北京信息科技大学机电工程学院 北京 100192
TN87；TP212
Mechanical Electrical Engineering School, Beijing Information Science and Technology University，Beijing 100192, China
针对目前跌倒检测系统存在的检测准确率不高、实时性差等问题，设计了一种基于多传感信息融合的跌倒监测系统。该系统以 ESP32 微处理器为核心，利用智能手机内置的传感器、压力薄膜传感器以及 MPU6050 传感器进行数据采集，并通过小程序界面实时显示健康数据，提供监测和预警功能。提出了一种云边协同的联合判别跌倒检测方法，该方法结合了本地的多级阈值算法和云端的改进SSA-LSTM-Transformer 算法和数据融合权重，算法经过公开数据集验证，准确率达到99.13%。最后，通过实验进行系统验证，实验结果表明，系统的跌倒检测准确率为 97.67%，能够有效检测跌倒行为并实时定位和预警。
To address the issues of low detection accuracy and poor real-time performance in current fall detection systems, a fall monitoring system based on multisensor information fusion has been designed. The system is centered around the ESP32 microprocessor and utilizes sensors embedded in smartphones, pressure film sensors, and MPU6050 sensors for data collection. Health data is displayed in real-time through a mini-program interface, providing monitoring and alert functions. A collaborative cloud-edge fall detection method has been proposed, combining a local multi-threshold algorithm with an improved SSA-LSTM-Transformer algorithm and data fusion weights in the cloud. This algorithm has been validated on a public dataset, achieving an accuracy rate of 99.13%. Finally, system validation was performed through experiments, and the results showed that the system′s fall detection accuracy is 97.67%. It effectively detects falls and provides real-time positioning and alerts.
孙巍伟,梁毅玮,毛亦鹏,胡志辉.基于多传感信息融合的跌倒监测系统开发[J].电子测量技术,2024,47(22):94-103
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