Rituraj Singh

SensIPro: Smart sensor analytics for Internet of things

Sensors play a vital role for realizing the vision of connected smart universe. In this paper, we present a novel sensor agnostic model SensIPro to perform robust unsupervised analysis of sensor data to support scalable analytics, a prime need for Internet of things (IoT). In the context of sensor analytics, outliers contain most delicate information. Analysis of anomaly or outlier is mostly dependent on the application domain as well as signal characteristics. Our proposed sensor analytics model SensIPro automates analysis of outliers based on inferring signal characteristics of diverse sensors from different IoT applications like healthcare, smart energy, smart transport. We apply relevant time-series algorithms using statistical analysis, information theoretic measure for sensor data analytics. We measure similarity/dissimilarity of the time series sensor data and correlate with detected outliers. Our algorithm does not require any prior knowledge of sensor data type and metadata. We present results and analysis based on real life heterogeneous sensor data sets. Obtained results further prove efficacy of the proposed mechanism.

Demo: IAS: Information Analytics for Sensors

Sensors are one of the primary building blocks of IoT. Owing to close proximity of physical world, sensors often collect sensitive information. Invariably, sensor data has rich information content. Here we propose a novel solution IAS: Information Analytics for Sensors to unlock massive potential of sensor data through information analytics and demonstrate an alerting mechanism based on criticality of sensor information. ECG anomaly detection for healthcare, unusual appliance operation detection from smart energy meter data, bad road condition as well as activity detection from accelerometer data are typical use-case scenarios. We use robust statistical and information theoretic approaches. Our approach is unsupervised and is completely sensor agnostic. This abstract provides overview of design and implementation of our tool IAS along with obtained results tested on publicly available datasets. Last but not the least, IAS validates that outliers contain most delicate information.

An unsupervised learning for robust cardiac feature derivation from PPG signals

We propose here derivation algorithms for physiological parameters like beat start point, systolic peak, pulse duration, peak-to-peak distance related to heart rate, dicrotic minima, diastolic peak from Photoplethysmogram (PPG) signals robustly. Our methods are based on unsupervised learning mainly following morphology as well as discrete nature of the signal. Statistical learning has been used as a special aid to infer most probable feature values mainly to cope up with presence of noise, which is assumed to be insignificant compared to signal values at each investigation window. Performance of the proposed method is found to be better than other standard methods, yielding precision and sensitivity more than 97% obtained from three real life data sets.We propose here derivation algorithms for physiological parameters like beat start point, systolic peak, pulse duration, peak-to-peak distance related to heart rate, dicrotic minima, diastolic peak from Photoplethysmogram (PPG) signals robustly. Our methods are based on unsupervised learning mainly following morphology as well as discrete nature of the signal. Statistical learning has been used as a special aid to infer most probable feature values mainly to cope up with presence of noise, which is assumed to be insignificant compared to signal values at each investigation window. Performance of the proposed method is found to be better than other standard methods, yielding precision and sensitivity more than 97% obtained from three real life data sets.

iCarMa: Inexpensive Cardiac Arrhythmia Management -- An IoT Healthcare Analytics Solution

Ubiquity of smartphones with array of inbuilt sensors, pave ways to inexpensive mobile-health systems, particularly for cardio-vascular health monitoring. Smartphones, wearable sensors, and body area sensors play an important role as a part of Internet of Things (IoT) m-health ecosystem. In this paper, we present iCarMa to enable an inexpensive auto-triggered arrhythmia cardiac management solution catering the need of in-house, round-the-clock cardiac health monitoring. It facilitates early detection of fatal cardiac conditions like asystole, extreme bradycardia, extreme tachycardia, ventricular flutter and ventricular tachycardia, which often compel an individual to get admitted in Intensive Care Unit (ICU). Smartphone or wearable sensor extracted photoplethysmogram (PPG) is the sole physiological signal that is considered to characterize the cardiac anomalous events. Our main novelty is to precisely detect and remove the motion artifacts in PPG signals and to ensure accuracy in arrhythmia condition detection, specifically to reduce the false negative alarms. We establish the efficacy of proposed solution, iCarMa by large set of experiments with field-collected and MIT-Physionet PPG signals.

CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement

In this paper, we present CardioFit, a completely noninvasive cardiac condition monitoring system that enhances the clinical utility of health care analytics like lowering false detection of cardiac arrhythmia condition, higher accuracy in heart rate variability (HRV) computation. It performs powerful local analysis to enable accurate as well as easy-to-use, round-the-clock in-house, remote or mobile cardiac health checking. Here, photoplethysmogram (PPG) is the sole physiological signal considered for cardiac health management. It is to be noted that PPG carries significant necessary features what is available from electrocardiogram (ECG) signal. Unlike ECG, extraction of PPG is noninvasive, easy and affordable using smartphone or other low cost sensors. However, PPG is frequently contaminated with various kinds of motion artifacts and noise. Our robust concoction of signal processing and machine learning techniques exhibit higher accuracy in the detection and removal of the corrupt PPG signal segments. The proposed mechanism substantially improves the detection capability of the cardiac condition. Efficacy of our scheme is depicted using publicly available MIT-Physionet database as well as through our own field-collected real-life PPG data.

3S: Sensing Sensor Signal: Demo Abstract

Detection of normal and anomalous events from sensor signal is a key necessity in todays 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.

Heartmate: automated integrated anomaly analysis for effective remote cardiac health management

Remote cardiac health management is an important healthcare application. We have developed Heartmate that enables basic screening of cardiac health using low cost sensors or smartphone-inbuilt sensors without manual intervention. It consists of robust denoising algorithm along with effective anomaly analytics for physiological signals. Heartmate identifies and eliminates signal corruption as well as detects cardiac anomaly condition from physiological cardiac signals like heart sound or phonocardiogram (PCG) and photoplethysmogram (PPG).