this dissertation is to advance the understanding and knowledge of  slip dynamics with emphasis on four interlaced topics: (i) analyzing and modeling the shoe-floor interaction during foot slip, (ii) developing a novel bipedal modeling framework  to capture human walking locomotion with foot slip, (iii) developing a novel  linear inverted pendulum (LIP) modeling framework for balance recovery control, and  (iv) developing new wearable sensing and robotic assistive devices for real-time detection  of foot slip and effective prevention of slip-induced falls. In the first part, we  present modeling of foot slip evolution and development based on a quasistatic friction  force model. We present a model to obtain the normal force distribution on the  shoe-floor contact patch. In addition, we extend the previously developed beam-spring  network model and integrate it with the LuGre dynamic friction model. In the second  part of the dissertation, we present a new bipedal modeling approach, where we relax  the non-slip assumption used in the existing literature. We develop a hybrid bipedal  model and the gait controllers to capture and predict human walking with foot slip.  In the third part, we present a new two-mass LIP model for human balance control  during walking and walking with foot slip. We extend the capture point based control  approach and incorporate time-varying locations of the zero moment point and the LIP  pivoting location. In the fourth part, we propose a novel real-time foot slip detection  method using only wearable inertial measurement units. The developed slip-prediction  algorithm is built on a dynamic model for bipedal walking and is also integrated with  the human locomotion constraints. A slip indicator is introduced into the algorithm  to detect the foot slip shortly after the heel-strike event. All of the above mentioned  models, control strategies and devices are validated through the extensive experiments  and simulations. In addition, we further design and fabricate a wearable robotic knee  assistive device for slip balance recovery and slip-induced fall prevention. This device  prototype serves as an enabling tool for future testing of possible robotic assistive fall  prevention strategies.