Soma Bandyopadhyay
Arijit Ukil
Arpan Pal
ABSTRACT
Detection of normal and anomalous events from sensor signal is a key necessity in today's smart world. Here, we propose a novel mechanism to classify normal and anomalous phenomena by using self-learning of signal, i.e., by discovering its pattern. This is the first step in the long drawn out analysis of signals. We demonstrate a prototype of our proposed method by using a real field quasi-periodic photoplethysmogram (PPG) signal with (or without) motion artifacts, which has an immense impact on cardiac health monitoring, stress, blood pressure, and SPO2 measurement. We have achieved more than 90% accuracy to detect anomalous phenomena in the signal.
- Bandyopadhyay, S., Ukil, A., Puri, C., Pal, A., Singh, R. and Bose, T. "Demo: IAS: Information Analytics for Sensors" In 13th ACM Sensys 2015. Google Scholar
Digital Library
- Bandyopadhyay, S., Ukil, A., Puri, C., Singh, R., Pal, A., Mandana, K and Murthy, C. A. "An Unsupervised Learning for Robust Cardiac Feature Derivation from PPG Signals" In EMBC, 2016.Google Scholar
- Bandyopadhyay S, Ukil A, Puri C, Singh R, Bose T, Pal A. SensIPro: Smart sensor analytics for Internet of things. In 2016 IEEE Symposium on Computers and Communication (ISCC) 2016 Jun 27 (pp. 415-421). IEEE.Google Scholar
Recommendations
Enteroscopic findings of Celiac Disease and their correlation with mucosal histopathologic changes
Background and aimsSingle Balloon Enteroscopy enables us to examine the small bowel for various diseases. It provides a view of the intestinal mucosa with biopsy capability, which may be helpful in search of a mucosal disease such as Celiac Disease. ...
Quantification of liver fat
Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases ...
Comments