Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects
(1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especiall...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3836 |
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| author | Riska Analia Anne Forster Sheng-Quan Xie Zhiqiang Zhang |
| author_facet | Riska Analia Anne Forster Sheng-Quan Xie Zhiqiang Zhang |
| author_sort | Riska Analia |
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| description | (1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especially in real-time, non-invasive applications; (2) Methods: This study proposes a lightweight, privacy-preserving, long-lie detection system utilizing thermal imaging and a soft-voting ensemble classifier. A low-resolution thermal camera captured simulated falls and activities of daily living (ADL) performed by ten healthy participants. Human pose keypoints were extracted using MediaPipe, followed by the computation of five handcrafted postural features. The top three classifiers—automatically selected based on cross-validation performance—formed the soft-voting ensemble. Long-lie conditions were identified through post-fall immobility monitoring over a defined period, using rule-based logic on posture stability and duration; (3) Results: The ensemble model achieved high classification performance with accuracy, precision, recall, and an F1 score of 0.98. Real-time deployment on a Raspberry Pi 5 demonstrated the system is capable of accurately detecting long-lie incidents based on continuous monitoring over 15 min, with minimal posture variation; (4) Conclusion: The proposed system introduces a novel approach to long-lie detection by integrating privacy-aware sensing, interpretable posture-based features, and efficient edge computing. It demonstrates strong potential for deployment in homecare settings. Future work includes validation with older adults and integration of vital sign monitoring for comprehensive assessment. |
| format | Article |
| id | doaj-art-e20a2c6961ec4e73943abf017a1caa30 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e20a2c6961ec4e73943abf017a1caa302025-08-20T03:26:52ZengMDPI AGSensors1424-82202025-06-012512383610.3390/s25123836Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy SubjectsRiska Analia0Anne Forster1Sheng-Quan Xie2Zhiqiang Zhang3School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UKAcademic Unit for Ageing and Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds LS2 9LN, UKSchool of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UKSchool of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK(1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especially in real-time, non-invasive applications; (2) Methods: This study proposes a lightweight, privacy-preserving, long-lie detection system utilizing thermal imaging and a soft-voting ensemble classifier. A low-resolution thermal camera captured simulated falls and activities of daily living (ADL) performed by ten healthy participants. Human pose keypoints were extracted using MediaPipe, followed by the computation of five handcrafted postural features. The top three classifiers—automatically selected based on cross-validation performance—formed the soft-voting ensemble. Long-lie conditions were identified through post-fall immobility monitoring over a defined period, using rule-based logic on posture stability and duration; (3) Results: The ensemble model achieved high classification performance with accuracy, precision, recall, and an F1 score of 0.98. Real-time deployment on a Raspberry Pi 5 demonstrated the system is capable of accurately detecting long-lie incidents based on continuous monitoring over 15 min, with minimal posture variation; (4) Conclusion: The proposed system introduces a novel approach to long-lie detection by integrating privacy-aware sensing, interpretable posture-based features, and efficient edge computing. It demonstrates strong potential for deployment in homecare settings. Future work includes validation with older adults and integration of vital sign monitoring for comprehensive assessment.https://www.mdpi.com/1424-8220/25/12/3836long-lie detectionthermal imagingensemble learningprivacy-preserving monitoringedge computing |
| spellingShingle | Riska Analia Anne Forster Sheng-Quan Xie Zhiqiang Zhang Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects Sensors long-lie detection thermal imaging ensemble learning privacy-preserving monitoring edge computing |
| title | Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects |
| title_full | Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects |
| title_fullStr | Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects |
| title_full_unstemmed | Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects |
| title_short | Privacy-Preserving Approach for Early Detection of Long-Lie Incidents: A Pilot Study with Healthy Subjects |
| title_sort | privacy preserving approach for early detection of long lie incidents a pilot study with healthy subjects |
| topic | long-lie detection thermal imaging ensemble learning privacy-preserving monitoring edge computing |
| url | https://www.mdpi.com/1424-8220/25/12/3836 |
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