Slip detection and prediction in human walking using only wearable inertial measurement units (IMUs)

Slip and fall is one of the major causes for human injuries for elders and professional workers. Real-time detection and prediction of the foot slip is critical for developing effective assistive and rehabilitation devices to prevent falls and train balance disorder patients. This paper presents a novel real-time slip detection and prediction scheme with wearable inertial measurement units (IMUs). The slip-detection algorithm is built on a new dynamic model for bipedal walking with slips. An extended Kalman filter is designed to reliably predict the foot slip displacement using the wearable IMU measurements and kinematic constraints. The proposed slip detection and prediction scheme has been demonstrated by extensive experiments.