Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78741
Title: Real-time vitals-based rerouting of hospital bed transport system
Authors: Wee, Andrew John Jia Rong
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Abstract: The Ministry of health has a plan for the year 2020 to meet the challenges of increasing aging population in Singapore. Healthcare sector workforce needs new skills and to leverage upon technology to meet this growing sector. A possible way to leverage on technology is to explore the area delegating menial tasks to devices that utilize autonomous technology that requires little or no human supervision. This will better meet the demand for the growing sector In this project, the area that is explored is to utilize autonomous technology is the hospital bed transportation. Traditionally the hospital bed transport requires a minimum of 2 persons to push the bed. As of the present, a semi-autonomous motorized bed experimental solution has been explored. However, a fully autonomous solution is not been explored yet. For this project, the aim is to explore the usage of machine learning techniques and algorithms to assess the patient’s vital signs during autonomous hospital bed transport. By assessing the patient’s vital signs using machine learning, the autonomous hospital bed will try to emulate a human nurse decision making process to reroute the bed destination if the patient faces complication. In this report, the basic principles of the machine learning techniques and model applicable to this project will be presented and the results of applying the machine learning model will be analysed and discussed
URI: http://hdl.handle.net/10356/78741
Schools: School of Mechanical and Aerospace Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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