Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone
Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized appro...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/7/2203 |
| _version_ | 1817989103677865984 |
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| author | Federico Cruciani Ian Cleland Chris Nugent Paul McCullagh Kåre Synnes Josef Hallberg |
| author_facet | Federico Cruciani Ian Cleland Chris Nugent Paul McCullagh Kåre Synnes Josef Hallberg |
| author_sort | Federico Cruciani |
| collection | DOAJ |
| description | Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine). |
| first_indexed | 2024-04-14T00:42:57Z |
| format | Article |
| id | doaj.art-f9d270b125324e229c6195958f739a08 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:42:57Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f9d270b125324e229c6195958f739a082022-12-22T02:22:08ZengMDPI AGSensors1424-82202018-07-01187220310.3390/s18072203s18072203Automatic Annotation for Human Activity Recognition in Free Living Using a SmartphoneFederico Cruciani0Ian Cleland1Chris Nugent2Paul McCullagh3Kåre Synnes4Josef Hallberg5Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UKComputer Science Research Institute, Ulster University, Newtownabbey BT370QB, UKComputer Science Research Institute, Ulster University, Newtownabbey BT370QB, UKComputer Science Research Institute, Ulster University, Newtownabbey BT370QB, UKDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenData annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).http://www.mdpi.com/1424-8220/18/7/2203human activity recognitionsupervised machine learninglabel noiseautomatic annotationinertial sensorssmartphone |
| spellingShingle | Federico Cruciani Ian Cleland Chris Nugent Paul McCullagh Kåre Synnes Josef Hallberg Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone Sensors human activity recognition supervised machine learning label noise automatic annotation inertial sensors smartphone |
| title | Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone |
| title_full | Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone |
| title_fullStr | Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone |
| title_full_unstemmed | Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone |
| title_short | Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone |
| title_sort | automatic annotation for human activity recognition in free living using a smartphone |
| topic | human activity recognition supervised machine learning label noise automatic annotation inertial sensors smartphone |
| url | http://www.mdpi.com/1424-8220/18/7/2203 |
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