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14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Capturing Daily Student Life by Recognizing Complex Activities Using Smartphones

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  • @INPROCEEDINGS{10.4108/eai.7-11-2017.2273984,
        author={Christian Meurisch and Artur Gogel and Benedikt Schmidt and Timo Nolle and Frederik Janssen and Immanuel Schweizer and Max M\'{y}hlh\aa{}user},
        title={Capturing Daily Student Life by Recognizing Complex Activities Using Smartphones},
        proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ACM},
        proceedings_a={MOBIQUITOUS},
        year={2018},
        month={4},
        keywords={student life user behavior analysis mobile sensing activity recognition heterogeneous data sources smartphone},
        doi={10.4108/eai.7-11-2017.2273984}
    }
    
  • Christian Meurisch
    Artur Gogel
    Benedikt Schmidt
    Timo Nolle
    Frederik Janssen
    Immanuel Schweizer
    Max Mühlhäuser
    Year: 2018
    Capturing Daily Student Life by Recognizing Complex Activities Using Smartphones
    MOBIQUITOUS
    ACM
    DOI: 10.4108/eai.7-11-2017.2273984
Christian Meurisch1,*, Artur Gogel1, Benedikt Schmidt1, Timo Nolle1, Frederik Janssen1, Immanuel Schweizer1, Max Mühlhäuser1
  • 1: TU Darmstadt
*Contact email: meurisch@tk.tu-darmstadt.de

Abstract

In-depth understanding of student life is essential to proactively support students in their academic educations. However, there is no work that identifies and recognizes a sufficient set of activities to capture a daily student life since complex activity recognition is still challenging. In this paper, we address this issue by recognizing 10 complex student activities such as learning, attending a lecture, or sleeping. We first identify these relevant student activities by conducting a pre-study with 21 students aiming to get an insight into their daily lives. Based on this outcome, we design our sensing applications to collect an appropriate dataset including user-annotated ground-truth data from 163 students over 4 weeks. We investigate different multi-class hierarchical approaches, as well as compare general models against individual models. The results show that our approaches consistently outperform the baseline classifiers and achieve F1-scores of 82.3% for the 1st level, 83.4% and 72.7% for both 2nd levels (study-related and non-study-related activities) on a merged activity set using individual models. The findings offer a novel way to capture a daily student life and can be used to support or guide students in their study.

Keywords
student life user behavior analysis mobile sensing activity recognition heterogeneous data sources smartphone
Published
2018-04-18
Publisher
ACM
http://dx.doi.org/10.4108/eai.7-11-2017.2273984
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