Name
Title | ||
|---|---|---|
Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors. |
Abstract | ||
|---|---|---|
Tracking the spatio-temporal activity is highly relevant for domains like security, health, and quality management. Since animal welfare became a topic in politics and legislation locomotion patterns of livestock have received increasing interest. In contrast to the monitoring of pedestrians cattle activity tracking poses special challenges to both sensors and data analysis. Interesting states are not directly observable by a single sensor. In addition, sensors must be accepted by cattle and need to be robust enough to cope with a rough environment. In this article, we introduce the novel combination of heart rate and positioning sensors. Attached to neck and chest they are less interfering than accelerometers at the ankles. Exploiting the potential of such combined sensor system that records locomotion and non-spatial information from the heart rate sensor however is challenging. We introduce a novel two level method for the activity tracking focused on the duration and sequence of activity states. We combine Support Vector Machine (SVM) with Conditional Random Field (CRF) and extend Conditional Random fields by an explicit representation of duration. The SVM characterizes local activity states, whereas the CRF addresses sequences of local states to sequences incorporating spatial and non-spatial contextual knowledge. This combination provides a reliable and comprehensive identification of defined activity patterns, as well as their chronology and durations, suitable for the integration in an activity data base. This data base is used to extract physiological parameters and promises insights into internal states such as fitness, well-being and stress. Interestingly we were able to demonstrate a significant correlation between resting pulse rate and the day of pregnancy. |
Year | DOI | Venue |
|---|---|---|
2016 | https://doi.org/10.1007/s10707-016-0260-3 | GeoInformatica |
Keywords | Field | DocType |
Spatio-temporal pattern,Machine learning,Activity monitoring,Indoor positioning system,Animal monitoring | Conditional random field,Data mining,Accelerometer,Support vector machine,Sensor system,Correlation,Pulse rate,Activity tracking,Geography,Indoor positioning system | Journal |
Volume | Issue | ISSN |
20 | 4 | 1384-6175 |
Citations | PageRank | References |
1 | 0.41 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Behmann | 1 | 61 | 6.52 |
| Kathrin Hendriksen | 2 | 1 | 0.41 |
| Ute Müller | 3 | 1 | 1.09 |
| Wolfgang Büscher | 4 | 2 | 4.27 |
| Lutz Plümer | 5 | 141 | 23.12 |