As the number of aged population grows, fall detection has attracted considerable research attentions in recent years. Through the data collected by wearable sensors and specific algorithms, fall detection and protection can be performed before the user hits the ground. However, most of existing researches have not considered the direction of falls which can be used for more effective protection. In this paper, to further distinguish the direction of falls and improve the detection accuracy, we propose a multi-sensor-based fall detection system by taking the detection as a multi-class problem. To extract the feature from multi-sensor data more effectively, we also present a Multi-source CNN Ensemble (MCNNE) structure. In the proposed system, data from different sensors are preprocessed and formatted as the training dataset independently, and output features map from different sensors are concatenated to construct a overall feature map. Compared with single CNN structure and various ensemble bi-model structures, MCNNE has better performance. On 800 falls and 1000 activities of daily living, the overall average accuracy of our detection system can reach to 99.30%, and false positive rate (FPR) is lower than 0.69%.

中文翻译：

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基于多源 CNN 集成的碰撞前跌倒检测


随着老年人口数量的增加，跌倒检测近年来引起了广泛的研究关注。通过可穿戴传感器收集的数据和特定算法，可以在用户落地之前进行跌倒检测和保护。然而，现有的研究大多没有考虑跌倒方向可以用来更有效的保护。在本文中，为了进一步区分跌倒方向并提高检测精度，我们将检测作为多类问题提出了一种基于多传感器的跌倒检测系统。为了更有效地从多传感器数据中提取特征，我们还提出了多源 CNN 集成（MCNNE）结构。在所提出的系统中，来自不同传感器的数据被独立地预处理和格式化为训练数据集，并且来自不同传感器的输出特征图被连接以构建整体特征图。与单一CNN结构和各种集成双模型结构相比，MCNNE具有更好的性能。在800次跌倒和1000次日常生活活动中，我们的检测系统的总体平均准确率可以达到99.30%，误报率（FPR）低于0.69%。