Observing human gait and detecting falls

Abstract

Falling remains a large source of (traumatic) injuries and healthcare costs. Over the past years, different actuators have been developed in the field of wearable robotics to help prevent injuries from falling. To increase the wearability of these systems, the weight of power storage can be decreased with intermittent instead of continuous control. A fall detector is then needed for these systems to trigger the activation of the actuator to prevent the fall. A proof of concept for a model-based fall detector that is aimed at using only wearable sensor measurements is presented. The algorithm is based on a single inertial measurement unit placed on the lower back. The upper-body orientation and centre of mass velocity are estimated with two separate Kalman filters. The velocity is estimated with a gait model consisting of a spring-damper-legged point mass in three-dimensional space. The balance of the subject is evaluated with the velocity estimates and the extrapolated centre of mass method. The presented model is verified on a non-falling treadmill walking dataset of real humans and shows accurate estimates of the centre of mass velocity. Furthermore, a planar falling simulation is performed to show successful pre-impact fall detection. This resulted in no false alarms outside the initial estimation settling time, and a successful detection with a lead time of 680 ms. This lead time is long enough to provide a trigger for fall mitigation devices.