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

TL;DR

This paper presents a real-time fall detection system combining smartphone accelerometers and Wi-Fi CSI, achieving high accuracy and low energy consumption for elderly safety.

Contribution

It introduces a novel integration of IMU and Wi-Fi CSI with CNN-based analysis for improved fall detection accuracy and energy efficiency.

Findings

CSI model achieves 99% accuracy in fall detection

System surpasses IMU-only models in accuracy

Reduces energy consumption on smartphones

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.