Immersive Micromanipulation Integrating Pipette and Injector Operations with McKibben-Based Haptic Sensations for Workload Reduction

Intracytoplasmic sperm injection (ICSI) requires advanced micromanipulation techniques but relies solely on visual feedback and involves frequent interface switching between pipette movement and injector operations. Existing haptic feedback systems primarily focus on pipette puncture forces and do not provide feedback on injector states. We developed an immersive micromanipulation system that unifies operational interfaces and provides McKibben-based haptic sensations to represent aspiration, discharge, and contact between the oocyte and pipette. Users operated both the pipette and injector with a single hand while receiving haptic sensations. A human-participant experiment revealed that the immersive operation interface improved micromanipulation speed and reduced cognitive workload of the micromanipulation compared with conventional methods. Additionally, McKibben-based haptic sensations improved overall system usability. The immersive micromanipulation system with McKibben-based haptic sensations successfully unified operational interfaces and reduced operator workload.

MutualEyeContact: A conversation analysis tool with focus on eye contact

Eye contact between individuals is particularly important for understanding human behaviour. To further investigate the importance of eye contact in social interactions, portable eye tracking technology seems to be a natural choice. However, the analysis of available data can become quite complex. Scientists need data that is calculated quickly and accurately. Additionally, the relevant data must be automatically separated to save time. In this work, we propose a tool called MutualEyeContact which excels in those tasks and can help scientists to understand the importance of (mutual) eye contact in social interactions. We combine state-of-the-art eye tracking with face recognition based on machine learning and provide a tool for analysis and visualization of social interaction sessions. This work is a joint collaboration of computer scientists and cognitive scientists. It combines the fields of social and behavioural science with computer vision and deep learning.