Deep learning enabled fall detection exploiting gait analysis

• Main Authors: Anwary, Arif Reza, Rahman, Md Arafatur, Abu Jafar, Md Muzahid, Ul Ashraf, Akanda Wahid, Patwary, Mohammad Nuruzzaman, Hussain, Amir
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: 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; http://umpir.ump.edu.my/id/eprint/39412/; https://doi.org/10.1109/EMBC48229.2022.9871964

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...