AbstractThe elderly population worldwide has an increasing expectation of wellbeing and life expectancy. The monitoring of the majority of elderly people on an individual basis, in a medical sense, will not be a viable proposition in the future due to the projected numbers of individuals requiring such activity. The expectation is that the infrastructure available will not be adequate to meet all the anticipated requirements and subsequently people will have to live at home with inadequate care. A new global objective that aims towards enhancing the quality of life of the elderly is being supported by extensive research. This research has been taking place in the field of ambient intelligence (AmI), considering factors including more comfort, improved health, enhanced security for the elderly, and facilitating the living in their homes longer. Prior research has shown a need for accelerated expansion in the ambient intelligence domain. To that end this work presents a novel learning technique for intelligent agents that can be used in Ambient Intelligent Environments (AIEs).
The main objective of this work is to add knowledge to the AmI domain and to explore the practical applications within this research field. The added knowledge is accomplished through the development of an ambient intelligent health care environment that allows a practical assessment of the human well-being to take place. This is achieved by transforming the elderly living environment into an intelligent pseudo robot within which they reside to better understand the human wellbeing.
The system developed aims to provide evidence that a level of automated care is both possible and practical. This care is for those with chronic physical or mental disabilities who have difficulty in their interactions with standardised living spaces. The novel integrated hardware and software architecture provides personalised environmental monitoring. It also provides control facilities based on the patient‘s physical and emotional wellness in their home.
Entitled Health Adaptive Online Emotion Fuzzy Agent (HAOEFA), the system provides a non-invasive, self-learning, intelligent controlling system that constantly adapts to the requirements of an individual. The system has the ability to model and learn the user behaviour in order to control the environment on their behalf. This is achieved with respect to the changing environmental conditions as well as the user‘s health and emotional states being detected. A change of emotion can have a direct impact on the system‘s control taking place in the environment. Thus HAOEFA combines an emotion recognition system within a fuzzy logic learning and adaptation based controller. The emotion recogniser detects the occupant‘s emotions upon the changes of the physiological data being monitored. In addition to acting as an output to the occupant‘s physiological changes, the detected emotion also acts as input to the whole situation being observed by HAOEFA. This allows HAOEFA to control the Glam i-HomeCare on the user‘s behalf with respect to their emotional status.
The system developed incorporates real-time, continuous adaptations to facilitate any changes to the occupant‘s behaviour within the environment. It also allows the rules to be adapted and extended online, assisting a life-long learning technique as the environmental conditions change and the user behaviour adjusts with it. HAOEFA uses the fuzzy c-means clustering methodology for extracting membership functions (MFs) before building its set of fuzzy rules. These MFs together with the rules base constitute a major part of the proposed system. It has the ability to learn and model the individual human behaviour with respect to their emotional status.
Following the provided literature review and the presentation of Fuzzy logic MFs (see section 3.3). The thesis presents two chosen unobtrusive self-learning techniques that are used in the development of the intelligent fuzzy system. Each approach combines an emotion recogniser with a fuzzy logic learning and adaptation based technique for systems that can be used in AIEs. A comparison of two different MFs designs is contrasted showing the impact they have on the system learning ability. A number of carefully designed experiments were performed by volunteers in the Glam i-HomeCare test-bed at the University of South Wales to examine the system‘s ability to learn the occupant‘s behaviour with respect to their health and emotional states. The experimental procedures were performed twice by each volunteer, while maintaining the same behavioural actions to compare how much the design of fuzzy membership functions can impact the learning process and the number of rules created by the system. Besides evaluating both systems‘ emotion recognition accuracies and comparing them to one another for each occupant, the empirical outcomes show the potential of the approach in assisting the extension of independent living. The results demonstrate how the type-1 fuzzy system both learnt and adapted to each occupant‘s behaviour with respect to their health and emotional state whilst assessing multiple environmental conditions.
|Date of Award
|23 Sept 2014
|Janusz Kulon (Supervisor) & Steven Wilcox (Supervisor)
- Ambient Intelligence
- Intelligent Agents
- Ambient Intelligent Environments
- automated care