Flow is the phenomenon of a state of optimal experience characterized by deep task engagement, a merging of action and awareness, and a balance between perceived challenges and individual skills, first described and conceptualized by Mihály Csíkszentmihályi (1975). While it has been shown to occur in teams (Peifer et al., 2021), it primarily emerges in individual experiences. Flow at work is something that both employers and employees strive for, as it is associated with increased well-being and improved performance (e.g., Harris et al, 2023; Ilies et al., 2016; Stano et al., 2026).

Detecting flow, however, remains a challenge. The primary method of measurement is usually self-report (e.g., Rosas et al. 2023), which, as a drawback, is either retrospective or disrupts the flow state through interruption. Therefore, researchers have sought to detect flow using wearable devices that capture various biosignals (cf. Irshad et al., 2023). However, to date, only one study has attempted to detect flow states using an earable device (Knierim et al., 2021), which, moreover, relied solely on EEG signals. Therefore, the goal of this thesis is to employ the multimodal capabilities of OpenEarable 2.0 (Röddiger et al., 2025) to explore to what extent they can be used to detect flow states in individuals.

1. Analyze the current state of the literature with respect to approaches that have used wearable solutions to detect flow.
2. Based on this, develop a rationale for your study, as well as a protocol and study design.
3. Conduct the study and collect data using the multimodal sensing capabilities of OpenEarable 2.0. It is highly recommended to implement the study protocol within the OpenWearables app.
4. Evaluate how the sensors of OpenEarable can be employed for flow detection.

MA Additional:

• Evaluate how the different sensor streams contribute to flow detection (for orientation, see Hummel & Burzer et al. (2026)).
• Develop a (condensed) model that is lightweight enough to be deployed for real-time use on a smartphone.
• Develop an application capable of detecting flow in real time, implemented as an in-app feature within the OpenWearables app.

• Designing and conducting a user-study
• Strong skills in Python (for data analysis)
• Solid skills in Flutter (for building the in-app in OpenWearables).
• Optional (MA only): Experience with bwUniCluster.

• A paragraph explaining your motivation.
• Your study program (Bachelor/Master), current semester, and field of study.
• A transcript of records (courses and grades).
• Your programming experience.
• Any areas of interest relevant to the topic.
• Your CV (if available)