Development of situation recognition, environment monitoring and patient condition monitoring service modules for hospital robots
Al Mamun, Md. Kabir (2012) Development of situation recognition, environment monitoring and patient condition monitoring service modules for hospital robots. PhD thesis, Dublin City University.
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An aging society and economic pressure have caused an increase in the patient-to-staff ratio leading to a reduction in healthcare quality. In order to combat the deficiencies in the delivery of patient healthcare, the European Commission in the FP6 scheme approved the financing of a research project for the development of an Intelligent Robot Swarm for Attendance, Recognition, Cleaning and Delivery (iWARD). Each iWARD robot contained a mobile, self-navigating platform and several modules attached to it to perform their specific tasks.
As part of the iWARD project, the research described in this thesis is interested to develop hospital robot modules which are able to perform the tasks of surveillance and patient monitoring in a hospital environment for four scenarios: Intruder detection, Patient behavioural analysis, Patient physical condition monitoring, and Environment monitoring. Since the Intruder detection and Patient behavioural analysis scenarios require the same equipment, they can be combined into one common physical module called Situation recognition module. The other two scenarios are to be served by their separate modules: Environment monitoring module and Patient condition monitoring module.
The situation recognition module uses non-intrusive machine vision-based concepts. The system includes an RGB video camera and a 3D laser sensor, which monitor the environment in order to detect an intruder, or a patient lying on the floor. The system deals with various image-processing and sensor fusion techniques.
The environment monitoring module monitors several parameters of the hospital environment: temperature, humidity and smoke.
The patient condition monitoring system remotely measures the following body conditions: body temperature, heart rate, respiratory rate, and others, using sensors attached to the patient’s body.
The system algorithm and module software is implemented in C/C++ and uses the OpenCV image analysis and processing library and is successfully tested on Linux (Ubuntu) Platform. The outcome of this research has significant contribution to the robotics application area in the hospital environment.
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