Flow experience detection
The COVID -19 pandemic has encouraged the spread of virtual teamwork, and today more and more teams around the world are collaborating virtually. However, virtual teams also have their pitfalls, such as less informal communication, which reduces the effectiveness of the team. Team flow is a concept with great potential to promote team effectiveness but measuring and promoting it is challenging. Traditional measures of team flow rely on self-assessment questionnaires that require interruption of the team process. Artificial intelligence approaches, i.e., machine learning, provide methods for identifying an algorithm based on behavioral and sensor data that is capable of determining team flow and its dynamics over time without interrupting the process.
In cooperation with Prof. Corinna Peifer(Institute of Psychology I, University of Lübeck) we are developing a learning-based pattern recognition platform that will automatically determine the flow experience based on data from multiple wearable sensors. Data acquisition for our pilot study is carried out with several devices (Empatica E4, RespiBan and Emotiv Epoc X) during arithmetic and reading task. These record a variety of physiological features (such as BVP, EDA, EMG, ECG, EEG and EOG) during the test.
We are currently working on data from our pilot study (10 subjects) to classify flow vs. no flow (i.e., in a 2-class problem). In the coming months, we plan to collect data from virtual teams to develop an algorithm capable of determining team flow and its dynamics using behavioral and sensor data.
Contact person
Dr.-Ing. Xinyu Huang
Third party funded projects and publications
DFG project: V-T-Flow - Team Flow and Team Effectiveness in Virtual Teams. Duration: 01/15/2022 - 01/14/2025.
Peifer, Corinna, Anita Pollak, Olaf Flak, Adrian Pyszka, Muhammad Adeel Nisar, Muhammad Tausif Irshad, Marcin Grzegorzek, Bastian Kordyaka, and Barbara Kożusznik. "The symphony of team flow in virtual teams. using artificial intelligence for its recognition and promotion." Frontiers in Psychology 12 (2021): 697093.
Leonie Kloep, Martje Buss, Marcin Grzegorzek, Muhammad Tausif Irshad, Philip Gouverneur, Barbara Kożusznik, Anita Pollak, Olaf Flak, Adrian Pyszka, and Corinna Peifer. "A Computational Approach to Understand Social Flow and Its Role in Interpersonal Relationships in Virtual Teams – Project Outline and First Results from a Pilot Study”. EAWOP Congress (2023), Poland.
- Research
- AI und Deep Learning in Medicine
- Medical Image Processing and VR-Simulation
- Integration and Utilisation of Medical Data
- Sensor Data Analysis for Assistive Health Technologies
- Medical Image Computing and Artificial Intelligence
- Medical Data Science Lab
- Medical Deep Learning Lab
- Medical Data Engineering Lab
- Junior Research Group Diagnostics and Research of Movement Disorders
Contact person
Xinyu Huang
Wissenschaftliche Mitarbeiter
Gebäude 64, 2.OG
,
Raum 09
x.huang(at)uni-luebeck.de
+49 451 3101 5612