From simulation to reality: Innovations in ESM research
PhD researcher Alireza Khanshan optimizes ESM research with personalized notifications and introduces innovative tools such as Experiencer and SAPPHIRE
PhD researcher Alireza Khanshan of the Department of Industrial Design received his PhD on December 16 with his research titled 鈥淔rom simulation to reality and back again - A Hybrid Approach to Optimize the Compliance and Tailor for Validity of Responses in ESM Studies鈥�.
What is ESM?
The Experience Sampling Method (ESM) is a research technique in which participants receive notifications at different times of the day to report their feelings, thoughts, and behaviors. This method is widely used in healthcare, psychology and human-computer interaction to accurately capture and analyze subjective experiences.
Improve compliance and validity
Khanshan's research focuses on improving compliance and validity of ESM surveys by personalizing the timing of notifications. He introduces the Wearable Experience Sampling Method (wESM), which uses smartwatches to collect context-sensitive and accurate data. This research shows that personalizing notifications using machine learning can significantly improve participation and data quality.
Practical applications
An important part of Khanshan's research is the development of Experiencer, an open-source tool for wESM, which has been used in more than 15 experiments at five research institutions. This tool helps optimize the timing of notifications and improve the user experience. He also introduces SAPPHIRE, a human behavior simulator that generates data to overcome cold start problems in ESM studies.
Alireza Khanshan defended his thesis on 16 December. Title of PhD thesis: 鈥溾��. Supervisors: Panos Markopoulos and Pieter Van Gorp.