Parallel session 2C

15:45 - 16:30 Tuesday, 23rd August, 2022

SULLY 1 - Level 1

Presentation type Oral

Janos Logo, Michal Sejnoha

Internet of Things & Engineering education


15:45 - 16:00

O2C.01 Immersive mixed reality experience empowered by the internet of things and geospatial technologies

Theofilos Papadopoulos1, Konstantinos Evangelidis2, Georgios Evangelidis1, Theodore Kaskalis1
1University of Macedonia Department of Applied Informatics, Greece. 2International Hellenic University - Serres Campus, Greece

Short Paper Summary

As Mixed Reality applications are penetrating people’s daily activities, strong synergies with Geospatial technologies and the Internet of Things are revealed. The first is due to the significance of the spatial reference of all involved actors of a Mixed Reality environment: the end-user, the real and the virtual objects. The second is due to the ever-increasing participation of sensors controlling devices and machines always and everywhere. This paper attempts to highlight these synergies and propose a case proving that they crucially empower a Mixed Reality experience.

Keywords

Geospatial Technologies
Mixed Reality
Internet of Things
Human-Computer Interactions
Physical Task Execution

Introduction

Mixed Reality applications have now penetrated people's daily lives, contributing, among many other things, to navigation decisions, such as Google Maps [1], entertainment, [2, 3] education and training [4]. Ubiquitous networking and interoperable browsers have brought such applications from a niche technology to the mainstream of modern life. Major components of a Mixed Reality environment are the observer-user participating in an immersive experience, the physical environment and the virtual one [5]. In an ideal case, it is not irrational to state that virtual objects “are aware” of the physical ones in a Mixed Reality environment. In other words, virtual objects interact with real ones [6]. By scanning and creating a three-dimensional digital equivalent of the physical world in real-time, e.g. through LIDAR-based techniques [7], one can transfer the physical properties to the virtual world [8], thus making them accessible by virtual objects. Alternatively, a digital twin of the real world may be deployed through capturing with high-resolution cameras and applying photogrammetric techniques [9]. These last-mentioned techniques are mainly used today for real-world capturing purposes and highlight a strong connection of Mixed Reality with Geospatial Technologies [10]. 

An effective way to empower a Mixed Reality experience without specialised equipment is to exploit the Internet of Things (IoT) through its associated technologies. IoT has contributed to the convenience and comfort of people's daily lives, providing many benefits, including interoperability and ease of control over the Internet. Every day, more professional or household environments upgrade their materials to smart counterparts (refrigerators, lights, TVs) [11]. The benefits of exploiting this technology are not limited to performing everyday tasks or controlling smart home appliances. For example, this technology has produced exceptional outcomes in infrastructure control [12] and data monitoring [13, 14].  At the same time, rapid technological developments are happening in hardware equipment of smart devices and Web Technologies. These include programming languages such as JavaScript and Web APIs that allow browsers to access smart device components such as graphics cards and sensors (GPS, gyroscope, camera) [15]. 

Utilising the technologies mentioned above, it is possible to allow virtual objects to perform actions on real ones. In this paper we demonstrate such an interaction by implementing a scenario through which an end user is ordering via a smart device to a virtual person to turn on/off the lights of a natural space where smart lighting has been installed.

Methods

2.1 Mapping the environment with Geospatial Technologies

 

Object recognition [16] and RGB depth mapping [17] are the prevailing techniques currently utilised to capture physical areas and objects of the natural world and support contextual awareness in Mixed Reality environments. Although they dominate in terms of direct and real-time capturing, they are inferior in object resolution issues and their capabilities for large areas capturing are poor. Traditionally, TDS (Total Data Stations) or GNSS (Global navigation satellite system) receivers were used to develop 3D models of the real world. Later, during the last decades, LiDAR or Laser Scanning became the ultimate mapping tool for 3D mapping purposes [18]. Today, with the evolution met in DSLR (Digital single-lens reflex) cameras and UAVs (Unmanned Aerial Vehicles), Photogrammetry has become the ultimate mapping technique. It produces 3D models reconstructed from images, and in contrast with LiDAR, it is applicable wherever reflectance exists (Figure 1) [19]

Uncaptioned visualUncaptioned visual

Figure 1: Capturing an area with high-resolution cameras and developing the digital surface model by employing photogrammetric techniques

Having already captured an area and developed a digital surface model, potential imported virtual objects in a Mixed Reality environment possess an apparent spatial reference. Consequently, they may develop elaborated movements over this area to enhance the immersive experience of the end-user.

Connecting smart devices utilising the IoT

The Internet of Things (IoT) connecting smart home appliances such as bells, lights, locks, security cameras and thermostats has made it easy for everyone to install and use these gadgets at home. However, you still need some sort of mechanism to control them [20]. These mechanisms differ and can be classified by energy efficiency, range coverage, security and scalability. The values of these features are mainly defined by the communication protocol of the smart devices. The protocols can initially be categorised into wired [21] and wireless [22][23], and some of them are Power Line Communication (PLC), Z-Wave, and Zigbee. In addition to the above protocols, communication and management of smart devices can be achieved through browsers, taking advantage of specific Web APIs such as the Fetch API[24][25], Bluetooth API[26][27] or the experimental USB API[28][29]. Furthermore, some manufacturers offer specific APIs to communicate with their products. For example, the WiZ and Phillips companies offer the WiZ Pro API [30] and the Phillips Hue API [31] to control their devices via HTTP Requests. In addition to this, researchers have proposed frameworks for specific APIs to ensure interoperability among devices [32].


Results

The application proposal examines the scenario in which a virtual object in a Mixed Reality environment visualised through a smartphone using a modern browser interacts with a smart device (physical object). The smart device is the Phillips Hue smart light bulb. The communication with the lamp is made over the Phillips Hue Bridge 2.0 smart hub that uses the ZigBee wireless protocol. This smart hub will allow us to programmatically control the state of the bulb using HTTP Requests with JavaScript based on Phillips’s well-documented Lights API [33].

For this to happen, the first step is to create a virtual geospatial world (Figure 2) [34]. Geospatial worlds are virtual scenes that allow virtual object placement and animations of moving ones to occur, with their position referencing the actual coordinates on the globe.

 

Uncaptioned visual

Figure 2: Representation of a virtual geospatial world of a small city block

 

As depicted in Figure 3 (a) is the creation of the digital twin of the area (virtual geospatial world), in our case, an office area. This area can be used as a mask object to enable real to virtual occlusion [6], thus creating a Mixed Reality environment by allowing spatial and entity awareness among realities. The second step (b) involves the user observing the MR Environment from a smartphone.

 

Uncaptioned visual

Figure 3: Representation of the discrete nodes that make virtual to real interaction possible: a) the digital twin, b) the visualisation media, c) the area in which when the virtual man enters, an HTTP Request is triggered from the visualisation media to the Smart Hub, d) the Smart Hub and e) the Smart Light bulb.

 

As the smartphone renders the whole scene, it is responsible for communicating with the smart hub to trigger the “light on” event. We have defined the area of the switch as shown in Figure 3 (c), and when the virtual man enters that area, the smartphone will inform the smart hub to turn on the light. To detect when the virtual man enters the anchor area, we used the bounding box of the area and the virtual man’s position. When the man enters the area, the application running on the user’s smartphone (b) sends an HTTP Request (Figure 4) to the smart hub (d) to turn on the lights (e).

 

Uncaptioned visual

Figure 4: An example of the HTTP Request sent to alter the state of the light.


Conclusions & Contributions

In previous work [35], we proposed a modality-based interaction taxonomy that makes it easier for researchers to detect gaps in human-computer interactions in immersive environments. Through this taxonomy, we can locate the contribution of the current research work in the Sensor-Based Modality with IoT-Based context and define the Physical Task Execution method.

 

Uncaptioned visual

Figure 5: Defining the modality, context and naming the method of interaction proposed in this research work

 

An alternative taxonomy that can induce lateral thinking is categorising interactions among realities based on the environment they participate in (virtual or real). Based on this concept, we singled out four basic ways of interacting with environments:

 

  • real to real: Interaction of a physical object with another physical object.
  • real to virtual: Interaction of a physical object with a virtual one. This can be achieved by creating a digital twin and presenting the illusion of the real interacting with the virtual (e.g., through partial or total occlusion).
  • virtual to virtual: Interaction of a virtual object with another virtual object.
  • virtual to real: Interaction of a virtual object with a physical object (by creating a digital twin and utilising the IoT and geospatial technologies)

 

Based on the latter classification, this research work introduced a virtual to real interaction by exploiting the Internet of Things.

The synergy of the Internet of Things, Geospatial technologies and Mixed Reality environments can support innovative applications that can contribute, among others, to cultural heritage promotion or the entertainment industry through creative interactions, providing enhanced immersion experiences. We demonstrated a simple example utilising these technologies. A more complex and impressive one could involve the synchronous movement of smart floor panels simulating ground waves to present an earthquake in a Mixed Reality scene. Freeing virtual objects from their virtual bonds by giving them the ability to perform tasks in conventional reality can also positively affect the user’s engagement, as things are no longer only taking place in a display but become somewhat real.

What is finally demonstrated is the capability of receiving real-time information on unlimited points of interest in real-world objects through input devices and sensors and triggering events by virtual objects based on the end-user position in the field of a Mixed Reality environment. All this evidences a scientific and substantial social impact through numerous potential applications to promote tourism and cultural resources and promote areas of high commercial or historic interest.


References

1. https://www.cnbc.com/2021/03/30/google-maps-launches-augmented-reality-directions-for-indoor-spaces.html

2. Stapleton, C., Hughes, C., Moshell, M., Micikevicius, P., & Altman, M. (2002). Applying mixed reality to entertainment. Computer, 35(12), 122-124.

3. Prattico, F. G., & Lamberti, F. (2020). Mixed-reality robotic games: design guidelines for effective entertainment with consumer robots. IEEE Consumer Electronics Magazine, 10(1), 6-16.

4. Hughes, C. E., Stapleton, C. B., Hughes, D. E., & Smith, E. M. (2005). Mixed reality in education, entertainment, and training. IEEE computer graphics and applications, 25(6), 24-30.

5. Rokhsaritalemi, S., Sadeghi-Niaraki, A., & Choi, S. M. (2020). A review on mixed reality: Current trends, challenges and prospects. Applied Sciences, 10(2), 636.

6. Evangelidis, K., Papadopoulos, T., & Sylaiou, S. (2021). Mixed Reality: A Reconsideration Based on Mixed Objects and Geospatial Modalities. Applied Sciences, 11(5), 2417.

7. Manghat, S. K., & El-Sharkawy, M. (2020, January). A multi sensor real-time tracking with LiDAR and camera. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0668-0672). IEEE.

8. Coutrix, C., & Nigay, L. (2006, May). Mixed reality: a model of mixed interaction. In Proceedings of the working conference on Advanced visual interfaces (pp. 43-50).

9. Nex, F. (2011). UAV photogrammetry for mapping and 3d modeling–current status and future perspectives. International archives of the photogrammetry, remote sensing and spatial information sciences, 38(1/C22).

10. Mahmood, T., Fulmer, W., Mungoli, N., Huang, J., & Lu, A. (2019, October). Improving information sharing and collaborative analysis for remote geospatial visualization using mixed reality. In 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 236-247). IEEE.

11. Kumar, P., & Pati, U. C. (2016, May). IoT based monitoring and control of appliances for smart home. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 1145-1150). IEEE.

12. Phupattanasilp, P., & Tong, S. R. (2019). Augmented reality in the integrative internet of things (AR-IoT): application for precision farming. Sustainability, 11(9), 2658.

13. Atsali, G., Panagiotakis, S., Markakis, E., Mastorakis, G., Mavromoustakis, C. X., Pallis, E., & Malamos, A. (2018). A mixed reality 3D system for the integration of X3DoM graphics with real-time IoT data. Multimedia Tools and Applications, 77(4), 4731-4752.

14. Natephra, W., & Motamedi, A. (2019, May). Live data visualization of IoT sensors using augmented reality (AR) and BIM. In 36th International Symposium on Automation and Robotics in Construction (ISARC 2019).

15. Jackson, T., Angermann, F., & Meier, P. (2011). Survey of use cases for mobile augmented reality browsers. In Handbook of augmented reality (pp. 409-431). Springer, New York, NY.

16. Dasgupta, A., Manuel, M., Mansur, R. S., Nowak, N., & Gračanin, D. (2020, March). Towards real time object recognition for context awareness in mixed reality: a machine learning approach. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 262-268). IEEE.

17. Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2014). RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In Experimental robotics (pp. 477-491). Springer, Berlin, Heidelberg.

18. Schwarz, B. (2010). Mapping the world in 3D. Nature Photonics, 4(7), 429-430.

19. Barsanti, S. G., Remondino, F., & Visintini, D. (2012, June). Photogrammetry and Laser Scanning for archaeological site 3D modeling–Some critical issues. In Proc. of the 2nd Workshop on'The New Technologies for Aquileia', V. Roberto, L. Fozzati.

20. Triantafyllou, A., Sarigiannidis, P., & Lagkas, T. D. (2018). Network protocols, schemes, and mechanisms for internet of things (iot): Features, open challenges, and trends. Wireless communications and mobile computing, 2018.

21. Sikder, A. K., Acar, A., Aksu, H., Uluagac, A. S., Akkaya, K., & Conti, M. (2018, January). IoT-enabled smart lighting systems for smart cities. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 639-645). IEEE.

22. Mainetti, L., Patrono, L., & Vilei, A. (2011, September). Evolution of wireless sensor networks towards the internet of things: A survey. In SoftCOM 2011, 19th international conference on software, telecommunications and computer networks (pp. 1-6). IEEE.

23. Al-Sarawi, S., Anbar, M., Alieyan, K., & Alzubaidi, M. (2017, May). Internet of Things (IoT) communication protocols. In 2017 8th International conference on information technology (ICIT) (pp. 685-690). IEEE.

24. https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API

25. Jeng, S. L., & Chieng, W. H. (2020, August). Web-based HMI of industrial controllers for general purpose. In 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII) (pp. 212-215). IEEE.

26. https://developer.mozilla.org/en-US/docs/Web/API/Web_Bluetooth_API

27. Carpentier, E., Thomasset, C., & Briffaut, J. (2019, November). Bridging the gap: Data exfiltration in highly secured environments using bluetooth iots. In 2019 IEEE 37th International Conference on Computer Design (ICCD) (pp. 297-300). IEEE.

28. https://developer.mozilla.org/en-US/docs/Web/API/USB

29. Ahn, S., Oh, H., & Choi, J. K. (2017, October). Hub-based Personal Web-Enabled Cross-Device Application. In 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE) (pp. 1-3). IEEE.

30. https://docs.pro.wizconnected.com/#introduction

31. https://developers.meethue.com/develop/hue-api/

32. Asano, S., Yashiro, T., & Sakamura, K. (2016, October). A proxy framework for API interoperability in the internet of things. In 2016 IEEE 5th Global Conference on Consumer Electronics (pp. 1-2). IEEE.

33. https://developers.meethue.com/develop/hue-api/lights-api/

34. Evangelidis, K., Papadopoulos, T., Papatheodorou, K., Mastorokostas, P., & Hilas, C. (2018). 3D geospatial visualizations: Animation and motion effects on spatial objects. Computers & geosciences, 111, 200-212.

35. Papadopoulos, T., Evangelidis, K., Kaskalis, T. H., Evangelidis, G., & Sylaiou, S. (2021). Interactions in Augmented and Mixed Reality: An Overview. Applied Sciences, 11(18), 8752.


16:00 - 16:15

O2C.02 School resumption during the post-COVID-19 era: analysis of students perspective

Reshmy Krishnan1, Ali Al Badi2, Shermina Jeba1, Menila James1, Joseph Anajemba3,4
1MUSCAT COLLEGE, Oman. 2GULF COLLEGE, Oman. 3UNIVERSITY OF BRISTOL, UK. 4INSTITUTE OF APPLIED TECHNOLOGY, ABU DHABI POLYTECHNICE, United Arab Emirates

Short Paper Summary

COVID-19 has its impact heavily marked in education and learning process. Students of different education levels, had significant impact on their mental health more than physical health. The world gears up back to Old Normal and every business resume to its old-style functioning, similarly the educational institutions prepare to offer courses in offline mode. However, the students who witnessed the major shift from offline to online mode of learning process when COVID-19 started are still coping with the impact of COVID-19. This requires a clear understanding of the student’s mindset towards back to school. COVID-19 is criticised for its negative impact, however there is other side too. The objective of this study is to map out the positive and negative implications that COVID-19 caused with respect to mental health of the students and finding out their inclination towards Offline or Online learning process and the reasons behind it. The study is conducted through a survey on students who are in different years of study at under-graduate level by multi-stage cluster sampling and stratified sampling. The collected data is sorted and analysed to get the required inference from which suggestion can be drawn. The outcome of this review can help the educational institutions in developing and implementing policies to accommodate the mental health issues while the students get migrated from Online to Offline

Keywords

Online
Offline
Education
COVID-19

Introduction

During the COVID 19 era, many waves recurred. The world witnessed several loss of human lives, economy meltdown, food security, health hazards, and so on. Children and young adults between ages 6 to 21 could not grasp the transition in their education patterns and the mostly affected ones were those from 18 to 21 age group [1]. This group is referred as young adults. They started their schooling with traditional methodologies, evaluated based on the universally accepted physical mode of examination and graded based on their performance during their examinations. The teachers were able to guide, monitor, motivate, discuss, discipline and instruct the students for their progressive learning process [2]. The responsibility to make a student get education is mostly determined by the teacher's role and the school environment. The parents played a less important role and the students themselves were dependent on the teachers [3]. 

The physiological and psychological patterns of this group were severely damaged during COVID as the students started to stay at home [4]. The responsibility from teachers has been shifted to students majorly and their parents took the role to monitor, discipline and instruct their children [5]. 

 The above fear factors are believed to have continued to impact on students even in Post-COVID era. However, it is important to know the positive implications of COVID-19 among the students. Though major studies concentrated on negative implications, there are certain positive strength too. Identified from social media and personal conversations two major positive effects were found and established as – emotional support from family [6] and absence of unwanted academic peer pressure [7]. 

While some student are eager to embrace the conventional in-classroom method of studies as schools globally are gradually gearing to go back to the physical mode of teaching, certain groups of students are reluctant to follow this conventional mode [8]. Therefore, the survey undertaken in this work is aimed to pull out the perspective of students with respect to their preferred learning modes. This would help both the parents and teachers to mitigate the negativities surrounding the different modes of education and to capitalize on the positive factors while the students shift back to school.


Methods

This study is focused on understanding the deliverability of the two modes of education (Online and Offline) in terms of the following parameters;

  1. acceptability 
  2. purpose
  3. suitability
  4. coverage
  5. effectiveness and 
  6. metamorphosis

In order to realize these objectives, the study employed quantitative approach in gathering the primary data required for the research. According to [9] “qualitative researches are mainly interested in the following establishing how perceptions are originated, how connotations are conveyed, how roles are established, how a prospectus works out, how a policy is framed and executed and how a pupil turn out to be aberrant”.  A total of 912 respondents, most of whom were students of the same college having undergone both modes of education, pre and post COVID methods for substantial periods of time and the rest were selected for the survey. Others were picked online from relevant sources. The sampling was done based on the principles of multi-stage and stratified sampling [10 – 11]. This helped in achieving the objective of this study with limited samples. 

The survey proceeded with 30 odd questions to compare the merits and demerits of both online and offline modes of education which are perceived as; 

  1. The preferred mode of education for students
    1. Offline
    2. Online
  2. The merits with education online:
    1. elimination of commutation
    2. total operational comfort
    3. significant reduction in cost
  3. The discouraging aspects with education online:
    1. Discreteness on consumption
    2. Diversion during reception
    3. Disconnect in operation
  4. The merits with education offline:
    1. Correlation on transaction
    2. Continuity over operation
    3. Concentration on comparative scale on betterment
  5. The three discouraging things with education offline
  6. Non-negotiable attention
  7. Fear of contamination
  8. Direct supervision

 Inference were generated from the 912 respondents. A cluster was drawn to find relevance, rationale and regression on data. A cluster of 52 data responses were rejected. However, 360 responses were considered on fulfilment on purpose of which 263 of them had expressions favouring online education while 597 respondents provided logic and projected the traditional offline path. The responses were analysed using possible data analytic tools and the following results were inferred.

Results

3.1       Favouring Offline mode of learning:

Figures 1 below represents a bar chart analysis of the preferential criteria that sets offline way ahead of the modern online method for reasons clearly inferred out of the responses from 912 respondents. The result in Figure 1 indicates that although some students still wish they should continue their online studies from home, greater percentage of all the respondents from the 10 different categories still prefer to resume back their offline (in-classroom) mode of studies. 

Uncaptioned visual

             Figure 1: Result analysis of student perception for online education

 

Furthermore, the bar chart in Figures 2 illustrates how students are inclined to offline education mode based on their individual opinions. Inferences were made on the construct of their logic by which they listed out the advantages of education offline in comparison. Feedbacks from the students indicates that majority of them prefer offline education due to health challenges associated with the online mode of studies. Majority of these respondents agree that online studies results in straining of major sense organs of the body such as, eyes, ears, and other sensory parts. Also, more of the respondents believe that offline mode of studies offers them more time to chat and associate with their peers. Moving on, greater percentage of them also responded on the affirmative indicating that offline studies provides them with more opportunities to scrutinize educational materials and tools.  

Uncaptioned visual

                    Figure 2: Respondents’ views that favours offline education

   

3.2       Favouring online mode of Learning

 The chart in Figure 3 indicates the result of the five most significant constructs from the respondent’s viewpoints which are in favour of online education.

Uncaptioned visual

              Figure 3: Response Indicators - Favouring Online Education

Conclusions & Contributions

In conclusion, this study aims to research the impact that the COVID-19 pandemic has on the mental health of young pupils and their perspective on returning back to school. This paper has highlighted the different contributing factors that impact student’s mental health after these two years of living with the virus and studying from their respective homes. The responses received and the preceded analysis gave mixed views from the students with respect to offline and online mode of education. However, it has clearly established that majority of the respondents which represents the views of most students globally are excited to return back to their in-classroom activities as they have faulted the online learning strategy in so many ways. The results from this study would help governments and schools develop policies and solutions to address better and manage the mental health of these youths going through a tough time in their developmental years

Findings

  1. offline over online education as the latter is monotonic and has a negative impact on health;
  2. to be in the company of friends, share ideas and learn together rather than being remotely connected by way of chatting;
  3. to go out of the comfort of their homes, a kind of closed atmosphere that creates chances to increase the factors of stress and boredom which rather is taken care of through socialization with other students;
  4. to move out of the emotional effects of being confined to home due to covid as the nostalgic ill-effects haunt the mind frequently;
  5. to attend college than being with the 12/7 schedule, home on-line;
  6. to be off to college to avoid the continuous, constant parental supervisions;
  7. to receive practical training over and above the theoretical stuff;
  8. to have physical interaction with teachers, students to align with the learning process on better terms;
  9. to listen, feel and learn in person with an open and relaxed mind rather than taxing the ears and eyes more in an imbalanced manner
  10. to participate in extracurricular activities to express their dormant and unexpressed skills.

 

Acknowledgements

 

This publication is part of the Research project funded by Ministry of Higher Education, Research and Innovation (MoHERI) with reference code: MoHERI/BFP/MC/01/2020

References

[1]    Bourmistrova, N. W., Solomon, T., Braude, P., Strawbridge, R., & Carter, B. (2022). Long-term effects of COVID-19 on mental health: A systematic review. Journal of affective disorders, 299, 118–125. https://doi.org/10.1016/j.jad.2021.11.031

[2]    Cahyadi, A. (2021). Anxiety barriers in joining digital online learning during COVID19 pandemic outbreaks. ,. el-Buhuth: Borneo Journal of Islamic Studies, 1-12.

[3]    Chu, Y. H., & Li, Y. C. (2022). The Impact of Online Learning on Physical and Mental Health in University Students during the COVID-19 Pandemic. International journal of environmental research and public health, 19(5), 2966. https://doi.org/10.3390/ijerph19052966

[4]    Houben-Wilke, S., Goërtz, Y. M., Delbressine, J. M., Vaes, A. W., Meys, R., Machado, F. V., van Herck, M., Burtin, C., Posthuma, R., Franssen, F. M., Vijlbrief, H., Spies, Y., van 't Hul, A. J., Spruit, M. A., & Janssen, D. J. (2022). The Impact of Long COVID-19 on Mental Health: Observational 6-Month Follow-Up Study. JMIR mental health, 9(2), e33704. https://doi.org/10.2196/33704 

[5]    Jena, Pravat Kumar, Impact of COVID-19 on Higher Education in India (June 18, 2020). International Journal of Advanced Education and Research (IJAER), Vol-5, Issue-3, Pg-77-81 (2020) DOI- http://www.alleducationjournal.com/archives/2020/vol5/issue3/5-3-27, Available at SSRN: https://ssrn.com/abstract=3691541

[6]    Longest Kaitlyn, Kang Jin-Ae, (2022), Social Media, Social Support, and Mental Health of Young Adults During COVID-19, Frontiers in Communication, v 7, ISSN=2297-900X, https://doi.org/10.3389/fcomm.2022.828135

[7]    López-Valenciano, A., Suárez-Iglesias, D., Sanchez-Lastra, M. A., & Ayán, C. (2021). Impact of COVID-19 Pandemic on University Students' Physical Activity Levels: An Early Systematic Review. Frontiers in psychology, 11, 624567. https://doi.org/10.3389/fpsyg.2020.624567 

[8]    Gina Di Malta, Julian Bond, Dominic Conroy, Katy Smith & Naomi Moller (2022) Distance education students’ mental health, connectedness and academic performance during COVID-19: A mixed-methods study, Distance Education, 43:1, 97-118, DOI: 10.1080/01587919.2022.2029352 

[9]    Gallegos, M. I., Zaring-Hinkle, B., & Bray, J. H. (2021). COVID-19 pandemic stresses and relationships in college students. Family relations, 10.1111/fare.12602. Advance online publication. https://doi.org/10.1111/fare.12602 

[10]    Trógolo, M. A., Moretti, L. S., & Medrano, L. A. (2022). A nationwide cross-sectional study of workers' mental health during the COVID-19 pandemic: Impact of changes in working conditions, financial hardships, psychological detachment from work and work-family interface. BMC psychology, 10(1), 73. https://doi.org/10.1186/s40359-022-00783-y 

[11]    Kuodi P, Gorelik Y, Edelstein M (2022) Characterisation of the long-term physical and mental health consequences of SARS-CoV-2 infection: A systematic review and meta-analysis protocol. PLoS ONE 17(4): e0266232. https://doi.org/10.1371/journal.pone.0266232