Falls are a major public health concern in today’s aging society. Virtual reality (VR) technology is a promising method for reducing fall risk. However, the absence of representations of the user’s body in a VR environment lessens the spatial sense of presence. In terms of user experience, augmented reality (AR) can provide a higher degree of presence and embodiment than VR. We developed an AR-based exergame system that is specifically designed for the elderly to reduce fall risk. Kinect2.0 was used to capture and generate 3D models of the elderly and immerse them in an interactive virtual environment. The software included three functional modules: fall risk assessment, cognitive–motor intervention (CMI) training, and training feedback. The User Experience Questionnaire (UEQ-S) was used to evaluate user experience. Twenty-five elders were enrolled in the study. It was shown that the average scores for each aspect were: pragmatic quality score (1.652 ± 0.868); hedonic quality score (1.880 ± 0.962); and overall score was 1.776 ± 0.819. The overall score was higher than 0.8, which means that the system exhibited a positive user experience. After comparing the average score in a dataset product of UEQ-S Data Analysis Tool, it was found that the pragmatic quality aspect was categorized as good, while the hedonic quality aspect was categorized as excellent. It revealed a positive evaluation from users.

Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient.

In this paper, a new method for robot path rolling planning in a static and unknown environment based ongrid modelling is proposed.

Due to the increasing threat of terrorism, it has become more and more important to detect abnormal behaviour in public areas. In this paper, we introduce a system to identify pedestrians with abnormal movement trajectories in a scene using a data-driven approach. Our system includes two parts. The first part is an interactive tool that takes an overhead video as an input and tracks the pedestrians in a semi-automatic manner. The second part is a data-driven abnormal trajectories detection algorithm, which applies iterative k-means clustering to find out possible paths in the scene and thereby identifies those that do not fit well in any paths. Since the system requires only RGB video, it is compatible with most of the closed-circuit television (CCTV) systems used for security monitoring. Furthermore, the training of the abnormal trajectories detection algorithm is unsupervised and fully automatic. It means that the system can be deployed into a new location without manual parameter tuning and training data annotations. The system can be applied in indoor and outdoor environments and is best for automatic security monitoring.