A ubiquitous wheelchair fall detection system using low-cost embedded inertial sensors and unsupervised one-class SVM

Abstract

Fall detector systems are one of the highly researched areas of Ambient Assisted Living (AAL) applications ensuring safety and autonomous living for the elderly. Today, despite the diversity of methods proposed for fall detectors, there is still a need to develop accurate and robust architectures, methods, and protocols for the detection of the occurrence of falls for a special-class of wheelchair-bound people. To address this issue, this paper proposes a wheelchair fall detector system based on a low-cost, light-weight inertial sensing method utilizing a hybrid scheme and unsupervised One-Class SVM (OCSVM) for detection of cases leading to a ‘fall’ anomaly during wheelchair maneuver and for the case of unassisted transfers. To make the system robust against noise, a novel hybrid multi-sensor fusion strategy combining Zero Angular Rate Update (ZART) and Complementary Filter (CF) to compensate sensor integral errors is utilized. A heterogeneous dataset is constructed using the publicly available Sis-Fall dataset to include possible fall cases due to unassisted transfers from wheelchairs and secondly, a prototype is developed to emulate the wheelchair system with the embedded inertial sensors to capture trends in the sensor measurements due to wheelchair tips and falls. The OCSVM anomaly detection technique is utilized to overcome the major disadvantage of supervised learning methods requiring huge datasets from activities performed by human subjects needed to guarantee higher accuracy rates from these detectors. In this regard, to capture the best features from the generated accelerometer and gyroscope feature set, the ReliefF algorithm is used. The proposed method is compared with the widely reported approaches in the literature for fall-detectors, i.e., threshold-based methods and other one-class learning approaches. It is demonstrated that the fall-detection accuracy (i.e., the g-mean score) was achieved up to 96% with the proposed method.