The coronavirus pandemic led to an unprecedented number of people working from home, which also led to an increasing number of online meetings. Several surveys in Switzerland and around the globe have shown that the home-office option will persist after the pandemic. While it has been advised to follow the 20-20-20 rule to limit eye constraint from computer use, in reality this is hardly achieved (our survey indicated that 65.85% of the participants only take a break after more than 60 minutes of computer usage). Sensing technologies which could capture human visual fatigue could assist users to manage their visual comfort; however, when and how to notify them is still an open question. The motivation of this review is to understand the acceptable and effective ways to learn from the user routine and notify users on possible actions towards better visual comfort management during remote working condition, specifically using reinforcement learning methods.

References:

Park, J., Dougherty, T., Fritz, H., Nagy, Z. (2018). LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning. Building and Environment Ho, B.

J., Balaji, B., Koseoglu, M., & Srivastava, M. (2018, October). Nurture: notifying users at the right time using reinforcement learning. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 1194-1201).

Reference person:

• Sailin Zhong

Ridesharing and delivery companies represent one of the major industries in the gig economy. A common claim of the gig-economy – e.g. from Uber – is that workers may work "whenever they want", "wherever they want", and that they can "be their own boss". This claim, at least in the case of Uber, is not always supported by Uber and its platform. This is primarily due to the various barriers that drivers face to their agency in terms of information/power asymmetries, algorithmic management, and emotional labor [1]. Food delivery (car) drivers face the absence of companionship and safety risks [3, 4]. This review aims to discover one specific angle the gig drivers struggled (e.g., safety, communication with the customer), and discuss alternatives compared to existing gig driver platforms (e.g., using AR, new ways of visualization). Such alternatives can be presented as but not limited to technological probes [5].

References:

[1] Ma, N. F., Yuan, C. W., Ghafurian, M., & Hanrahan, B. V. (2018, April). Using stakeholder theory to examine drivers' Stake in Uber. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12)

[2] Glöss, M., McGregor, M., & Brown, B. (2016, May). Designing for labour: uber and the on-demand mobile workforce. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 1632-1643).

[3] Seetharaman, B., Pal, J., & Hui, J. (2021). Delivery Work and the Experience of Social Isolation. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-17.

[4] Yao, Z., Weden, S., Emerlyn, L., Zhu, H., & Kraut, R. E. (2021). Together But Alone: Atomization and Peer Support among Gig Workers. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-29.

[5] Hutchinson, H., Mackay, W., Westerlund, B., Bederson, B. B., Druin, A., Plaisant, C., ... & Eiderbäck, B. (2003, April). Technology probes: inspiring design for and with families. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 17-24).

Reference person:

• Sailin Zhong

In this work, we want to investigate the potential of augmented reality based serious games on wearable devices for people with visual impairments. The student will precise the notion of visual impairment (low vision), review existing serious games for visually impaired in the literature and try to come up with a novel idea that could be ported on an wearable augmented reality device. Finally he should imagine an experiment to evaluate his proposed solution. The proposed serious game should aim at facilitating rehabilitation of low vision individuals.

References:

G. Regal, E. Mattheiss, D. Sellitsch and M. Tscheligi, “Mobile location-based games to support orientation & mobility training for visually impaired students,” In Proc. of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '18), pp. 1–12, doi: 10.1145/3229434.3229472

A. Dragos Bogdan Moldoveanu et al., "Virtual environments for training visually impaired for a sensory substitution device," 2017 Zooming Innovation in Consumer Electronics International Conference (ZINC), 2017, pp. 26-29, doi: 10.1109/ZINC.2017.7968654.

M. I. Torres-Carazo, M. J. Rodríguez-Fórtiz and M. V. Hurtado, "Analysis and review of apps and serious games on mobile devices intended for people with visual impairment," 2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH), pp. 1-8, 2016, doi: 10.1109/SeGAH.2016.7586263

Reference person :

• Yong-Joon Thoo
• Dr Simon Ruffieux

The industry 4.0 is profoundly affecting the manufacturing sector because of its vision of automation. New technologies like collaborative robotics (cobots) bring new opportunities for this revolution, but the communication gap between the human and the machine is a bottleneck slowing down the collaboration of the two entities.

At the Berner Fachhochschule (BFH), we are developing a robotic cell that tries to bring a solution to this problem, using the help of no-code programming, AR/VR tools and cobots. The use of these different tools provides a maximum of information about the state of the system, helpful for the user to better understand, and therefore control the system.

The goal of this project is to do a short state of the art of AR/VR technologies acceptance and extract what could prevent the acceptance of this technology. In a second part, we want to emit a hypothesis on how to improve this acceptance in the industrial context, and propose an experiment to validate our theory.

References:

Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (Hentout et al., 2019)

An investigation of acceptance and e-readiness for the application of virtual reality and augmented reality technologies to maintenance training in the manufacturing industry (Scott et al., 2020)

20 years of research on virtual reality and augmented reality in tourism context: A text-mining approach (Loureiro, 2020)

Reference person :

• Charly Blanc

As air traffic complexity keeps growing, the tasks performed by the Air Traffic Controller (ATCO) is becoming more and more difficult. New methods, purely visual or based on Machine Learning, are being developed to help the ATCO in conflict resolution tasks. However, the ATCO often has difficulty understanding the behaviour of such AI-driven aids, and the benefits they offer are balanced by a poor User Experience (UX). This seminar paper will constitute a literature review of the methods aimed to improve the collaboration between the human agent and the Artificial-Intelligence (AI)-driven aids.

References

Kistan, T.; Gardi, A.; Sabatini, R. Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification. Aerospace 2018, 5, 103. https://www.mdpi.com/2226-4310/5/4/103#cite

Reference person :

• Raphael Tuor

In face of the need for climate change mitigation, individual behaviour change has probably the most potential [1,3]. However, convincing users to change requires providing them meaningful and operative pathways towards lower carbon behaviour, not leading to rebound effects [2]. Offering change proposals tailored to specific lifestyles is likely to increase impact. These lifestyles could be based on observations, but also be constructed through data-centric sensing [5]. The review will concentrate on what kind of technological support hardware and/or software [4] would help in detecting different lifestyle groups regarding carbon-intensive dimensions such as nutrition, mobility or comfort.

References

[1] Coskun, A., Zimmerman, J., & Erbug, C. (2015). Promoting sustainability through behavior change: A review. Design Studies, 41, Part B, 183–204. https://doi.org/10.1016/j.destud.2015.08.008

[2] Elf, P., Gatersleben, B., & Christie, I. (2019). Facilitating Positive Spillover Effects: New Insights From a Mixed-Methods Approach Exploring Factors Enabling People to Live More Sustainable Lifestyles. Frontiers in Psychology, 9. https://www.frontiersin.org/article/10.3389/fpsyg.2018.02699

[3] Habib, R., White, K., Hardisty, D. J., & Zhao, J. (2021). Shifting consumer behavior to address climate change. Current Opinion in Psychology, 42, 108–113. https://doi.org/10.1016/j.copsyc.2021.04.007

[4] Wang, W., Harari, G. M., Wang, R., Müller, S. R., Mirjafari, S., Masaba, K., & Campbell, A. T. (2018). Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), 141:1-141:21. https://doi.org/10.1145/3264951

[5] Zhao, S., Li, S., Ramos, J., Luo, Z., Jiang, Z., Dey, A. K., & Pan, G. (2019). User profiling from their use of smartphone applications: A survey. Pervasive and Mobile Computing, 59, 101052. https://doi.org/10.1016/j.pmcj.2019.101052

Reference person :

• Dr Julien Nembrini