Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6484203 | Biocybernetics and Biomedical Engineering | 2017 | 13 Pages |
Abstract
Human activity recognition (HAR) from wearable motion sensor data is a promising research field due to its applications in healthcare, athletics, lifestyle monitoring, and computer-human interaction. Smartphones are an obvious platform for the deployment of HAR algorithms. This paper provides an overview of the state-of-the-art when it comes to the following aspects: relevant signals, data capture and preprocessing, ways to deal with unknown on-body locations and orientations, selecting the right features, activity models and classifiers, metrics for quantifying activity execution, and ways to evaluate usability of a HAR system. The survey covers detection of repetitive activities, postures, falls, and inactivity.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Bioengineering
Authors
Jafet Morales, David Akopian,