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TU Berlin

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Carola Trahms

Lupe

Biography

Carola Trahms received her M.Sc. degree in Computer Engineering from TU Berlin in 2015. During her studies she focused on Artificial Intelligence and Machine Learning. She joined the Quality and Usability Labs in 2016 as a research assistant.

 

Teaching

 

Address

Quality and Usability Lab
Technische Universität Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin, Germany

E-Mail:
Tel:  +49 30 8353 58479

Publications

Finding critical features for predicting quality of life in tablet-based serious games for dementia
Zitatschlüssel cha2019a
Autor Cha, Jeehoon and Voigt-Antons, Jan-Niklas and Trahms, Carola and O'Sullivan, Julie Lorraine and Gellert, Paul and Kuhlmey, Adelheid and Möller, Sebastian and Nordheim, Johanna
Seiten 1–20
Jahr 2019
ISSN 2366-0147
DOI 10.1007/s41233-019-0028-2
Journal Quality and User Experience
Jahrgang 4
Nummer 1
Monat nov
Notiz online, print
Verlag Springer
Wie herausgegeben Fullpaper
Zusammenfassung While the number of dementia cases is steadily increasing, as of today no medication has been developed to cure its underlying causes. Instead, the focus in treatment has shifted to improve quality of life (QoL) for people with dementia (PwD). To this end, some non-pharmacological treatments such as exercising, socializing, and playing games have received increasing attention. PflegeTab is a tablet-based application developed for this purpose. It includes a number of services such as cognitive training games, everyday activity training games, emotional applications, and a biographical picture album. In the present paper, we explore the possibility of QoL prediction for PwD using data collected while nursing home residents played games in PflegeTab ($$N = 81$$N=81). Using features generated from the data and applying linear discriminant analysis for classification, our approach obtained an average accuracy of 74.80% on predicting QoL ratings when measured by Monte Carlo cross-validation. Furthermore, this paper investigates which features were dominant for the classification (prominent features were e.g. time needed for task completion) and briefly discusses how the results might be utilized for managing general QoL of PwD.
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