BLE Signal Processing and Machine Learning for Indoor Behavior Classification
Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4496 |
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| author | Yi-Shiun Lee Yong-Yi Fanjiang Chi-Huang Hung Yung-Shiang Huang |
| author_facet | Yi-Shiun Lee Yong-Yi Fanjiang Chi-Huang Hung Yung-Shiang Huang |
| author_sort | Yi-Shiun Lee |
| collection | DOAJ |
| description | Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. |
| format | Article |
| id | doaj-art-9df66c07394f4ed5bf62c5c5946583fe |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9df66c07394f4ed5bf62c5c5946583fe2025-08-20T03:32:16ZengMDPI AGSensors1424-82202025-07-012514449610.3390/s25144496BLE Signal Processing and Machine Learning for Indoor Behavior ClassificationYi-Shiun Lee0Yong-Yi Fanjiang1Chi-Huang Hung2Yung-Shiang Huang3Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanGraduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanGraduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanDepartment of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanSmart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications.https://www.mdpi.com/1424-8220/25/14/4496BLE-based indoor positioningmachine learning for behavior analysisprivacy-preserving health monitoringwearable IoT for remote health trackingAI-driven fall detectionsmart home healthcare |
| spellingShingle | Yi-Shiun Lee Yong-Yi Fanjiang Chi-Huang Hung Yung-Shiang Huang BLE Signal Processing and Machine Learning for Indoor Behavior Classification Sensors BLE-based indoor positioning machine learning for behavior analysis privacy-preserving health monitoring wearable IoT for remote health tracking AI-driven fall detection smart home healthcare |
| title | BLE Signal Processing and Machine Learning for Indoor Behavior Classification |
| title_full | BLE Signal Processing and Machine Learning for Indoor Behavior Classification |
| title_fullStr | BLE Signal Processing and Machine Learning for Indoor Behavior Classification |
| title_full_unstemmed | BLE Signal Processing and Machine Learning for Indoor Behavior Classification |
| title_short | BLE Signal Processing and Machine Learning for Indoor Behavior Classification |
| title_sort | ble signal processing and machine learning for indoor behavior classification |
| topic | BLE-based indoor positioning machine learning for behavior analysis privacy-preserving health monitoring wearable IoT for remote health tracking AI-driven fall detection smart home healthcare |
| url | https://www.mdpi.com/1424-8220/25/14/4496 |
| work_keys_str_mv | AT yishiunlee blesignalprocessingandmachinelearningforindoorbehaviorclassification AT yongyifanjiang blesignalprocessingandmachinelearningforindoorbehaviorclassification AT chihuanghung blesignalprocessingandmachinelearningforindoorbehaviorclassification AT yungshianghuang blesignalprocessingandmachinelearningforindoorbehaviorclassification |