Shen, Zhouyang; (2025) Prediction and Control of Spatiotemporal Modulation Parameters Effects on Human Perceptual and Emotional Responses. Doctoral thesis (Ph.D), UCL (University College London).

Text Eletronic_Thesis_Copy.pdf - Accepted Version Access restricted to UCL open access staff until 1 November 2027. Download (12MB)

Abstract

Contactless interaction is an emerging field that has gained increasing interest in recent years. One of the main approaches used in this field is mid-air haptic stimuli, which can be delivered using air-jet, lasers, or ultrasound. Among these methods, ultrasound mid-air haptics (UMH) has become increasingly popular due to its high spatial and temporal resolutions. To achieve immersive contactless interactions, many researchers have explored how different UMH modulation techniques affect the tactile experiences (i.e., perceptual and emotional responses) users can develop from the stimuli. Despite that, currently, no systematic pipeline has been presented and few research studies have been done to investigate the effect of latest modulation technique (i.e., STM) and predict and alter tactile experiences users perceive from this technique, for different interaction contexts. This research attempts to address this gap by deploying a fundamental framework o build such models. I accomplish this by 1) Understand: reviewing the existing literature on the stimulation parameters and potential perceptions inducible by UMH devices, understanding these parameters' effects on user perceptions and emotions through subjective and objective measures, 2) Predict: using data-driven approaches to link stimulation parameters with these responses, and 3) Control: demonstrating the potential to develop a real-time closed-loop control system to dynamically induce desired perceptions through adjusting UMH stimulation parameters. The contributions of this research expanded our understanding of how UMH affects perceptions and emotions and enabled the development of advanced modulation techniques and UMH systems that intelligently predict and, eventually, optimize the tactile experiences. It also offers significant insights into the integration of human-computer interaction and data-driven modeling, demonstrating the potential of data-driven approaches to enhance various interaction systems.

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