Name
Abstract | ||
|---|---|---|
•We investigate activity recognition using unsupervised learning, with a smartphone.•The number of activities can be determined by the Caliński–Harabasz index.•The mixture of Gaussian outperforms when the number of activities is known.•The hierarchical clustering and DBSCAN attain above 90% accuracy for appropriate settings.•The study provides an idea for activity recognition methods without training datasets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2014.04.037 | Expert Systems with Applications |
Keywords | Field | DocType |
Human activity recognition,Unsupervised learning,Healthcare services,Smartphone sensors,Sensor data analysis | Hierarchical clustering,Data mining,Activity recognition,Computer science,Supervised learning,Unsupervised learning,Gaussian,Artificial intelligence,Machine learning,DBSCAN | Journal |
Volume | Issue | ISSN |
41 | 14 | 0957-4174 |
Citations | PageRank | References |
37 | 1.02 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongjin Kwon | 1 | 37 | 1.02 |
| Kyuchang Kang | 2 | 127 | 14.39 |
| Changseok Bae | 3 | 161 | 23.90 |