• Emotional design and evaluation of intelligent human-machine systems based on shared mobility
  智慧共享交通之感性設計與人機系統評估 | 2023–2025 | $1,612,000 
  Principal Investigator.
  Technological development has influenced people's behaviour and their expectations of everyday product and systems. In the context of self-driving, for instance, the user's attention may be shifted to the interior of the vehicle. Hence, how to effectively distribute visual feedback within in human-centered system services is essential for a better user experience. This project aims to apply Kansei engineering to design and evaluate the intelligent human-computer interactions for shared mobility. The eye-tracking technology will also be utilized to study user's cognitive load and trust. The findings of this study will serve as an important reference for future human-robot collaborations, providing potential benefits for various industries and applications.
  [NSTC-1122221E027080]
  
  

• Applying Kansei engineering to explore the influence of cognitive bias and information design for intelligent human-machine interfaces
  應用感性工學於智慧人機介面之認知偏誤與提示設計 | 2022–2023 | $758,000
  Principal Investigator.
  People interact with products and systems that are driven by artificial intelligence (AI), such as robot vacuums, route recommendations, and voice assistants, in their daily lives. However, machines may not operate and respond properly to human activities all the time. Unpredictable behaviours and errors occur. The sudden changes in user experience reduce the perceived trustworthiness leading to unsafe reactions. One possible reason is due to the effect of human cognitive biases in training the machine learning model. To build people’s confidence and acceptance of AI-driven applications, integrating a user-centred approach in the digital data-oriented design process is warranted. The findings of this study will contribute to a better understanding of the design process, in hope of developing an integrated conceptual framework for sustainable human-machine systems.
  [MOST-1112222E027006]

• Integrating data literacy in human factors design teaching practice research | 2024–2025 |
  數據素養在人因設計中的教學實踐與效果評估
  Principal Investigator.
  Current human factors design courses emphasise hands-on practice for physical mockups but often overlook the data needed for informed decisions based on user behaviour and needs. This project combines problem-based and self-directed learning to enhance students' understanding of human factors data and cultivate their data literacy. Students will learn how to collect and analyse this data to evaluate their designs. This will prepare them for interdisciplinary project collaborations and enable them to create more human-centric product systems in the future development of AI products.
  [MOE-PHA1136495]
  
  

• Introducing chatbot to enhance design students’ metacognitive skills in collaborative learning | 2023–2024 | 
  導入聊天機器人以增進設計學生於合作學習模式之後設認知能力
  Principal Investigator.
  This project aims to improve the efficacy of Human Factors Engineering education, shifting from traditional exam-based assessments to bridging theory with design practice. As it stands, students often struggle with cooperative learning during project-based assignments, resulting in unproductive discussions, incomplete conclusions, and waning motivation. Harnessing technological advancements, the instructor can incorporate tools like chatbots into course activities to augment the learning experience, ultimately enhancing students' satisfaction and overall course outcomes.
  [MOE-PHA1123033]

• Empirical study and simulation of commuting behaviour and acceptance of personal mobility devices | 2018 – 2020 | S$339,500
  Lead researcher.
  Advances in Personal Mobility Device (PMD) technology is a boon to Singapore Government’s push to be a car-lite nation. However, public acceptance and experience of PMDs have not been fully investigated in Singapore. For example, due to lack of infrastructure, pedestrians and PMDs are compelled to share the footpaths, posing an increased risk to pedestrians. Therefore, the aim of this study is to develop guidelines that can help pedestrians of all ages feel comfortable and safe around PMDs in terms of relative speed and distance. A proper understanding of their interactions helps regulators impose effective safety measures. The findings of this project are crucial to the simulation of last-mile commuting behaviour, so as to fulfil the vision of smart urban mobility.
  [Paper 1] [Paper 2]

AY 2021/22

• Designing sustainable packaging to reduce damage to fresh produce using generative design methods

• Harnessing smart assistive technology for practicing and learning Braille

• Design of 3D-printed bicycle saddles based on functionally-graded lattice structures 

AY 2019/20

• Experimental assessment of personal mobility devices for first and last-mile commuting on a shared path with elderly pedestrian  [Industry partner: Schaeffler Singapore]

• A study on pedestrian crowd simulations through distributed personal mobility device docking hubs [Industry partner: Schaeffler Singapore]

• Smart goggle for peronsal mobility 

AY 2018/19 

• Next generation urban deployment system for electric personal mobility vehicles [Renaissance Capstone Project]

• Designing active safety features for personal mobility devices

• Data-driven design for the user experience of personal mobility devices

AY 2017/18

• Integrated system for the future air traffic management work place and human factors evaluations of conflict resolution aid in future air traffic control [Industry partner: Air Traffic Management Research Institute, Singapore]

AY 2014/15

• Influence of bicycle saddle on comfort level of female cyclists [Industry partner: Giant Manufacturing, Taiwan]

• Influence of gender, drop handlebar height and handgrip position on dynamic motion during cycling [Industry partner: Giant Manufacturing, Taiwan]