用手机加WiFi信号检测跌倒，实时准且省电。
Real-Time Fall Detection Using Smartphone Accelerometers and WiFi Channel State Information
- 结合手机传感器与优化的WiFi信道信息做双重验证
- 基于CNN的WiFi模型跌倒检测准确率达99%
- 适合老人居家安全监测，功耗低易部署
随着人口老龄化加剧，跌倒对老年人健康构成日益严重的威胁。本文提出一种实时跌倒检测系统，融合智能手机的惯性测量单元（IMU）与优化的Wi-Fi信道状态信息（CSI）进行二次验证。首先，IMU以极低计算开销区分跌倒与日常活动；随后，利用CSI评估跌倒后个体的移动能力。该方法不仅实现高精度检测，还显著降低手机平台能耗。专门开发的Android应用在检测到跌倒且用户无法起身时自动发出紧急警报。实验结果表明，基于卷积神经网络（CNN）的CSI模型检测准确率达99%，优于同类仅依赖IMU的模型，并在区分跌倒与非跌倒行为方面表现出显著鲁棒性。
原文摘要 · Abstract (English)
In recent years, as the population ages, falls have increasingly posed a significant threat to the health of the elderly. We propose a real-time fall detection system that integrates the inertial measurement unit (IMU) of a smartphone with optimized Wi-Fi channel state information (CSI) for secondary validation. Initially, the IMU distinguishes falls from routine daily activities with minimal computational demand. Subsequently, the CSI is employed for further assessment, which includes evaluating the individual's post-fall mobility. This methodology not only achieves high accuracy but also reduces energy consumption in the smartphone platform. An Android application developed specifically for the purpose issues an emergency alert if the user experiences a fall and is unable to move. Experimental results indicate that the CSI model, based on convolutional neural networks (CNN), achieves a detection accuracy of 99%, \revised{surpassing comparable IMU-only models, and demonstrating significant resilience in distinguishing between falls and non-fall activities.
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