Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem. The DRHN network accepts the RGB-Depth frame sequences and produces a representation of semantically related posture states. We achieved 91.47% accuracy at 10 fps rate for sitting posture recognition.

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53% which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process.

In the pedagogical process, a serious game acts as a method of teaching and upbringing, the transfer of accumulated experience and knowledge. In this paper, we describe an interactive serious programming game based on game-based learning for teaching JavaScript programming in an introductory course at university. The game was developed by adopting the gamification pattern-based approach. The game is based on visualizations of different types of algorithms, which are interpreted in the context of city life. The game encourages interactivity and pursues deeper learning of programming concepts. The results of the evaluation of the game using pre-test and post-test knowledge assessment, the Technology Acceptance Model (TAM), and the Technology-Enhanced Training Effectiveness Model (TETEM) are presented.

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.

Low back pain is a leading cause of disability worldwide, putting a significant strain on individual sufferers, their families, and the economy as a whole. It has a significant economic impact on the global economy because of the costs associated with healthcare, lost productivity, activity limitation, and work absence. Self-management, education, and adopting healthy lifestyle behaviors, such as increasing physical activity, are all widely recommended treatments. Access to services provided by healthcare professionals who provide these treatments can be limited and costly. This evaluation study focuses on the application of the MyRelief serious game, with the goal of addressing such challenges by providing an accessible, interactive, and fun platform that incorporates self-management, behavior change strategies, and educational information consistent with recommendations for managing low-back pain, based on self-assessment models implemented through ontology-based mechanics. Functional disability measured using the Oswestry Disability Questionnaire showed the statistically significant (p < 0.001) improvement in subjects’ self-evaluation of their health status. System Usability Scale (SUS) test score of 77.6 also suggests that the MyRelief serious game can potentially influence patient enablement.