# Exploring the Feasibility of Fall Detection Using Bluetooth Low Energy Channel Sounding in Residential Environments

## TL;DR

This paper explores using Bluetooth Low Energy signals to detect falls in homes, aiming to provide a privacy-friendly and easy-to-deploy solution for elderly care.

## Contribution

The study introduces a novel use of BLE 6.0 Channel Sounding for device-free fall detection with low-complexity signal processing.

## Key findings

- XGBoost achieved the best sensitivity-specificity balance for fall detection.
- BLE CS captures motion-related signal changes useful for fall detection.
- High recall is possible under sensitivity-first operation, though with more false alarms.

## Abstract

Falls represent a major health risk for older adults living independently, motivating the development of unobtrusive and privacy-preserving monitoring solutions. This study investigates whether Bluetooth Low Energy (BLE) 6.0 Channel Sounding (CS) can support device-free fall detection using low-complexity signal representations suitable for residential deployment. The proposed system employs two BLE nodes performing periodic channel sounding, from which only scalar distance estimates are extracted. Time-domain and temporal-dynamic features are computed from sliding windows of the distance signal and used for supervised classification. Three widely used classifiers—Support Vector Machine with radial basis function kernel, Random Forest, and gradient-boosted decision trees (XGBoost)—are evaluated under both a default operating point and a sensitivity-first regime achieved through validation-based decision threshold adjustment, reflecting the higher cost of missed fall detections in assisted living scenarios. Experiments conducted in a furnished indoor environment with six participants performing realistic fall and non-fall scenarios demonstrate strong window-level sensitivity under subject-independent evaluation, with XGBoost providing the most favorable sensitivity–specificity balance. Under sensitivity-first operation, very high recall is achieved at the expense of increased false alarms. Given the limited dataset and single-environment setting, the reported results should be interpreted as a proof-of-concept demonstration of feasibility rather than definitive large-scale performance. The findings suggest that BLE CS captures motion-relevant signal variations that may support practical fall detection while maintaining low deployment complexity and privacy preservation.