With the rapidly increasing number of older people in our societies, fall detection is becoming more important: Older adults may fall at home when they are alone and they may not be found in time for them to get help. In addition, a fall itself can cause serious injuries such as lacerations, fractures and hematomas. Although many previous studies have been reported on various fall detection technologies based on wearable sensors, the inconvenience of wearing them is problematic. Vision or ambient based methods may be alternatives, but high cost and complex installation process limit applicable areas. We propose a cost-effective, ambient-based fall detection system based on a pressure sensing triboelectric nanogenerator (TENG) array. Apart from simple observation of output signal waveforms according to different actions, key technologies, including appropriate filtering and distinguishing between falls and daily activities, are demonstrated with data acquisition from 48 daily activities and 48 falls by eight participants. The proposed system achieves a classification accuracy of 95.75% in identifying actual falls. Due to its low cost, easy installation and notable accuracy, the proposed system can be immediately applied to smart homes and smart hospitals to prevent additional injuries caused by falls.

中文翻译：

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使用压力感应摩擦电纳米发电机的自供电跌倒检测系统

随着我们社会中老年人的迅速增加，跌倒检测变得越来越重要：老年人可能独自一人在家中跌倒，并且可能无法及时找到他们来寻求帮助。另外，跌倒本身会导致严重的伤害，例如割伤，骨折和血肿。尽管已经报道了许多基于可穿戴传感器的各种跌倒检测技术的先前研究，但穿戴它们的不便是有问题的。视觉或基于环境的方法可能是替代方法，但是高成本和复杂的安装过程限制了适用范围。我们提出了一种基于压力感应摩擦电纳米发电机（TENG）阵列的经济有效的基于环境的跌倒检测系统。除了根据不同动作简单观察输出信号波形外，关键技术还包括：包括适当的过滤和区分跌倒和日常活动，这是通过从48个日常活动中获取数据以及八名参与者进行的48次跌落进行证明的。所提出的系统在识别实际跌倒时达到了95.75％的分类精度。由于其成本低，易于安装且精度显着，因此建议的系统可立即应用于智能家居和智能医院，以防止因跌倒而造成额外伤害。