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Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient’s room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator’s preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

Virtual chemistry laboratories (VCLs) are the alternative solutions of the physical laboratories, where students can virtually conduct their experiments with a lower cost, and in an efficient and safer way. Considering the importance of technology-enhanced learning and that of the experimental study, several VCLs have been proposed. However, the existing VCLs are static and only provide the simulation of pre-defined experiments, procedures, or safety procedures and cannot be adapted according to the students’ level or new experimental tasks. In this paper, we proposed a dynamic virtual chemistry lab (DVCL) where instructors or experts are allowed to add a new chemical experiment by adding its apparatus, chemicals, glassware, and mechanism or add something new to its properties. We conducted a subjective study with field experts to investigate the effect of proposed DVCL in secondary school chemistry education. During evaluation, twenty-seven field experts were participated and evaluated the proposed DVCL with system usability scale (SUS)-questionnaire and by a simple questionnaire. The results showed that the proposed DVCL is very helpful for students’ performance and mental modeling and also for effortlessly uplifting their knowledge for hands-on experiments.

In the context of Virtual Reality (VR), interactions refer to the plausible actions in a Virtual Environment (VE). To have an engrossing interface, interactions by the gestures of hand are becoming prominent. With this research work, a novel interaction technique is proposed where interactions are performed on the basis of the position of thumb in dynamic image stream. The technique needs no expensive tracker but an ordinary camera to trace hand movements and position of thumb. The interaction tasks are enacted in distinct interaction states, where the Angle of Inclination (AOI) of thumb is used for state-to-state transition. The angle is computed dynamically between the tip-of-thumb and the base of the Region of Interest (ROI) of an input image. The technique works in two phases: learning phase and application phase. In the learning phase, user-defined fistpostures with distinct AOI are learnt. The Support Vector Machine (SVM) classifier is trained by the AOI of the postures. In the application phase, interactions are performed in distinct interaction states whereas a particular state is activated by posing the known posture. To follow the trajectory of thumb, dynamic mapping is performed to control a virtual hand by the position of thumb in the input image. The technique is implemented in a Visual Studio project called Thumb-Based Interaction for Virtual Environments (TIVE). The project was evaluated by a group of 15 users in a moderate lighting condition. The 89.7% average accuracy rate of the evaluation proves suitability of the technique in the wide range VR applications.

Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body’s joints. To consider all of the joints, it is essential to determine a person’s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach’s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach’s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.

In science education laboratory experimentation has a vital role for students’ learning enhancement. Keeping in view the importance of modern day technologies in teaching learning process, various interactive laboratories (ISLs) have been developed to assist students in hands-on experiments in science education. In this paper we describe the potential contributions of existing interactive science laboratories (ISLs) in the major subjects of science, i.e., chemistry, biology and physics. The existing ISLs include virtual labs and simulation software where users performed their experiments. Important problems and challenges in the existing ISLs are highlighted. The systematic literature review (SLR) methodology is used for article searching, selection, and quality assessments. For this study, 86 articles after final selection using SLR are selected and classified into different categories. Each article is selected after briefly studying its different information, including category of the article, key idea, evaluation criterion, and its strengths and weaknesses. A subjective study with field experts was also conducted to investigate one of our existing virtual lab about the practical implementation and to find out the key issues in its implementation and use. Then, considering the suggestions of the subjective study, some guidelines are proposed for the improvement of future ISLs.

For Collaborative Virtual Environments (CVEs), many interaction techniques are developed. Depending on the purpose of the collaborative work, techniques of interaction and manipulation change from one application to another. There is no general, good and efficient solution for all the collaborative systems. In addition, people in CVEs also use communication channels to share task goals, task decomposition and task progress. Therefore, awareness and communication are usually considered as important instruments to complete collaborative task. In this paper we present a comparative study of user performance with assembly task across four different guidance navigation aids i.e. 3-Dimensional Map-Liner (3DML), Audio, Textual and Arrows-Casting. Dijkstra's algorithm is used for shortest path selection while making the assembly task in CVEs. We conducted experiments on different college students to check the performance of the students. We reported the results of a precise experiments containing of 24 virtual teams of 48 individual students. Overall results showed that students performed task faster using the arrows while they were slow with audio support in navigation.

Research demonstrates that individualized instruction is superior to the traditional one-size-fits-all teaching approach. The use of 3D virtual environments for educational purposes is becoming attractive because of their rich presentation, user friendly interaction techniques, and adaptive capabilities. However, defining the adaptive aspect of 3D virtual learning environments (3D-VLEs) is a challenging task. In this paper, we quantitatively measure learning skill of a student through a fuzzy logic-based approach and use it as adaptation criterion for changing the contents of 3D-VLEs. The system displays customize teaching materials for different students, which results in improved learning. The experimental results show that the proposed approach is effective and can be efficiently used to enhance the learning capabilities of students in 3D-VLEs.

Fiducial markers are images or landmarks placed in real environment, typically used for pose estimation and camera tracking. Reliable fiducials are strongly desired for many augmented reality (AR) applications, but currently there is no systematic method to design highly reliable fiducials. In this paper, we present fiducial marker optimizer (FMO), a tool to optimize the design attributes of ARToolKit markers, including black to white (B:W) ratio, edge sharpness, and information complexity, and to reduce inter-marker confusion. For these operations, the FMO provides a user friendly interface at the front-end and specialized image processing algorithms at the back-end. We tested manually designed markers and FMO optimized markers in ARToolKit and found that the latter were more robust. The FMO will be used for designing highly reliable fiducials in easy to use fashion. It will improve the application's performance, where it is used.

In virtual laboratories, various cognitive aids are used to improve students' performance and assist them while completing experimental tasks. However, excessive use of cognitive aids in virtual laboratories can lead to a cognitive load on students which affects their performance. In this paper, we proposed the concept of adaptive aids virtual reality chemistry laboratory. User expertise, quantitatively measured in terms of errors and time spent, is used as an adaptation criterion to dynamically update adaptive aids (arrows, text or animations). The system modifies the contents of such aids for good, average, and weak students to give them the opportunity to improve their performance. In this manner, adaptive aids assist the users to perform an experiment in the proposed system correctly according to the correct procedure with high performance. For evaluation, we conducted a comprehensive user study with 59 participants from various institutions. These students were selected randomly by their teachers and contained different levels of students (i.e., weak, average, and good students). We also conducted a written quiz and based on their scores we divided them into three groups (i.e., good, average, and weak). The participants (students) were classified into three groups G1, G2, and G3 based on their expertise levels (i.e., good, average and low). Then each group conducted an experiment four times. Evaluations revealed that the proposed system consistently improved students' performance in four trials. In particular, weak students' performance greatly improved as compared to that of good and average students in terms of a variety of factors, including time and errors during the performance of experiments.