Building A Wheelchair Controlling and Fall Detection System Using Mobile Application

Rahman, Md. Atiqur; Ahsanuzzaman, S.M; Hasan, Abid; Rahman, Ishman; Ahmed, Toufik; Kadir, Monirul

doi:10.1109/icaict51780.2020.9333478

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…In Rahman et al (2020), a prototype of wheelchairs with an FD structure can be advanced that can be controlled by an android application. A nurse or caretaker can control this developed prototype in various directions with speed variations; the patient's position is observed.…”

Section: Related Workmentioning

confidence: 99%

Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection Approach to Aid Elderly People

Alabdulkreem¹,

Marzouk²,

Alduhayyem³

et al. 2023

Journal of Disability Research

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Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.

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Section: Related Workmentioning

confidence: 99%