Real-Time Fall Detection Using Smartphone Accelerometers and WiFi Channel State Information

TL;DR

This work tackles real-time fall detection by fusing smartphone IMU data with WiFi CSI to provide robust, energy-efficient monitoring for the elderly. It introduces a two-stage pipeline: Stage I uses IMU signals with an MLP to preliminarily detect falls, while Stage II uses CSI amplitude analyzed by a CNN with attention to validate post-fall mobility and discriminate self-rescue from non-self-rescue scenarios. The system achieves high performance, with the CSI component delivering ~99% accuracy and the integrated approach reducing false positives, demonstrated via an Android app implementation using TensorFlow Lite. The results suggest a practical, low-cost solution for in-home elder care, capable of real-time alerts without extra hardware, albeit with considerations for battery life and device placement.

Abstract

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.

Figures (20)

• Figure 1: An example application scenario for fall detection using Wi-Fi CSI is monitoring elderly individuals living alone, where our system can immediately detect a fall and issue warnings when necessary.
• Figure 2: The framework of the real-time fall detection system.
• Figure 3: Three phases of a fall event: descent, impact, and stationary.
• Figure 4: The environment of fall detection using IMU and Wi-Fi CSI built in the smartphones.
• Figure 5: Orientation of built-in sensors of smartphones.