TU Berlin

Quality and Usability LabJan-Niklas Voigt-Antons

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Dr.-Ing. Jan-Niklas Voigt-Antons


Jan-Niklas Voigt-Antons joined the Telekom Innovation Laboratories as a research scientist in January 2009 and is working there since 2014 as a senior research scientist. He received his diploma in psychology in 2008 from the Technische Universität Darmstadt, Germany, a Doctor-of-Engineering degree in 2014 from the Technische Universität Berlin, Germany and has been doing research at the Quality and Usability Lab at the Technische Universität (TU) Berlin, since. His research interests are in Quality-of-Experience evaluation and its physiological correlates with an emphasis on media transmissions and human-machine-interaction, including neural processing of multimodal interaction. During summer 2012 he was visiting researcher at MuSAE Lab (INRS-EMT), Canada where he examined neural correlates of quality perception for complex speech signals. In spring 2014 he was visiting researcher at the department of psychology of NTNU, Norway where he examined neural correlates of audiovisual asynchrony.

QULab research group: Quality, User Experience, Augmented and Virtual Reality

Research Topics: 

• Multimedia Experience (Usability evaluation methods, Quality-of-Experience evaluation physiological measures)

• Interaction Design (Adaptive software, data mining, sensor and behavioural data)

Current projects:


Measuring of immersive media experience

Exergaming in virtual reality

DemTab - Tabletgestützte ambulante Versorgung von Menschen mit Demenz

VoiceAdapt - Adaptives Sprachtraining für ältere Menschen mit Aphasie

OurPuppet - Pflegeunterstützung mit einer interaktiven Puppe für informell Pflegende

Past projects:

PflegeTab - Technik für mehr Lebensqualität trotz Pflegebedürftigkeit bei Demenz (GKV)

Quality of Mobile Gaming

Bernstein Focus Neurotechnology - Berlin (BFNT - B)


Affective Computing
Study Project Quality & Usability (6/9 CP)


Current thesis offers of our lab can be found here. Please contact me via email if you are interested in doing a thesis supervised by me.


Current job offers of our lab can be found here


+49 30 8353 58 377


Technische Univertistät Berlin
Quality and Usability Lab
Telekom Innovation Laboratories
Ernst-Reuter-Platz 7
10587 Berlin, Germany


Predicting personality traits from touchscreen based interactions
Citation key küster2018
Author Küster, Ludwig and Trahms, Carola and Voigt-Antons, Jan-Niklas
Title of Book 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)
Pages 1–6
Year 2018
ISBN 978-1-5386-2605-4
DOI 10.1109/QoMEX.2018.8463375
Location Cagliari, Italy
Address Piscataway, NJ, USA
Month may
Note Online
Publisher IEEE
Series QoMEX
How Published Fullpaper
Abstract Human influence factors can have a strong impact on how users perceive the quality of a given system. Traditional measures such as questionnaires can be a time consuming and partially invasive means to assess human influence factors like personality. In this paper, we investigate whether an individual's personality traits can be classified based solely upon the characteristics of their touchscreen usage. We record the tablet input of 75 subjects using a cognitive training application. The application requires the user to spell words by tapping on letters and dragging them to their denoted position on the screen. The task is presented in two conditions: in one with normal functionality of the program, the other with the usability of the application impaired. The subject's personality is measured with the NEO-FFI questionnaire and captured in the five dimensions of the five-factor model (Big 5). The personality scores and 68 features (touch-behavior and task-performance), as well as statistical metrics derived from them, are fed to a number of classification algorithms. Our results show that in a binary classification task with normal usability, high levels of `Neuroticism' can be distinguished from low levels with a mean accuracy of 66 percent. The extent to which a personality dimension can be predicted is dependent on the usability of the test system. In a normal setting, `Extraversion' can be predicted with an accuracy of 61 percent. However, with impaired usability, the prediction rises to an average of 67 percent. The experiment shows that records of touchscreen interactions allow for the prediction of personalities significantly better than random. The study of the human influence factor personality and its relation to perceived quality would be facilitated by using touchscreen interaction data as a fast and easily accessible estimate of a user's personality.
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