Ahmad Haj Mosa

In Active and Assisted Living environments (AAL), a major service that can be provided is the automated assessment of old people’s well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of the paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS dataset and artificially generated laboratory data using our HBMS simulator. The results show a high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches.

With respect to automotive safety, the driver plays a crucial role. Stress level, tiredness, and distraction of the driver are therefore of high interest. In this paper, a driver state detection system based on cellular neural networks (CNNs) to monitor the driver's stress level is presented. We propose to include a capacitive-based wireless hand detection (position and touch) sensor for a steering wheel utilizing ink-jet printed sensor mats as an input sensor in order to improve the performance. A driving simulator platform providing a realistic virtual traffic environment is utilized to conduct a study with 22 participants for the evaluation of the proposed system. Each participant is driving in two different scenarios, each representing one of the two no-stress/stress driver states. A “threefold” cross validation is applied to evaluate our concept. The subject dependence is considered carefully by separating the training and testing data. Furthermore, the CNN approach is benchmarked against other state-of-the-art machine learning techniques. The results show a significant improvement combining sensor inputs from different driver inherent domains, giving a total related detection accuracy of 92%. Besides that, this paper shows that in case of including the capacitive hand detection sensor, the accuracy increases by 10%. These findings indicate that adding a subject-independent sensor, such as the proposed capacitive hand detection sensor, can significantly improve the detection performance.

Emotions play an extremely important role in how we make a decision, planning, reasoning and other human mental states. The recognition of these emotions is becoming a vital task for e-healthcare systems. Using bio-sensors such as Electroencephalogram (EEG) to recognise the mental state of patients that could need a special care offers an important feedback for Ambient Assisted Living (AAL). This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients. The proposed approach combines wavelet energy, modified energy, wavelet entropy and statistical features to classify four emotion states. Three different classifiers are used (quadratic discriminant analysis, k-nearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested based on the EEG database “DEAP” using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.87% is obtained.

Electroencephalogram (EEG) signals play an important role in e-healthcare systems, especially to recognise the mental state of patients that could need a special care. This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients for Ambient Assisted Living (AAL). The proposed approach combines wavelet energy, modified energy and wavelet entropy features to classify four different classes of emotions. Three different classifiers are used (quadratic discriminant analysis (QDA), k-nearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested using the EEG database \"DEAP\" using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.2% is obtained.