Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.

中文翻译：

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基于支持向量机的多传感器数据融合的人体跌倒检测算法

跌倒是老年人生活中的常见现象，也是严重的健康伤害和老年人死亡的十大主要原因之一。为了防止老年人跌倒，可穿戴智能设备上安装了实时跌倒预测系统，可以及时触发警报，减少跌倒造成的意外伤害。目前，大多数基于单传感器数据的算法不能准确描述跌倒状态，而基于多传感器数据集成的跌倒检测算法可以提高预测的灵敏度和特异性。在这项研究中，我们设计了基于多传感器数据融合的跌倒检测系统，并使用100名模拟跌倒和日常活动的志愿者的数据分析了跌倒的四个阶段。在本文中，数据融合方法用于提取代表人体加速度和姿势变化的三个特征参数，验证了多传感器数据融合算法的有效性。灵敏度为96.67％，特异性为97％。当训练集包含训练集中的样本数量最大时，识别率最高。因此，在基于大量有效数据训练模型之后，可以提高其识别能力，并逐渐增加防止跌倒的可能性。为了比较随机森林和支持向量机在可穿戴智能设备开发中的适用性，分别建立了两个跌倒姿势识别模型，并比较了模型的训练时间和识别时间。