Next Article in Journal
Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
Previous Article in Journal
A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(8), 1265;

UniMiB AAL: An Android Sensor Data Acquisition and Labeling Suite

Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Author to whom correspondence should be addressed.
Received: 16 June 2018 / Revised: 20 July 2018 / Accepted: 26 July 2018 / Published: 31 July 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [5303 KB, uploaded 31 July 2018]   |  


In recent years, research on techniques to identify and classify activities of daily living (ADLs) has significantly grown. This is justified by the many application domains that benefit from the application of these techniques, which span from entertainment to health support. Usually, human activities are classified by analyzing signals that have been acquired from sensors. Inertial sensors are the most commonly employed, as they are not intrusive, are generally inexpensive and highly accurate, and are already available to the user because they are mounted on widely used devices such as fitness trackers, smartphones, and smartwatches. To be effective, classification techniques should be tested and trained with datasets of samples. However, the availability of publicly available datasets is limited. This implies that it is difficult to make comparative evaluations of the techniques and, in addition, that researchers are required to waste time developing ad hoc applications to sample and label data to be used for the validation of their technique. The aim of our work is to provide the scientific community with a suite of applications that eases both the acquisition of signals from sensors in a controlled environment and the labeling tasks required when building a dataset. The suite includes two Android applications that are able to adapt to both the running environment and the activities the subject wishes to execute. Because of its simplicity and the accuracy of the labeling process, our suite can increase the number of publicly available datasets. View Full-Text
Keywords: dataset; Android application; ADL recognition; falls detection dataset; Android application; ADL recognition; falls detection

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ginelli, D.; Micucci, D.; Mobilio, M.; Napoletano, P. UniMiB AAL: An Android Sensor Data Acquisition and Labeling Suite. Appl. Sci. 2018, 8, 1265.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top