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.

跌倒探测器系统是环境辅助生活（AAL）应用领域中经过研究的领域之一，可确保老年人的安全和自主生活。如今，尽管为跌倒检测器提议的方法多种多样，但仍需要开发出准确而可靠的体系结构，方法和协议，以检测轮椅坐下的特殊人群跌倒的发生情况。为了解决这个问题，本文提出了一种基于轮椅的跌倒检测器系统，该系统基于一种低成本，轻型惯性传感方法，该方法利用混合方案和无监督的一类SVM（OCSVM）来检测导致“坠落”异常的情况在轮椅操作期间以及在无辅助转移的情况下。为了使系统具有强大的抗噪能力，结合零角速率更新（ZART）和互补滤波器（CF）来补偿传感器积分误差的新型混合多传感器融合策略被利用。使用公开的Sis-Fall数据集构建了一个异构数据集，以包括由于轮椅无助转移而可能导致的跌倒情况；其次，开发了一个原型来模拟带有嵌入式惯性传感器的轮椅系统，以捕获由于以下原因导致的传感器测量趋势：轮椅提示和跌倒。OCSVM异常检测技术用于克服监督学习方法的主要缺点，该学习方法要求从人类受检者的活动中获取大量数据集，以确保这些检测器具有更高的准确率。在这方面，为了从生成的加速度计和陀螺仪功能集中捕获最佳功能，使用了ReliefF算法。将所提出的方法与文献中针对跌倒检测器的广泛报道的方法进行比较，即基于阈值的方法和其他一类学习方法。结果表明，所提出的方法可以使跌倒检测的准确性（即g均值）达到96％。