Inertial Sensor-Based Slip Detection in Human Walking

Slip is one of the leading causes of fall-related injuries among occupational and elderly population. Existing research supports proactive slip and fall prevention approaches, while active strategies remain underdeveloped. Development of active slip-induced fall prevention systems requires fast, effective slip detection. This paper aims to develop a novel, real-time slip detection and estimation algorithm during human walking. The slip estimation is built on a slip dynamic model for biped walkers with the integration of the human locomotion constraints. The slip detection uses a set of wearable inertial sensors attached on the lower limbs. A slip indicator is introduced to detect the slip shortly after the heel-strike event. We present an extended Kalman filter-based slip estimation to characterize the slipping distance. One attractive property of the algorithm is its fast, accurate slip onset detection and slipping distance estimation with low-cost, nonintrusive sensing features. Experiments are conducted to validate and demonstrate the performance of the proposed slip detection and estimation scheme.