Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.

翻译：跌倒是全球老年人面临的公共卫生问题，因跌倒导致的伤害伴随高额医疗成本。跌倒可能造成严重损伤，若老年人遭遇"长躺"甚至可能导致死亡。因此，需要可靠的跌倒检测（FD）系统以提供紧急急救警报。得益于可穿戴设备技术与人工智能的进步，部分跌倒检测系统已采用机器学习和深度学习方法分析加速度计与陀螺仪采集的信号。为实现更优的跌倒检测性能，本研究提出一种融合粗-细粒度卷积神经网络与门控循环单元的集成模型。该模型采用的并行结构设计恢复了空间特征的不同粒度，并捕获时间依赖性以进行特征表示。本研究应用FallAllD公开数据集验证所提出模型的可靠性，模型分别达到92.54%的召回率、96.13%的精确率和94.26%的F值。结果表明，该集成模型在区分跌倒与日常活动方面具有可靠性，且其性能优于用于跌倒检测的当前最优卷积神经网络长短期记忆模型（CNN-LSTM）。