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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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BLE Signal Processing and Machine Learning for Indoor Behavior Classification.

Yi-Shiun Lee¹, Yong-Yi Fanjiang^1,2, Chi-Huang Hung^1,3

¹Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan.

Sensors (Basel, Switzerland)

|July 30, 2025

Summary

This summary is machine-generated.

This study introduces a privacy-preserving smart home system using Bluetooth Low Energy (BLE) for health monitoring. It accurately recognizes user behavior and location without cameras, enhancing in-home care.

Keywords:

AI-driven fall detection BLE-based indoor positioning machine learning for behavior analysis privacy-preserving health monitoring smart home healthcare wearable IoT for remote health tracking

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Area of Science:

Health Informatics
Ubiquitous Computing
Machine Learning

Background:

Smart home technology improves in-home care and health monitoring.
Privacy concerns limit the use of video-based behavior analysis.
Non-visual alternatives are needed for sustainable and private health monitoring.

Purpose of the Study:

To propose a Bluetooth Low Energy (BLE)-enabled system for indoor positioning and behavior recognition.
To develop a privacy-preserving solution for smart healthcare applications.
To support elderly care and remote health monitoring through non-invasive behavior analysis.

Main Methods:

Utilized a vertically mounted Data Collection Unit (DCU) for enhanced height positioning.
Implemented synchronized data collection and Kalman filtering for signal smoothing (RSSI).
Employed AI-based RSSI analysis for accurate behavior recognition using a smart wristband.

Main Results:

The system reliably tracks user locations within a home environment.
Successfully identifies various user behavior patterns (standing, sitting, lying down).
Demonstrated the effectiveness of BLE signal variations for activity recognition.

Conclusions:

The proposed BLE system offers a privacy-preserving alternative for health monitoring.
This technology supports sustainable elderly care and remote patient observation.
Enables non-invasive behavior analysis for smart healthcare applications.