The digital twins (DTs) paradigm creates a virtual replica of a physical artifact that represents its real-world status in a virtual space. When implemented in the Internet of Robotic Things (IoRT), it develops a virtual model of the physical robot that receives real-time sensor data, acting like an actual robot. The DTs-based IoRT allows remote patient monitoring (RPM) that improves treatment plans and reduces the burden of gathering vital signs on health carers by automating repeated tasks. However, RPM procedures generate large amounts of sensor data, which are difficult to examine. Furthermore, health carers cannot forecast abnormalities based on health data. Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. This research leverages the advantages of ML and DTs-based IoRT to remotely monitor chronic or infectious patients and predict their future status. The proposed system is an extension of our previously published work. It uses the virtual twin (VT) to navigate the physical twin (PT) for collecting data from the patient-mounted sensors and applies ML techniques to predict health anomalies. It evaluated six ML algorithms to determine the most accurate model. The models were trained and tested by using a dataset combining two public datasets and real-world data collected by healthcare providers. Results confirmed that the K Nearest-Neighbor (KNN) has the best accuracy i.e. 97%. The system was also tested in the clinical setting to collect patient data and the best-performing algorithm (KNN) was used for status prediction, obtaining 98% accuracy.

Virtual reality driving simulators have been increasingly used for training purposes, but they are still lacking effective driver assistance features, and poor use of user interface (UI) and guidance systems leads to users’ performance being affected. In this paper, we investigate image–arrow aids in a virtual reality driving simulator (VRDS) that enables trainees (new drivers) to interpret instructions according to the correct course of action while performing their driving task. Image–arrow aids consist of arrows, texts, and images that are separately rendered during driving in the VRDS. A total of 45 participants were divided into three groups: G1 (image–arrow aids), G2 (audio and textual aids), and G3 (arrows and textual aids). The results showed that G1 (image–arrow guidance) achieved the best performance, with a mean error rate of 8.1 (SD = 1.23) and a mean completion time of 3.26 min (SD = 0.56). In comparison, G2 (audio and textual aids) had a mean error rate of 10.8 (SD = 1.31) and completion time of 4.49 min (SD = 0.67), while G3 (arrows and textual aids) had the highest error rate (18.4, SD = 1.43) and longest completion time (6.51 min, SD = 0.68). An evaluation revealed that the performance of G1 is significantly better than that of G2 and G3 in terms of performance measures (errors + time) and subjective analysis such as usability, easiness, understanding, and assistance.

The Periodic Table (PT) serves as the fundamental basis of chemistry, often commencing classroom discussions led by chemistry teachers. Mastery of its structure empowers students to anticipate variations in the chemical and physical traits of the elements. This study presents a Hierarchical-Based Interactive Virtual Periodic Table (HBIVPT) aimed at enhancing comprehension and learning outcomes. The HBIVPT offers a hierarchical interface, dynamically illustrating atomic structures, with electrons orbiting the nucleus in designated orbitals. Utilizing multimodal feedback (both visual and auditory), users can improve their knowledge related to different elements of the PT. To evaluate the effectiveness of the HBIVPT, pre- and post-tests were conducted to measure learning improvements. During the evaluation process, forty-five (45) students participated and they were divided into three groups (G1, G2, and G3). G1 used the textbook for chemical elements, G2 used the proposed HBIVPT, and G3 used the Royal Society of Chemistry's Interactive VPT. The students in G2 who used HBIVPT were significantly better as compared to G1 and G3. This is evidenced by the observed average increase in learning: 15% for students using textbooks (G1), 20% for those studying online Periodic Tables (G3), and, notably, 37% for those utilizing HBIVPT (G2). The study results demonstrate that the use of the proposed HBIVPT system significantly improved students' learning and comprehension of the Periodic Table, resulting in enhanced academic performance compared to students using other methods.

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.

Different Virtual Chemistry Laboratories (VCLs) have been developed where users can perform their experimental work to increase their learning for hands-on chemistry experiments. The existing VCLs are playing a vital role in the enhancement of users’ performance, however, mental/cognitive load on users is a key issue in the existing VCLs. The issue of mental/cognitive load arises due to the complexity of the environment by displaying a number of chemicals, glass wares, and other lab equipments in the VCLs. In this paper, we present a new concept of performing experiments in a Purpose Built Virtual Chemistry Laboratory (PBVCL) with interactive tutorial information (ITI) to minimize mental/cognitive load on users and enhance their performance and theoretical knowledge about the experiment. PBVCL displays only those chemicals and glass wares which are necessary in the current experiment while hiding other unnecessary equipments. During evaluations, fourty-eight (48) students perform the experiments in two different groups using PBVCL and general VCL. Evaluations reveal that the proposed PBVCL provides less mental load and users can easily enhance their performance.

Research shows that cognitive aids such as audio,video and visual etc can help reduce mental load on students when working in 3D virtual envirments but at the same time it may hinder students' active exploration . Active exploration is necessary when the student perform the same experiment in physical world. The strategy of adaptive repetition is used as a control strategy to actively explore 3D virtual learning environments. However,this adaptation occurs automatically without allowing students to choose when to use the aid and what type aid to use. In this study, a controlled adaptive repetition strategy is proposed where the system facilitates students to repeat a task with a desired type of aid while working in 3D virtual learning environments. Experimental results shows that the proposed strategy is effective which enhances active exploration and students perform better in physical world.

Adaptive Virtual Learning Environments (VLEs) present customized teaching materials to individual students which help them to achieve their learning goals and serve quite a vital role in virtual learning environments. In this paper, we present a new student-centered learning approach in a three-dimensional (3D) virtual biology laboratory (VBIOLAB). The approach is based on the concept of horizontal transition with student preference (HTWSP) implemented with the help of VBIOLAB. The HTWSP is based on the concept of allowing students to choose their preferred learning styles according to their needs and pace instead of automatically adapted aids. HTWSP allows students to stay in a certain module and attain more information about that learning module through various aids of their choice. To go to the next learning module there is a mechanism of vertical transition which allows a student to make quick progress by skipping the details about a certain module. Intermediate-level students participated in experiments that compared the proposed system with an adaptive virtual laboratory. Experimental results indicated that 75% of students improved their examination scores through the use of VBIOLAB. Data from the system usability scale (SUS) and the subjective rating supported greater participation, motivation, and effectiveness in learning using VBIOLAB. The experimental results reveal that this approach is effective and vital to utilize to enhance students’ learning in 3D-VLEs.

Serious games play a pivotal role in engaging users in activities which are considered less-engaging but healthy. Collaborative Virtual Environments (CVEs), one of the enablers of serious games are used in various fields including education, healthcare, tele-conferencing and assembly tasks. There are different factors that affect user performance in CVE. These factors include network latency, loose coordination, lack of task distributions strategy and lack of awareness. As compared to other factors,network latency has unfortunately got less attention of the researcher and therefore, it sometimes becomes a bottleneck for the user performance in CVE. In this paper, we analyzed the effect of network latency on user performance in CVE with textual, audio, 3D Map-Liner (3DML) and arrows-casting aids on different participants. Based on task distribution model, a simulated environment for collaborative assembly task was developed to conduct the experiments. The experiments were performed on users with textual, audio, 3DML and arrows-casting aids with five level of latency (i.e. 0, 50, 100, 150 and 200 ms) to evaluate 19 groups(two participants per group) to check their performance in terms of network latency. Overall, results showed that the user’s performance was better in arrows-casting based navigation as compared to other navigation aids (i.e textual, audio and 3DML) using five level of latency (i.e 0, 50, 100, 150 and 200 ms). Whenever the value of latency was increased the task completion time increased significantly with slight decrease in errors rate.

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital-twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive Internet of Things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the Internet of Robotic Things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica [virtual twin (VT)] that receives real-time data from the physical RT [physical twin (PT)] to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a realtime system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The VT operates the PT in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, and timeliness) and high accuracy of PT's navigation.

Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The proposed system consists of training and testing phases. The proposed training phase consists of gait features extraction using the Microsoft Kinect (MS Kinect) controller, dataset generation based on joints' position, pre-processing of gait features, feature selection by calculating the Standard error and Standard deviation of the arithmetic mean and best model selection using R2 and adjusted R2 techniques. T-test and ANOVA techniques show that nine joints (right shoulder, right elbow, right hand, left knee, right knee, right ankle, left ankle, left, and right foot) are statistically significant at a 5% level of significance for age estimation. The proposed testing phase correctly predicts the age of a walking person using the results obtained from the training phase. The proposed approach is evaluated on the data that is experimentally recorded from the user in a real-time scenario. Fifty (50) volunteers of different ages participated in the experimental study. Using the limited features, the proposed method estimates the age with 98.0% accuracy on experimental images acquired in real-time via a classical general linear regression model.

Gender classification based on gait is a challenging problem because humans may walk in different directions at different speeds and with varying gait patterns. The majority of investigations in the literature relied on gender-specific joints, whereas the comparison of the lower-body joints in the literature received little attention. When considering the lower-body joints, it is important to identify the gender of a person based on his or her walking style using the Kinect Sensor. In this paper, a logistic-regression-based model for gender classification using lower-body joints is proposed. The proposed approach is divided into several parts, including feature extraction, gait feature selection, and human gender classification. Different joints' (3-dimensional) features were extracted using the Kinect Sensor. To select a significant joint, a variety of statistical techniques were used, including Cronbach's alpha, correlation, T-test, and ANOVA techniques. The average result from the Coronbach's alpha approach was 99.74%, which shows the reliability of the lower-body joints in gender classification. Similarly, the correlation data show a significant difference between the joints of males and females during gait. As the p-value for each of the lower-body joints is zero and less than 1%, the T-test and ANOVA techniques demonstrated that all nine joints are statistically significant for gender classification. Finally, the binary logistic regression model was implemented to classify the gender based on the selected features. The experiments in a real situation involved one hundred and twenty (120) individuals. The suggested method correctly classified gender using 3D data captured from lower-body joints in real-time using the Kinect Sensor with 98.3% accuracy. The proposed method outperformed the existing image-based gender classification systems.

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.

Different cognitive aids (such as arrows, audio, etc.) are used in virtual laboratories for the enhancement of students' performance during their experimental tasks. However, excessive use of aids in virtual laboratories can lead to increased cognitive load, which affects task performance. In this article, we first conducted a subjective study with field experts to investigate about the practical implementation of our existing virtual chemistry laboratory. To consider the suggestions of the field experts, we propose task specific aids based virtual reality chemistry laboratory (TSA-VRCL) to minimize students' cognitive load and enhance their performance. The task specific aids consist of an arrow, animation, and audio aids that are separately rendered with each step of the experimental tasks. During evaluations, eighty students performed the experiments in four different groups using four different experimental conditions. Evaluations revealed that the proposed TSA-VRCL minimizes students' cognitive load and enhances their performance.

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.

Different navigation aids (text, audio, maps, arrows, etc.) are used in complex virtual environments (VEs) to assist users in task operation and performance enhancement. Most current studies use cognitive cues to assist users during navigation and path selection in VEs. However, novices need to know which gesture to execute to carry out specific navigation. In this paper, a new concept of navigation aid is proposed that uses visual aids with gestural interaction during navigation tasks. The proposed aids provide two-fold guidance in the VE: they assist users in selecting the correct path and posing the correct gestures. In addition, it proposes fingertip pointing-based gestures for realistic navigation inside the VE to achieve high performance and usability using simple and lightweight gestures. Furthermore, the proposed aids (gesture guides) are compared with existing aids such as audio, textual, arrow-casting + textual, and 3D map + textual aids in terms of performance and usability. A VE is designed and implemented using OpenGL for experimental purposes. The Leap Motion controller is used for hand gesture recognition and interaction with VE. The System Usability Scale (SUS) is used for assessing system usability. Experimental results show comparatively improved performance and high usability for the proposed aids as compared to audio, textual, arrow-casting + textual, and 3D map + textual aids.

Virtual Chemistry Laboratories (VCLs) are used as an alternative to the physical laboratories, where users can enhance their performance for hands-on chemistry experiments. However, cognitive load and other issues make the VCLs impractical. The issue of cognitive load arises due to the complexity of the environment by displaying a number of chemicals, glass wares, and other lab equipments in the VCL. In this paper, we first investigate the field experts about the practical use of the VCLs and then propose a Purpose-built Virtual Chemistry Laboratory (PbVCL) with arrow textual aids to minimize the cognitive load and improve the learning efficiency. PbVCL displays only specific chemicals and glass wares, used in the current experiment while hiding other equipments. Students simulate their chemistry experiments with the help of arrow-textual aids (i.e., textual and an arrow guidance). Users complete the experimental tasks in the PbVCL following the arrow-textual aids. During evaluations, seventy-six (76) students perform the experiments in four different groups using four different experimental conditions. Evaluations revealed that the proposed PbVCL with arrow-textual aids improve students' performance on the basis of various aspects such as time and errors while conducting experiment.

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.

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.

Gender classification plays an important role in many applications such as security and medical applications. Human gender can be classified using different biometric techniques such as face recognition, voice recognition, activity recognition and gait recognition. Different approaches based on gait-recognition have been proposed for the identification of gender. However, performance and accuracy of such systems suffer from the recurring and inherent issues like occlusion of body parts, computational costs and false recognition of 3D joints. The problems can be subdued with deep feature-based analysis and extensive calculation but that may further degrade performance of the system. In this paper, we propose a limited feature-based, Three Dimensional (3D), real time, and multi-view gait-based automatic gender classification system using Microsoft kinect (MS Kinect). A statistical model is molded from the binary logistic regression of the gait data extracted at run time using the sensor. The proposed method is successfully implemented and evaluated by 80 (50 male and 30 female) users. The achieved accuracy rate (97.50%) proves applicability of the model.

Simple and natural interaction has a vital role in any realistic virtual environment (VE). This research proposes a set of lightweight gesture-based techniques for interaction in VEs with a focus on high accuracy, performance, and usability. The proposed techniques use a single fingertip pose and state for object/task selection, translation, navigation, rotation, and scaling. Four different techniques are proposed for interaction, i.e., MSGE (Menu-based task selection and gesture-based task execution), GSGE (Gesture-based task selection and gesture-based task execution), SGTE (Single gesture for task selection and execution), and TSGE (Time slice-based task selection and gesture-based task execution). Keeping in mind the concept of re-usability, the index-tip spatial position is used for task operation in all techniques. For experimental evaluation of the proposed techniques, a VE is designed in Unity3D, while interaction is carried out using the Leap Motion controller. The experimental study was conducted with forty (40) volunteer participants and two experts (authors). Experimental results show improved accuracy for TSGE (participants 97.22%, and experts 97.22%) as compared to others (participants: SGTE 95.55%, GSGE 94.44%, and MSGE 92.75%, experts: SGTE 94.44%, GSGE 94.44%, and MSGE 91.67%). Similarly, the results show high task performance for TSGE (participants 112.9 seconds, SD 5.3, experts 101.75 seconds, SD 3.3) as compared to others (participants: SGTE 117.2 seconds, SD 5.7, GSGE 121.8 seconds, SD 8.0, and MSGE 126.7 seconds, SD 12.9 and experts: SGTE 107.0, SD 5.7, GSGE 113.25, SD 3.5, and MSGE 122.0 seconds, SD 3.6). In addition, usability analysis shows high usability for the proposed interaction techniques, i.e., TSGE (SUS score 98.5), SGTE (SUS score 95.75), GSGE (SUS score 95.25), MSGE (SUS score 94.75). Furthermore, a comparative study with state-of-the-art interaction techniques showed a high accuracy rate, multiple tasks, and reusability support, use of easy to learn and use and fewer features-based gestures (fingertip gestures), and multiple interaction techniques (four techniques) support for the proposed techniques.