Name
Title | ||
|---|---|---|
A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks. |
Abstract | ||
|---|---|---|
Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16071033 | SENSORS |
Keywords | Field | DocType |
artificial organic networks,artificial hydrocarbon networks,robust human activity recognition,supervised machine learning,wearable sensors,noise tolerance | Data mining,Noisy data,Activity recognition,Wearable computer,Artificial intelligence,Engineering,Healthcare system,Classifier (linguistics),Noise tolerance,Machine learning | Journal |
Volume | Issue | Citations |
16 | 7.0 | 5 |
PageRank | References | Authors |
0.42 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiram E. Ponce | 1 | 26 | 13.63 |
| María de Lourdes Martínez-Villaseñor | 2 | 26 | 8.35 |
| Luis Miralles Pechuán | 3 | 6 | 0.78 |