Aged people who live independently require continuous monitoring of their health and activities of daily living in order to be supported by different health services and maintain their health status. This need can be addressed in the home setting, by providing a “health smart home” living environment for them. Using a health smart home approach has many advantages, such as, reducing the cost of health services by minimizing visits to hospitals, improving the quality of life for aged people recovering from illness at home instead of hospital, providing a secure and safe place for aged people who live independently, and routinely monitoring health status and daily activities to assist in improving health life of aged people. To provide such solution, it is required to classify the activities of daily living by using an activity recognition system. The development of sensing technologies that are cheap in price and provide an appropriate level of accuracy has opened the door for a wide range of research in the field of human activity recognition, including health applications. Different types of sensing technologies, modelling approaches and computational methods have been proposed for use in activity recognition systems, some of which are very complex. However, no one system solution has been widely accepted as optimal, providing scope for more investigations and improvements in this very rapidly growing area. The aim of this thesis is to develop a rule-based system to classify the activities of daily living in different hierarchical levels by using a cheap and sufficiently accurate ultrasonic location system (Hexamite19). Moreover, using a simple classification method based on initial application of activity distinguishing rules and then improving these results using finite state machine methods that can provide a high level of accuracy similar or better to previous research. In addition, a comparison of the system performance with existing classification methods is desirable, and in this case a decision-tree method (implemented in Sipina software) was used. To achieve the aims of the thesis, a systematic approach was followed, that included defining the research questions, setting up the experimental facility, selecting wearable sensor technology, collection of data on typical daily activities, development of methods for pre-processing of data followed by windowing, feature extracting, classification and finally the analysis of the rule-based system performance and accuracy. The rule-based system deployed three classification methods (range-based method, backward range-based method and symmetric range-based method). Range-based method deploys only rules, where backward range-based method and symmetric range-based method deploy rules and finite state machine extensions. The difference between backward range-based method and symmetric range-based method is the improvement of classification for undefined activity. System testing accuracy was used to assess the accuracy values of the different hierarchical levels. The rule-based system performance and accuracy was improved by using the finite state machine and the best method was symmetric range-based method for all hierarchical levels, except for the second hierarchical level where the accuracy of the three classification methods was equal. Moreover, it was found that the accuracy range of rule-based system was 83.4%-100%. By comparing the accuracy range of rule-based system with previous research and decision-tree method of Sipina software, it was found that the performance and accuracy of rule-based system were comparable with previous research and better in some cases. By using the decision-tree method of Sipina software, the accuracy range was 74.4%-99.3%. By comparing the accuracy range of rule-based system and decision-tree method of Sipina software, it is obvious that the rule-based system performance and accuracy was better, except for the activities sleep, walk straight and walk curvy. In conclusion, based on the analysis it was found that the rule-based system succeeded in classifying the activities of daily living into hierarchical levels; the finite state machine improved the accuracy of the rule-based system and the rule-based system accuracy was comparable with previous research and better than the decision-tree method of Sipina software (for all activities except for sleep, walk straight and walk curvy). It is therefore claimed that the deployed rule-based system has fulfilled the objectives of providing a robust and computationally inexpensive solution for common home-based activity recognition
