Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring
Continuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient priva...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Current Directions in Biomedical Engineering |
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| Online Access: | https://doi.org/10.1515/cdbme-2024-2086 |
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| author | Kalashtari Niloofar Huhs Niklas Kraitl Jens Hornberger Christoph Simanski Olaf |
| author_facet | Kalashtari Niloofar Huhs Niklas Kraitl Jens Hornberger Christoph Simanski Olaf |
| author_sort | Kalashtari Niloofar |
| collection | DOAJ |
| description | Continuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient privacy by capturing detailed visual information. LiDAR technology presents a non-intrusive alternative for continuous monitoring, offering effectiveness across various lighting conditions due to its use of laser light. It provides high-resolution depth information encoded as intensity, making it an ideal solution for maintaining patient privacy while delivering precise monitoring capabilities. LiDAR was selected as a reference method because of its precision and ability to capture detailed depth information, which are crucial for accurate monitoring. In this project, a solution that leverages LiDAR technology to enhance patient monitoring systems was introduced. By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. The high accuracy of the LiDAR sensor enables precise human tracking, which is critical for timely interventions and ensuring patient safety. This approach accelerates response times, preserves privacy, ensures safety during disease outbreaks, addresses healthcare worker shortages, and facilitates efficient monitoring of multiple patients simultaneously without invasive sensors. The ultimate goal of this project is to integrate this solution into Ambient Assisted Living (AAL) systems for elderly individuals, providing them with a safer and more autonomous living environment. Our trained YOLOv5 model has demonstrated exceptional performance, achieving a Mean Average Precision (mAP) of 99.4% at an Intersection over Union (IoU) threshold of 0.5, with a Precision of 99.6% and a Recall of 99.4%. |
| format | Article |
| id | doaj-art-cc1d34a6aa024d34832d1bc228fb8508 |
| institution | OA Journals |
| issn | 2364-5504 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj-art-cc1d34a6aa024d34832d1bc228fb85082025-08-20T01:47:45ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042024-12-0110435135510.1515/cdbme-2024-2086Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare MonitoringKalashtari Niloofar0Huhs Niklas1Kraitl Jens2Hornberger Christoph3Simanski Olaf4Hochschule Aalen, Beethovenstraße 1,Aalen, GermanyHochschule Wismar,Wismar, GermanyHochschule Wismar,Wismar, GermanyHochschule Wismar,Wismar, GermanyHochschule Wismar,Wismar, GermanyContinuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient privacy by capturing detailed visual information. LiDAR technology presents a non-intrusive alternative for continuous monitoring, offering effectiveness across various lighting conditions due to its use of laser light. It provides high-resolution depth information encoded as intensity, making it an ideal solution for maintaining patient privacy while delivering precise monitoring capabilities. LiDAR was selected as a reference method because of its precision and ability to capture detailed depth information, which are crucial for accurate monitoring. In this project, a solution that leverages LiDAR technology to enhance patient monitoring systems was introduced. By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. The high accuracy of the LiDAR sensor enables precise human tracking, which is critical for timely interventions and ensuring patient safety. This approach accelerates response times, preserves privacy, ensures safety during disease outbreaks, addresses healthcare worker shortages, and facilitates efficient monitoring of multiple patients simultaneously without invasive sensors. The ultimate goal of this project is to integrate this solution into Ambient Assisted Living (AAL) systems for elderly individuals, providing them with a safer and more autonomous living environment. Our trained YOLOv5 model has demonstrated exceptional performance, achieving a Mean Average Precision (mAP) of 99.4% at an Intersection over Union (IoU) threshold of 0.5, with a Precision of 99.6% and a Recall of 99.4%.https://doi.org/10.1515/cdbme-2024-2086object detection (od)lidardeep learning (dl)yolov5osdomereal-time |
| spellingShingle | Kalashtari Niloofar Huhs Niklas Kraitl Jens Hornberger Christoph Simanski Olaf Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring Current Directions in Biomedical Engineering object detection (od) lidar deep learning (dl) yolov5 osdome real-time |
| title | Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring |
| title_full | Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring |
| title_fullStr | Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring |
| title_full_unstemmed | Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring |
| title_short | Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring |
| title_sort | deep learning based real time human detection system using lidar data for smart healthcare monitoring |
| topic | object detection (od) lidar deep learning (dl) yolov5 osdome real-time |
| url | https://doi.org/10.1515/cdbme-2024-2086 |
| work_keys_str_mv | AT kalashtariniloofar deeplearningbasedrealtimehumandetectionsystemusinglidardataforsmarthealthcaremonitoring AT huhsniklas deeplearningbasedrealtimehumandetectionsystemusinglidardataforsmarthealthcaremonitoring AT kraitljens deeplearningbasedrealtimehumandetectionsystemusinglidardataforsmarthealthcaremonitoring AT hornbergerchristoph deeplearningbasedrealtimehumandetectionsystemusinglidardataforsmarthealthcaremonitoring AT simanskiolaf deeplearningbasedrealtimehumandetectionsystemusinglidardataforsmarthealthcaremonitoring |