Integrating Mobile and Cloud for PPG Signal Selection to Monitor Heart Rate During Intensive Physical Exercise
| Title | Integrating Mobile and Cloud for PPG Signal Selection to Monitor Heart Rate During Intensive Physical Exercise |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Jindal, Vasu |
| Conference Name | Proceedings of the International Conference on Mobile Software Engineering and Systems |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4178-3 |
| Keywords | accelerometer, belief networks, Collaboration, composability, Deep belief networks, Deep Learning, heart rate monitoring, Human Behavior, Internet of Things (IoT), Metrics, policy, PPG signals, pubcrawl, Resiliency, Scalability |
| Abstract | Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However, current determination of heart rate through mobile applications suffers from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for selection of PPG signals using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%. |
| URL | http://doi.acm.org/10.1145/2897073.2897132 |
| DOI | 10.1145/2897073.2897132 |
| Citation Key | jindal_integrating_2016 |