Matchmaking for Mental Well-being: Development of a Peer-based Support System (Peer2S) for Students during COVID Lockdown

Social support is known to be a critical factor for mental well-being. More specifically, the protective effect of quality social support in times of crisis is well documented in many psychological studies. In this study, we developed a social support matching system that connects people who are going through similar life circumstances to provide peer-based support, allowing them to better cope with their situation together. As a case study, we focused on Japanese students whose lives were impacted by the COVID-19 lock down. To develop the recommendation model used in our system, 50 participants were asked to register their profile and afterwards, 20 users determined whether they would match with each of the profiles resulting in 1000 data points. We then experimented with various collaborative filtering and deep learning approaches and evaluated their effectiveness in recommending profiles to users. Finally, a user experiment study was conducted in which 11 users used the system 2 weeks. The results showed that while there was no significant difference in perceived social support, users reported significantly less anxiety and a borderline reduction in depression. © 2023 Owner/Author.

Detecting Changes in User Emotions During Bicycle Riding by Sampling Facial Images

In the context of mobility as a Service (MaaS), bicycles are an important mode of transport for the first and last mile between the home and other transport modalities. Also, due to covid-19 bicycle users such as food delivery drivers and commuters to work are increasing. To investigate driving experience of bicycle users in context and improve MaaS service quality, we propose and describe a method to automatically detect changes in user emotions during bicycle riding by sampling facial images using a smartphone. We describe the proposed method and how we plan to use it in the future. © 2022 Owner/Author.