Vijay N. Tiwari

Quantifiable fitness tracking using wearable devices.

Monitoring health and fitness is emerging as an important benefit that smartphone users could expect from their mobile devices today. Rule of thumb calorie tracking and recommendation based on selective activity monitoring is widely available today, as both on-device and server based solutions. What is surprisingly not available to the users is a simple application geared towards quantitative fitness tracking. Such an application potentially can be a direct indicator of ones cardio-vascular performance and associated long term health risks. Since wearable devices with various inbuilt sensors like accelerometer, gyroscope, SPO2 and heart rate are increasingly becoming available, it is vital that the enormous data coming from these sensors be used to perform analytics to uncover hidden health and fitness associated facts. A continuous estimation of fitness level employing these wearable devices can potentially help users in setting personalized short and long-term exercise goals leading to positive impact on ones overall health. The present work describes a step in this direction. This work involves an unobtrusive method to track an individuals physical activity seamlessly, estimate calorie consumption during a day by mapping the activity to the calories spent and assess fitness level using heart rate data from wearable sensors. We employ a heart rate based parameter called Endurance to quantitatively estimate cardio-respiratory fitness of a person. This opens up avenues for personalization and adaptiveness by dynamically using individuals personal fitness data towards building robust modeling based on analytical principles.

Remote health monitoring system for detecting cardiac disorders.

Remote health monitoring system with Clinical Decision Support System as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with ECG signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as Myocardial Infarction. The system, that we have developed consists of three major components viz., (a) Mobile Gateway, deployed on patients mobile device, that receives 12-Lead ECG signals from any ECG sensor (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) Point of Care Device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present work our focus has been towards developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.

Cuff-less PPG based continuous blood pressure monitoring — A smartphone based approach

Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individuals vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individuals vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.

Continuous monitoring of stress on smartphone using heart rate variability

Continuous monitoring of an individuals stress levels is essential to manage stress and mental state in an effective way. With increasing ubiquity of wearable heart rate monitors and their unobtrusiveness, HRV (Heart rate variability) derived from heart rate signals has emerged as one of the most relevant parameters for continuous monitoring of stress. In the present work, we have made an attempt to address the challenges about distinguishing between stressed and non-stressed state of a person based on just one minute of IBI (Inter Beat Interval) records with good accuracy. Such ultra-short term analysis of HRV is particularly advantageous towards capturing very short term fluctuations in mental stress levels and enhanced scope for frequent monitoring. We have analyzed various time domain, frequency domain and nonlinear HRV features to narrow down to a most influential set of features for accurate classification between stressed and non-stressed state. We have identified RMSSD (root mean square of successive differences) of IBI series to be the most direct indicator of stressed state. We also provide a continuous stress score which, when used in continuous monitoring scenario, provides the user with adequate details about his/her stress levels. This helps the user to understand stress patterns across a day in a better way and to take appropriate measures to manage stressful situations. We have developed and deployed a system, based on above concept, on smartphone as an android application for real-time stress monitoring.

Remote health monitoring system for detecting cardiac disorders

Remote health monitoring system with Clinical Decision Support System as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with ECG signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as Myocardial Infarction. The system, that we have developed consists of three major components viz., (a) Mobile Gateway, deployed on patients mobile device, that receives 12-Lead ECG signals from any ECG sensor (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) Point of Care Device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present work our focus has been towards developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.

Endurance based personalized fitness planner

Endurance is an important factor of cardiovascular fitness indicating the capacity of an individual to perform exercise for a longer duration with increased intensity. Various subject specific and exercise related parameters affect endurance of an individual. In this work, we propose a statistical technique to model endurance as a function of these factors incorporating the serial dependence of observations generated by individuals over time. The proposed model provides a device to predict future endurance of a test subject following particular exercise regime. This facilitates a test user with a fitness planner with the provision to fix exercise regimes to reach a set fitness goal.

StayFit: A wearable application for Gym based power training

Comprehensive fitness training involves both cardiorespiratory and power components. Often power/muscle strength training is confused with cardiorespiratory endurance training. However, each of them target different physiological aspects of fitness. Although, wearable based fitness trackers designed towards cardiorespiratory endurance training are available in the market, a dedicated wearable based fitness application designed for power training/tracking is still not readily available to fitness enthusiasts. With growing usage of wearable technology to manage and track personal health, it is imperative to develop health/fitness applications for wearables. A wearable based application for power training will allow the user to track build-up of muscle strength unobtrusively over a period of time. This work provides a framework and design for automatic detection, counting repetitions of strength training Gym exercises (covering all the major muscle groups), estimate personalized calories spent in each session and track power on a standalone Gear watch (both analysis and display including User Experience(UX) design). Our proposed method detects activity with ~96% sensitivity and ~96% specificity on an average and count repetitions with an overall accuracy of >95% using motion sensor data (accelerometer, gyroscope) for eight major Gym exercises. Additionally, using heart rate sensor data we have provided a mechanism to individually track the power/muscle strength of a person. This work will give further impetus towards developing wearable based systems for personalized fitness tracking and training. This will also give an option for wearable users to address both the crucial aspects of fitness (cardiorespiratory and muscle strength).