Demo Abstract: 3S-Sensing sensor signal

Authors

Soma Bandyopadhyay, TCS Research
Arijit Ukil, TCS Research
Rituraj Singh, TCS Research
Chetanya Puri, TCS Research
Arpan Pal, TCS Research
C. A. Murthy, Indian Statistical Institute, Kolkata

Document Type

Conference Article

Publication Title

Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016

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 quasiperiodic 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.

First Page

302

Last Page

303

DOI

10.1145/2994551.2996533

Publication Date

11-14-2016

Recommended Citation

Bandyopadhyay, Soma; Ukil, Arijit; Singh, Rituraj; Puri, Chetanya; Pal, Arpan; and Murthy, C. A., "Demo Abstract: 3S-Sensing sensor signal" (2016). Conference Articles. 693.
https://digitalcommons.isical.ac.in/conf-articles/693