Name
Abstract | ||
|---|---|---|
Physiological sensor analytics is becoming an important tool to monitor health as the availability of sensor-enabled portable, wearable, and implantable devices becomes ubiquitous in the growing Internet of Things (IoT). Physiological multi-sensor studies have been conducted previously to detect stress. In this study, we focus on ECG monitoring that can now be performed with minimally invasive wearable patches and sensors, to develop an efficient and robust mechanism for accurate stress identification. A unique aspect of our research is personalized individual stress analysis including three stress levels: low, medium and high. Using machine learning algorithms from the ECG signals alone, we could achieve 88.24% accuracy in detecting the three classes of stress. We also find that high stress can be successfully detected for a person in comparison to his or her rest period with 100% accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BigData.2015.7364066 | Big Data |
Keywords | Field | DocType |
ecg signals, stress medicine, machine learning, classification, time series, physiological sensors, driving, driver monitoring, precision medicine | Precision medicine,Computer science,Wearable computer,Internet of Things,Feature extraction,Artificial intelligence,Analytics,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.52 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Keshan | 1 | 5 | 0.52 |
| P. V. Parimi | 2 | 5 | 0.52 |
| Isabelle Bichindaritz | 3 | 532 | 55.74 |