Comparison of Feature Learning Methods for Human Activity Recognition using Wearable Sensors

Frédéric Li1, Kimiaki Shirahama1, Muhammad Adeel Nisar1, Lukas Köping1, Marcin Grzegorzek1,2

1 Research Group for Pattern Recognition, University of Siegen, Germany
2 Department of Knowledge Engineering, University of Economics in Katowice, Poland

Note (last updated on May 10, 2019): This webpage is a copy of the one linked in the paper, which was originally located on the website of Research Group for Pattern Recognition at the University of Siegen (Germany). Because the whole research group moved to another University at the beginning of 2019, the old webpage will no longer be accessible in the coming months. Consequently, all research materials used in the paper have been moved to the web server of Kindai University (Japan), the current institution of the second author. The contents contained and linked in this webpage are exactly the same as the original ones.

The increasing availability of wearable sensors made the obtention of personal data easier, opening new opportunities of applications for Human Activity Recognition (HAR). The interest in this field is visible by the growing number of solutions - in particular deep-learning-based - proposed to learn effective feature representations for the classification of activities by analysing large amounts of data.

However, the solutions proposed in past research works can often prove to be hard to reproduce, due to unsufficient amounts of provided details regarding the data processing operations or setup of the feature learning models. In addition, the lack of a baseline evaluation framework makes a strict comparison between those techniques - as well as the establishment of a hierarchy among them - difficult.

To address those issues, an evaluation framework fixing the data pre-processing, segmentation and classifier construction phase is defined to test and compare the effectiveness of several state-of-the-art feature learning approaches including:

Figure of the Activity Recognition Chain (ARC). Raw data are firstly acquired from sensors. After pre-processing, segments of data are extracted (Segmentation) and values relevant for the recognition problem are computed from them (Feature extraction). A classifier is then trained and evaluated using those features (Classification). In the frame of our study, all steps of the ARC, except the feature extraction part, are fixed.

The evaluation framework is used on two benchmarks for wearable-based HAR: the OPPORTUNITY and UniMiB-SHAR datasets. The results of the comparative study are summarized in our publication.

Links to repository

All research materials used for the studies, including raw and processed datasets, codes and instructions on how to use them, learned features, are provided on this webpage (see overview.txt). This includes:

For any question about the paper, study or code, please contact the main author of the publication, whose coordinates can be found here.

Citation request

Please reference the following publication [1] if you use any of the codes and/or datasets linked above:

[1] Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, Marcin Grzegorzek, Comparison of Feature Learning Methods for Human Activity Recognition using Wearable Sensors, Sensors, Vol. 18, No. 2, Article No. 679, 2018 (paper (open access))