Detecting Abnormal Health Conditions in Smart Home Using a Drone
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914637152256000 |
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| author | Barman, Pronob Kumar |
| author_facet | Barman, Pronob Kumar |
| contents | Nowadays, detecting aberrant health issues is a difficult process. Falling, especially among the elderly, is a severe concern worldwide. Falls can result in deadly consequences, including unconsciousness, internal bleeding, and often times, death. A practical and optimal, smart approach of detecting falling is currently a concern. The use of vision-based fall monitoring is becoming more common among scientists as it enables senior citizens and those with other health conditions to live independently. For tracking, surveillance, and rescue, unmanned aerial vehicles use video or image segmentation and object detection methods. The Tello drone is equipped with a camera and with this device we determined normal and abnormal behaviors among our participants. The autonomous falling objects are classified using a convolutional neural network (CNN) classifier. The results demonstrate that the systems can identify falling objects with a precision of 0.9948. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_05012 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Detecting Abnormal Health Conditions in Smart Home Using a Drone Barman, Pronob Kumar Computer Vision and Pattern Recognition Nowadays, detecting aberrant health issues is a difficult process. Falling, especially among the elderly, is a severe concern worldwide. Falls can result in deadly consequences, including unconsciousness, internal bleeding, and often times, death. A practical and optimal, smart approach of detecting falling is currently a concern. The use of vision-based fall monitoring is becoming more common among scientists as it enables senior citizens and those with other health conditions to live independently. For tracking, surveillance, and rescue, unmanned aerial vehicles use video or image segmentation and object detection methods. The Tello drone is equipped with a camera and with this device we determined normal and abnormal behaviors among our participants. The autonomous falling objects are classified using a convolutional neural network (CNN) classifier. The results demonstrate that the systems can identify falling objects with a precision of 0.9948. |
| title | Detecting Abnormal Health Conditions in Smart Home Using a Drone |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.05012 |