Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data
Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature ext...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidad Internacional de La Rioja (UNIR)
2012-06-01
|
| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_5.pdf |
| _version_ | 1818759531763597312 |
|---|---|
| author | Pekka Siirtola Juha Röning |
| author_facet | Pekka Siirtola Juha Röning |
| author_sort | Pekka Siirtola |
| collection | DOAJ |
| description | Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent. |
| first_indexed | 2024-12-18T06:44:13Z |
| format | Article |
| id | doaj.art-e4e57baa53d24aa88d1e713be384bb74 |
| institution | Directory Open Access Journal |
| issn | 1989-1660 |
| language | English |
| last_indexed | 2024-12-18T06:44:13Z |
| publishDate | 2012-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-e4e57baa53d24aa88d1e713be384bb742022-12-21T21:17:32ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602012-06-01153845Recognizing Human Activities User-independently on Smartphones Based on Accelerometer DataPekka SiirtolaJuha RöningReal-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent.http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_5.pdfActivity recognitionclassificationmobile phones |
| spellingShingle | Pekka Siirtola Juha Röning Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data International Journal of Interactive Multimedia and Artificial Intelligence Activity recognition classification mobile phones |
| title | Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data |
| title_full | Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data |
| title_fullStr | Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data |
| title_full_unstemmed | Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data |
| title_short | Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data |
| title_sort | recognizing human activities user independently on smartphones based on accelerometer data |
| topic | Activity recognition classification mobile phones |
| url | http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_5.pdf |
| work_keys_str_mv | AT pekkasiirtola recognizinghumanactivitiesuserindependentlyonsmartphonesbasedonaccelerometerdata AT juharoning recognizinghumanactivitiesuserindependentlyonsmartphonesbasedonaccelerometerdata |