Abstract
In recent years, various methods for the automated recognition of human activities based on the analysis of sensor data using machine learning approaches have been researched. The continuously increasing hardware performance of mobile devices such as smartphones and wearables and the ongoing development of existing algorithms have resulted in steadily higher recognition rates. These latest advances resulted in an growing demand for intelligent and context sensitive mobile applications. However, the generation of valid ground truth information with a suitable quality remains a major challenge. In addition, there is currently no standardized procedure for generating an activity classifier for the use in custom application areas. The holistic workflow introduced in this paper focuses on the recognition of activities using mobile devices. For this purpose, the generation of a ground truth information by recording and annotating sensor data is described. The generated data set is used for transfer learning in a machine learning framework and the resulting model is the basis for a mobile real-time classification application. The data source is a current study of the University of Applied Sciences Mittweida.
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This work was written in the junior research group “Agile Publika” funded by the European Social Fund (ESF) an the Free State of Saxony.
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Rolletschke, T., Roschke, C., Thomanek, R., Platte, B., Manthey, R., Zimmer, F. (2019). Generation of Individual Activity Classifiers for the Use in Mobile Context-Aware Applications. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-23528-4_42
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