Please use this identifier to cite or link to this item:
|Title:||Implementation of data analytics and generation of algorithms for non-intrusive healthcare monitoring system of vulnerable elderlies||Authors:||Tan, Brennon Joo Liang||Keywords:||Engineering::Mechanical engineering::Assistive technology||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Tan, B. J. L. (2022). Implementation of data analytics and generation of algorithms for non-intrusive healthcare monitoring system of vulnerable elderlies. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159174||Project:||A230||Abstract:||Over the years, Singapore has experienced a demographic shift of an overwhelming aging population. Due to higher life expectancy rates and lower birth rates, the repercussions on the country’s healthcare needs are more profound as the rapidly aging population proves to be a national concern. Vulnerable elderlies who live alone and have little to no contact with healthcare workers may be exposed to underlying hazards such as falls and certain personal health complications. This would be challenging for them to contact emergency services if an incident occurs. Additionally, the recent pandemic has placed an enormous strain on the entire healthcare system, and coupling it with the shortage of staff, could potentially lead to slower response times when there is an emergency. To aid this problem, a non-intrusive means of monitoring the vulnerable elderly with a 3-Dimensional (3D) solid-state Light Detection and Ranging (LiDAR) sensor is used to detect falls and track the day-to-day activity level of the individual in real space and time. As the world moves into the 4th industrial revolution, LiDAR sensors have seen enhanced usage in numerous automotive and healthcare industries due to their limitless application. The focus of this project implements this 3D solid-state LiDAR combined with the usage of the Robot Operating System (ROS), Point Cloud Libraries (PCL), and a Raspberry Pi 4 (Miniature Computer), to generate reliable algorithms for fall detection and object tracking coupled with exploring real-time programming of the surrounding environment.||URI:||https://hdl.handle.net/10356/159174||Schools:||School of Mechanical and Aerospace Engineering||Organisations:||Government Technology Agency (GovTech)||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
Updated on Sep 26, 2023
Updated on Sep 26, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.