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Visual Fall Detection Analysis Through Computer Vision and Deep Learning – Technology Proposition
C Kiranmai1, B Srivalli2, CH Komali3, G Apurva4, B Sneha Yesshaswi5
1Dr. C Kiranmai, Department of Computer Science, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
2B Srivalli, Department of Computer Science Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
3CH Komali, Department of Computer Science Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
4G Apurva, Department of Computer Science Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
5B Sneha Yesshaswi, Department of Computer Science Engineering, Yesshaswi, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
Manuscript received on 06 March 2024 | Revised Manuscript received on 13 March 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 1-4 | Volume-13 Issue-1, May 2024 | Retrieval Number: 100.1/ijrte A802913010524 | DOI: 10.35940/ijrte.A8029.13010524
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Advances in modern medicine have increased humans’ life span. Older adults face mobility problems as they age. They also feel less fit to continue any activity for short intervals. This is due to declining fitness levels or muscle strength, diminished dexterity, and loss of balance. These symptoms can lead to the individual’s fall and sometimes be fatal if not immediately attended to. It’s an alarming issue for people staying alone. They may pose significant health risks and need immediate assistance. Fall detection technologies are majorly categorised as wearable sensors and ambient sensors. Fall detection wearable devices, such as pendant necklaces, watches, and wristband devices, as well as clip-on medical alerts, use accelerometers to detect rapid downward movements that can indicate a fall. They often also include manual alert buttons for increased accuracy. This requires a level of comfort and awareness with technology for practical usage. Ambient home sensors use video cameras to monitor the user’s movement and detect falls. When the fall is transmitted to a monitoring centre, a representative typically calls the user to check on them before notifying contacts or calling for emergency services, but this can depend on the user’s preferences and risk factors. In this paper, we propose a technology that utilises security cameras to record videos and create a video-based fall detection system. The system utilises computer vision and deep learning algorithms to recognise fall-related movements and distinguish them from regular activities accurately. This system can be integrated to prompt alerts to emergency contacts, thus assisting in providing immediate aid to individuals who have experienced a fall. For higher accuracy, this system integrates multiple-angle videos and multi-person tracking to estimate the intensity of the fall, enabling immediate attention. Thus, this fall detection system can contribute to the safety, well-being and independence of individuals at risk of falling.
Keywords: Vision-based Fall Detection, Human Pose Estimation, Multi-directional Fall Detection, Multi-person Tracking, Fall Intensity Detection, Healthcare Monitoring, Deep Learning, Computer Vision
Scope of the Article: Deep Learning