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Related Experiment Video

Updated: May 17, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System.

Nasrettin Koksal¹, AbdulRahman Ghannoum², William Melek¹

¹Mechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

This study introduces an Intelligent Bluetooth Virtual Door system for occupancy monitoring. It uses Bluetooth low energy signals and machine learning for accurate indoor/outdoor tracking without needing floor plans.

Keywords:

BLE RSSI deep learning hybrid learning neural network occupancy monitoring

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

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Last Updated: May 17, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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Published on: February 8, 2019

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

Occupancy monitoring (OM) is crucial for smart environments but faces challenges in power consumption and cost.
Bluetooth low energy (BLE) offers an energy- and cost-efficient solution for wearable device communication.
Integrating BLE Received Signal Strength Indicator (RSSI) with machine learning (ML) enables AI-enhanced OM.

Purpose of the Study:

To propose an Intelligent Bluetooth Virtual Door (IBVD) system for indoor/outdoor occupancy monitoring.
To leverage ML algorithms for intelligent OM using BLE RSSI signals.
To develop a reliable tracking system that does not require floorplan information.

Main Methods:

Utilized BLE devices worn by occupants and two BLE beacons at doorways.
Employed ML algorithms for pattern detection from BLE RSSI signals for OM.
Evaluated system performance across various ML models.

Main Results:

Achieved classification accuracy between 96.6% and 97.3% for all tested ML models.
Demonstrated a highly reliable indoor/outdoor tracking system.
Showcased a system that functions without requiring floorplan data.

Conclusions:

The IBVD system provides an accurate and reliable solution for occupancy monitoring and individual tracking.
The AI-enhanced approach using BLE RSSI and ML offers a cost- and energy-efficient alternative.
This technology has significant potential for applications in home automation, smart offices, and emergency management.