On the homogenization of heterogeneous inertial-based databases for human activity recognition

In the last years supervised machine learning techniques are largely employed for automatic Human Activity Recognition (HAR) using inertial sensors, such as accelerometer and gyroscope. HAR has many applications in several domains such as, for example, healthcare, sport, and entertainment. Machine learning scientists made available to the community a plenty of labeled databases for benchmarking that, unfortunately, are not consistent, both syntactically (e.g., different sampling frequency) and semantically (e.g., labels with different meanings). Commonly, due to this inconsistency, scientists evaluate their progress on individual databases separately, which corresponds to training and testing using the same database. Coherent merging of existing databases would enable: 1) evaluation of generalization capabilities of methods across databases; 2) use of deep learning techniques that, unlike traditional ones, require much more labeled data for the training process. Moreover, the growth in the daily use of wearable devices will produce a big amount of inertial data which, if not correctly labeled, cannot be efficiently exploited for the study of automatic HAR. In this paper we propose a semi-automatic procedure to coherently merge existing databases based on signal and word similarity. Preliminary experiments demonstrates the effectiveness of the proposed procedure.