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Similarity measures for analyzing human behavior patterns are inseparable part of the intelligent environment, with the assistive functionality as its core value. The measure must represent the contexts properly which characterizes the users' environment. Recent studies attempted to formulate similarity measures for the intelligent environment by incorporating relevant contexts. Yet, they are lacking the integration of multiple inter-related important contexts, which leads to model underestimation and possibly the wrong interpretation. This work proposes a context-based similarity measure for analyzing human behavior patterns. The proposed similarity measure extends and combines the commonly used contexts (i.e., activity, location, and time) into a holistic measure. To avoid the biased representation of activity context similarity, we add one more aspect, namely process context, which describes a wide range of interval relations among the activities of a user. The proposed approach is compared with state-of-the art similarity measures by evaluating both real and simulated data. The result shows that our approach yields the better result in terms of robustness toward noises. In addition, our approach also shows a better reliability compared to previous works in the case of anomaly detection. [ABSTRACT FROM AUTHOR]

Human behavior computing has received a lot of attention in many business domains to derive daily routines, such as company's regular operations and cultural activities, especially towards to the development of smart industries in the fourth industrial revolution. Using the recent technology such as adaptive sensor devices, human movement and activity can be detected and traced. Discovering daily routines from lifelog can assist the growth of individual and organizations but yet needs a reliable approach to produce a representative behavior model. Some conformance checking approaches have been developed to verify the conformity of a real log with a generated model. However, the existing conformance checking approach relies on the business process property which is less reliable to the human behavior. Accordingly, limited approaches attempt to assess the conformity between the discovered model and self-elicited behavior (lifelog). This study presents norm approaches to assess the deviation between model space and lifelog. Two approaches, approximate and aggregate norm, are proposed to evaluate the discovered (routine) model according to the respective lifelog and a particular person. In this study, the model has been derived using the concept of time-lapse embedded in the sequential rules. The result of the experiment using the two-norm approaches shows that the routine models have 99% characteristics compared with the lifelog. [ABSTRACT FROM AUTHOR]

Physical activity recognition using wearable sensors has gained significant interest from researchers working in the field of ambient intelligence and human behavior analysis. The problem of multi-class classification is an important issue in the applications which naturally has more than two classes. A well-known strategy to convert a multi-class classification problem into binary sub-problems is the error-correcting output coding (ECOC) method. Since existing methods use a single classifier with ECOC without considering the dependency among multiple classifiers, it often fails to generalize the performance and parameters in a real-life application, where different numbers of devices, sensors and sampling rates are used. To address this problem, we propose a unique hierarchical classification model based on the combination of two base binary classifiers using selective learning of slacked hierarchy and integrating the training of binary classifiers into a unified objective function. Our method maps the multi-class classification problem to multi-level classification. A multi-tier voting scheme has been introduced to provide a final classification label at each level of the solicited model. The proposed method is evaluated on two publicly available datasets and compared with independent base classifiers. Furthermore, it has also been tested on real-life sensor readings for 3 different subjects to recognize four activities i.e. Walking, Standing, Jogging and Sitting. The presented method uses same hierarchical levels and parameters to achieve better performance on all three datasets having different number of devices, sensors and sampling rates. The average accuracies on publicly available dataset and real-life sensor readings were recorded to be 95% and 85%, respectively. The experimental results validate the effectiveness and generality of the proposed method in terms of performance and parameters. [ABSTRACT FROM AUTHOR]

• A graph convolution model for skeleton-based action recognition is proposed. • Normalized Laplacian Matrix is utilized to encode the graph information. • An attention-based feature aggregation is proposed to extract the salient features. • The proposed method achieves better results than the baseline models. Skeleton-based human action recognition has attracted a lot of attention due to its capability and potential to provide more information than just using the sequence of RGB images. The use of Graph Convolutional Neural Network (GCN) becomes more popular since it can model the human skeleton very well. However, the existing GCN architectures ignore the different levels of importance on each hop during the feature aggregation and use the final hop information for further calculation, resulting in considerable information loss. Besides, they use the standard Laplacian or adjacency matrix to encode the property of a graph into a set of vectors which has a limitation in terms of graph invariants. In this work, we propose a Sequential Inter-hop Graph Convolution Neural Network (SIhGCN) which can capture salient graph information from every single hop rather than the final hop only and our work utilizes the normalized Laplacian matrix which provides better representation since it relates well to graph invariants. The proposed method is validated on two large datasets, NTU-RBG + D and Kinetics, to demonstrate the superiority of our proposed method. [ABSTRACT FROM AUTHOR]