Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4205-4209. doi: 10.1109/EMBC48229.2022.9871342.
With the increasing global aging population, the health of the elderly has become a global concern. Accidental falls, as one of the major causes of health and safety issues affecting the elderly, can cause serious hazards. In this paper, a fall detection system is proposed to be able to deliver timely information after a fall. The acceleration and angular velocity time series extracted from motion were used to describe human motion features. Hybrid threshold analysis algorithm and machine learning algorithm are used for classification between falls and activities of daily living (ADLs). The fall detection results showed 98.55% accuracy, 98.16% sensitivity, and 98.73% specificity. The result is higher than the single-threshold algorithm and slightly lower than the machine learning algorithm. In addition, the hybrid algorithm of fall detection in this paper is to put the threshold analysis algorithm in the edge device for calculation and put the machine learning algorithm in the cloud server for calculation. Since the single machine learning algorithm needs to transmit data to the cloud server all the time, the hybrid algorithm has lower power consumption than machine learning algorithms, and the average alarm time is shorter, making it more suitable for actual systems.
随着全球人口老龄化的加剧，老年人的健康已成为全球关注的焦点。意外跌倒作为影响老年人健康和安全的主要问题之一，可能会造成严重危害。本文提出了一种跌倒检测系统，以便在跌倒后能够及时提供信息。该系统使用从运动中提取的加速度和角速度时间序列来描述人体运动特征。采用混合阈值分析算法和机器学习算法对跌倒和日常生活活动（ADL）进行分类。跌倒检测结果的准确率为 98.55%，灵敏度为 98.16%，特异性为 98.73%。结果优于单一阈值算法，略低于机器学习算法。此外，本文提出的跌倒检测混合算法是将阈值分析算法放在边缘设备中进行计算，将机器学习算法放在云服务器中进行计算。由于单一的机器学习算法需要将数据传输到云服务器，因此混合算法的功耗比机器学习算法低，平均报警时间更短，更适合实际系统。
