Deep learning enabled fall detection exploiting gait analysis
Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we p...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39412/ http://umpir.ump.edu.my/id/eprint/39412/1/Deep%20Learning%20enabled%20Fall%20Detection%20exploiting%20Gait%20Analysis.pdf http://umpir.ump.edu.my/id/eprint/39412/2/Deep%20learning%20enabled%20fall%20detection%20exploiting%20gait%20analysis_ABS.pdf |
| _version_ | 1848825762353774592 |
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| author | Anwary, Arif Reza Rahman, Md Arafatur Abu Jafar, Md Muzahid Ul Ashraf, Akanda Wahid Patwary, Mohammad Nuruzzaman Hussain, Amir |
| author_facet | Anwary, Arif Reza Rahman, Md Arafatur Abu Jafar, Md Muzahid Ul Ashraf, Akanda Wahid Patwary, Mohammad Nuruzzaman Hussain, Amir |
| author_sort | Anwary, Arif Reza |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434mathrm{x}2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives. |
| first_indexed | 2025-11-15T03:34:05Z |
| format | Conference or Workshop Item |
| id | ump-39412 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:34:05Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-394122023-11-28T05:05:54Z http://umpir.ump.edu.my/id/eprint/39412/ Deep learning enabled fall detection exploiting gait analysis Anwary, Arif Reza Rahman, Md Arafatur Abu Jafar, Md Muzahid Ul Ashraf, Akanda Wahid Patwary, Mohammad Nuruzzaman Hussain, Amir QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434mathrm{x}2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39412/1/Deep%20Learning%20enabled%20Fall%20Detection%20exploiting%20Gait%20Analysis.pdf pdf en http://umpir.ump.edu.my/id/eprint/39412/2/Deep%20learning%20enabled%20fall%20detection%20exploiting%20gait%20analysis_ABS.pdf Anwary, Arif Reza and Rahman, Md Arafatur and Abu Jafar, Md Muzahid and Ul Ashraf, Akanda Wahid and Patwary, Mohammad Nuruzzaman and Hussain, Amir (2022) Deep learning enabled fall detection exploiting gait analysis. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 , 11-15 July 2022 , Glasgow. pp. 4683-4686., 2022-July (182637). ISSN 1557-170X ISBN 978-172812782-8 (Published) https://doi.org/10.1109/EMBC48229.2022.9871964 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Anwary, Arif Reza Rahman, Md Arafatur Abu Jafar, Md Muzahid Ul Ashraf, Akanda Wahid Patwary, Mohammad Nuruzzaman Hussain, Amir Deep learning enabled fall detection exploiting gait analysis |
| title | Deep learning enabled fall detection exploiting gait analysis |
| title_full | Deep learning enabled fall detection exploiting gait analysis |
| title_fullStr | Deep learning enabled fall detection exploiting gait analysis |
| title_full_unstemmed | Deep learning enabled fall detection exploiting gait analysis |
| title_short | Deep learning enabled fall detection exploiting gait analysis |
| title_sort | deep learning enabled fall detection exploiting gait analysis |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39412/ http://umpir.ump.edu.my/id/eprint/39412/ http://umpir.ump.edu.my/id/eprint/39412/1/Deep%20Learning%20enabled%20Fall%20Detection%20exploiting%20Gait%20Analysis.pdf http://umpir.ump.edu.my/id/eprint/39412/2/Deep%20learning%20enabled%20fall%20detection%20exploiting%20gait%20analysis_ABS.pdf |