Context-aware activity recognition and abnormality detection approaches in smart home environments

The rising number of elderly population has become a common concern in many countries around the world. The issue has impacted social and economic life of modern societies due to the fact that elderly people are known to suffer from many medical disabilities. As one of the solutions, current technologically-driven approaches, particularly in the area of smart home environments have been developed in recent years to support the independent living and reduce the caregivers’ burden in taking care of elderly individuals. Sensors installed in the environments are used to monitor users’ daily routine to see trends in the behaviour and to be informed of any abnormal activity. However, the accurate interpretation of sensor data in identifying human activities and their abnormal behaviour is still limited. Furthermore, pattern analysis involving these two areas are becoming an increasingly scientific challenge to the real-world environments. This study intends to deal with the issue by investigating appropriate means of pattern recognition and data mining methods within smart home environments. In particular, the study attempts to develop an intelligent reasoning system that can identify residents’ activities and abnormal behaviour of the smart home residents. In this study, two types of activities are identified, i.e., context-related and motion-related activities. The former is classified using the hybrid approach while the latter is performed through the ensemble-based machine learning techniques. The output models produced by these activity recognition approaches are then used as the input for the deep learning networks to produce behavioural model of smart home residents. Experimental procedures are then performed to validate the proposed approach. First, a comparison between the knowledge-driven model and hybrid activity model is carried out to identify the context-related activity. Then, another comparison between the performances of single classifier with multi-classifier system is also performed to identify the motion related activity. Furthermore, for the abnormality detection, several types of reasoning systems are used. These include the case-based reasoning (CBR), deep learning models composed of multi-layer perceptron network (DMLP) and deep recurrent neural network (DRNN) as well as the conventional machine learning algorithms such as naïve Bayes (NB), Support Vector Machine (SVM) and multi-layer perceptron neural network (MLP). The experimental results show that the proposed hybrid approach has better classification rate to identify context-related activity compared to the knowledge-driven model, where the accuracy is obtained at 98.7% ± 0.4. Meanwhile, the multi-classifier system performs better than a single classifier in identifying motion-related activity, with the accuracy of 99.6% ± 0.2. Moreover, DMLP shows higher accuracy rate (98.2%) compared to the DRNN, CBR and other machine learning algorithms for the abnormality detection system. The presented results show that this study can give an impact to the improvement of reasoning process in identifying abnormal situations in smart homes. This can be used in many applications especially in healthcare domains. Furthermore, this study helps to benefit future technologists in order to achieve Society 5.0.