Projects:2020s1-2450 Ambient Intelligence Technology for Assisted Elderly Living
Contents
Ambient Intelligence Technology for Assisted Elderly Living
Introduction
The project will involve the development of a Smart Bed system designed for use by bedridden and elderly patients for monitoring and notification purposes, into a larger connected network. For this Honours project, there will be collaboration with a Mechanical Engineering Honours team that will also use the same smart bed system, but they are still considered separate projects. The team will focus on the development of custom software, making use of commercially-available sensors and components which will need to be integrated into a single system, culminating in a prototype smart bed for proof-of-concept demonstrations.
Project Team
Project Supervisor
- Associate Professor Mathias Baumert
Undergraduate Members
- Martin Gallo Picon: Bachelor of Engineering (Electrical and Electronic) (Biomedical) (Honours)
- Dingbang Lu: Bachelor of Engineering (Electrical and Electronic) (Honours)
Overview
System Description
The project goal is to develop a system capable of providing various patient monitoring functions to assist in elderly living, with comparable accuracy to clinical monitoring methods. Several patient vital signs have been identified as important for health monitoring purposes amongst vulnerable populace, these are heart rate, respiration rate and core body temperature. An additional function was requested by the project sponsor, Uniting Communities, which is a fall detection feature. Fall prevention has been requested because it is one of the more common and damaging problems for bedridden patients in aged care. Thus, the sponsor requested the group to design a method to detect potential falls, which would be used in conjunction with an actuator in future work to prevent falls before they occur. The scope of this project does not include design or implementation of actuators to prevent the falls that are detected, mainly due to time and budget constraints. The following diagram shows a high-level description of project functionality and data connections. Inputs are sent from three different sensor types, and this data is used to complete the expected functionality of the prototype.
Similar IoT health monitoring projects have been done in the past in terms of IoT health monitoring of vital signs, this project being based on several of these examples. The main difference is that the Smart Bed for Assistive Living aims to combine the most accurate monitoring methods across the health monitoring IoT projects that Group 2450 has researched. The result will be a working prototype that incorporates the sensing methods describe previously into a proof-of-concept prototype.
Smart Bed Decomposition
The Smart Bed itself consists of two separate, but connected components. The following section describes their function in high level, then proceeds to elaborate on the implementation methods.
Onboard Component of the Smart Bed
One of the two main parts of the smart bed is the onboard component, which consists of the hardware that is included in the bed overlay. This part includes all electronic components which are used to measure respiration rate, as well as the onboard computer which is used in all functions of the smart bed (which in this case is the Raspberry Pi 3). The fall detection system of the bed is also entirely contained within this part of the smart bed (which is based on load cell readings).
