Predicting passenger discomfort using multimodal sensors

Discomfort during long-haul flights is a significant concern for passengers, particularly for those traveling in business class who expect a high level of comfort and luxury. However, there is a lack of smart seat cushions available in the market that can function in real time.

This project explores the design of a novel pneumatic-based seat cushion that is smart and modular. The cushion is specifically engineered to redistribute pressure in the seat pan, providing a highly comfortable and effective solution for long-haul flights. It incorporates a Velostat pressure mat that is capable of measuring pressure in real time, and has an innovative machine learning model to integrate objective and subjective data to better understand the factors that contribute to discomfort during prolonged use.

Project Team

Students:

  • Byun Hyun Bin (Electrical Engineering, Class of 2023)
  • Jonathan Khoo Teng Tang (Computer Engineering, Class of 2023)
  • Pok Ruey Jye (Mechanical Engineering, Class of 2023)
  • Sarupraba D/O Arjunan (Biomedical Engineering, Class of 2023)

Supervisors: