Qualification: 
B-Tech
rahulkrishnan@am.amrita.edu

Rahul Krishnan P. P. currently serves as a Project Associate at the Amrita Center for Wireless Networks & Applications (Amrita WNA), Amritapuri Campus. He has more than four years experience in different research labs. His focus area has been on designing and developing mobile visualization systems, communication and networking protocols in the area of wireless healthcare systems. He has contributed towards the development of the wireless ECG system developed at AmritaWNA, called Amrita- Spandanam, which is currently being tested at hospitals. He has also worked on the design, development and testing of next-generation remote classroom technologies under a funded project from National Knowledge Network (NKN). Prior to this he has worked with the networks research lab at UC Davis with Dr. Prasant Mohapatra, on network characterization of high bandwidth streaming data on mobile phones. He has also worked on Android applications for building visualization for post-earthquake scenario at HIT Lab, New Zealand with Dr. Mark Billinghurst. He is currently pursuing PhD in wireless communication systems for healthcare devices under Dr. Venkat Rangan.

Qualification

B.Tech in Computer Science and Engineering, Amrita Vishwa Vidyapeetham (2008 - 2012)

Experience

Year Affiliation
September 2012 - Present Project Associate at AmritaWNA, Amrita Vishwa Vidyapeetham
July 2013 - Present

Served as Teaching Assistent for Multimedia Systems, Advanced Computer Networks, Wireless Multimedia Networks and Mobile  Computing and Networking courses at AmritaWNA

June 2012 - August 2012 Research Assistant at UC Davis
February 2012 - June 2012

Research Assistant at HIT (Human Interface Technology) Lab NZ

Awards and Achievements

  • UN Alliance of Civilizations - CREATE finalist for Sanskar Android application - 2014
  • Certificate of Appreciation for achievements in academics for AY 2012 
  • Amrita Vishwa Vidyapeetham silver medal in B.Tech Computer Science and Engineering - 2012

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

Rahul K Pathinarupothi, Ekanath Srihari Rangan, and P Durga, “Deriving High Performance Alerts from Reduced Sensor Data for Timely Intervention in Acute Hypotensive Episodes”, in 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 18) , Honolulu, Hawaii, 2018.[Abstract]


Alerting critical health conditions ahead of time leads to reduced mortality rates. Recently wirelessly enabled medical sensors have become pervasive in both hospital and ambulatory settings. These sensors pour out voluminous data that are generally not amenable to direct interpretation. For this data to be practically useful for patients, they must be translatable into alerts that enable doctors to intervene in a timely fashion. In this paper we present a novel three-step technique to derive high performance alerts from voluminous sensor data: A data reduction algorithm that takes into account the medical condition at personalized patient level and thereby converts raw multi-sensor data to patient and disease specific severity representation, which we call as the Personalized Health Motifs (PHM). The PHMs are then modulated by criticality factors derived from interventional time and severity frequency to yield a Criticality Measure Index (CMI). In the final step we generate alerts whenever the CMI crosses patient-disease-specific thresholds. We consider one medical condition called Acute Hypotensive Episode (AHE). We evaluate the performance of our CMI derived alerts using 7,200 minutes of data from the MIMIC II database. We show that the CMI generates valid alerts up to 180 minutes prior to onset of AHE with F1 score, precision and recall of 0.8, 1.0 and 0.67 respectively, outperforming alerts from raw data.

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2018

Conference Paper

P Durga, Rahul K Pathinarupothi, Ekanath Srihari Rangan, and Prakash Ishwar, “When Less is Better: A Summarization Technique that Enhances Clinical Effectiveness of Data”, in 8th ACM International Digital Health Conference (DH 2018), Lyon, France, 2018.[Abstract]


The increasing number of wearable sensors for monitoring of various vital parameters such as blood pressure (BP), blood glucose, heart rate (HR), etc., has opened up an unprecedented opportunity for personalized real-time monitoring and prediction of critical health conditions of patients. This, however, also poses the dual challenges of identifying clinically relevant information from vast volumes of sensor time series data and of storing and communicating it to health-care providers especially in the context of rural areas of developing regions where communication bandwidth may be limited. One approach to address these challenges is data summarization, but the danger of losing clinically useful information makes it less appealing to medical practitioners. To overcome this, we develop a data summarization technique called RASPRO (Rapid Active Summarization for effective PROgnosis), which transforms raw sensor time series data into a series of low bandwidth, medically interpretable symbols, called “motifs”, which measure criticality and preserve clinical effectiveness benefits for patients. We evaluate the predictive power and bandwidth requirements of RASPRO on more than 16,000 minutes of patient monitoring data from a widely used open source challenge dataset. We find that RASPRO motifs have much higher clinical efficacy and efficiency (20 − 90% improvement in F1 score over bandwidths ranging from 0.2–0.75 bits/unit-time) in predicting an acute hypotensive episode (AHE) compared to Symbolic Aggregate approXimation (SAX) which is a state-of-the-art data reduction and symbolic representation method. Furthermore, the RASPRO motifs typically perform as well or much better than the original raw data time series, but with up to 15-fold reduction in transmission/storage bandwidth thereby suggesting that less is better.

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2017

Conference Paper

Rahul K Pathinarupothi, Bithin Alangot, and Ekanath Srihari Rangan, “Severity Summarization and Just in Time Alert Computation in mHealth Monitoring”, in EFMI International Conference on Informatics for Health, Manchester, UK , 2017, vol. 235, pp. 48-52.[Abstract]


Mobile health is fast evolving into a practical solution to remotely monitor high-risk patients and deliver timely intervention in case of emergencies. Building upon our previous work on a fast and power efficient summarization framework for remote health monitoring applications, called RASPRO (Rapid Alerts Summarization for Effective Prognosis), we have developed a real-time criticality detection technique, which ensures meeting physician defined interventional time. We also present the results from initial testing of this technique.

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2017

Conference Paper

Rahul K Pathinarupothi, Vinaykumar R, Ekanath Srihari Rangan, Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, in IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, 2017, pp. 293-296.[Abstract]


Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.

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2017

Conference Paper

Rahul K Pathinarupothi, Dhara Prathap J, Ekanath Srihari Rangan, Gopalakrishnan E A, Vinaykumar R, and Dr. Soman K. P., “Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning”, in IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, USA, 2017.[Abstract]


A large number of obstructive sleep apnea (OSA) cases are under-diagnosed due unavailability, inconvenience or expense of sleep labs. Hence, an automated detection by applying computational techniques to multivariate signals has already become a well-researched subject. However, the best-known techniques that use various features have not achieved the gold standard of polysomnography (PSG) tests. In this paper, we substantiate the medical conjecture that OSA directly impacts body parameters such as Instantaneous Heart Rate (IHR) and blood oxygen saturation (SpO2). We then use a deep learning technique called LSTM-RNN (long short-term memory recurrent neural networks) to experimentally prove that OSA severity detection can be solely based on either IHR or SpO2 signals, which can be easily, obtained using off-the-shelf non-intrusive wearable single sensors. The results obtained from LSTM-RNN model shows an area under curve (AUC) of 0.98 associated with very high accuracy on a dataset of more than 16,000 apnea non-apnea minutes. These results have encouraged our collaborating doctors to further come up with a diagnostic protocol that is based on LSTM-RNN, SpO2, and IHR, thereby increasing the chances of larger adoption among medical community.

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2017

Conference Paper

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Consensus Motifs as Adaptive and Efficient Predictors for Acute Hypotensive Episodes”, in IEEE EMBC , 39th Annual International Conference ofthe IEEE Engineering in Medicine and Biology Society, Jeju Island, Korea, 2017.[Abstract]


Acute hypotensive episodes (AHE) are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently occurring blood pressure pattern among these which we call consensus motifs. We apply machine learning techniques (support vector machine) on these consensus motifs. The results show that the use of consensus motifs instead of raw time series data extends the predictability by 45 minutes beyond the 2 hours that is possible using only the raw data, yielding a significant improvement without compromising the clinical accuracy. The system has been implemented as part of a new framework called RASPRO (Rapid Summarization for Effective Prognosis) that we have developed for Wireless Remote Health Monitoring.

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2017

Conference Paper

Ekanath Srihari Rangan and Rahul K Pathinarupothi, “Multi-Sensor Architecture and Algorithms for Digital Health at Every Doorstep”, in IEEE International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, 2017.[Abstract]


Consistent cost effective health monitoring has become the need of the hour especially for the unstable, chronically and critically ill. Here we present a novel architecture and algorithmic methodology combining the sensing subsystem and the analytics engines. Physiological parameters from multiple sensors feed into a severity quantizer and a subsequent multiplexer, the output of which is processed by successive physician assist filters to rapidly discover and alert any health criticalities. The architecture is optimized for communication and energy performance, and the algorithms result in lucid presentations to physicians. The whole system is the result of close collaboration between engineering and medical teams at our multidisciplinary University, building on a multi-terabyte, more than a million patient Hospital Information System (HIS) database, and is being readied for deployment on a large telemedicine network of more than 60 nodes in the Indian subcontinent and parts of Africa.

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2016

Conference Paper

A. Arunan, Rahul K Pathinarupothi, and Dr. Maneesha V. Ramesh, “A Real-time Detection and Warning of Cardiovascular Disease LAHB for a Wearable Wireless ECG Device”, in 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2016, pp. 98-101.[Abstract]


According to the World Health Organization, an estimated 17 million people die annually due to cardiac disease, which accounts for 30% of the global deaths. Current studies on cardiac diseases indicate that 15% of the people have Left Anterior Hemiblock (LAHB), which ranks third after Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB). To our knowledge, a reliably consistent disease detection and warning algorithm is not currently available for LAHB although various ECG morphologies can be monitored for real-time detection of LAHB. The objective of this research is to develop a real-time detection and warning of LAHB. The presented work describes the design of a weighted feature-based disease classification algorithm, which can be run in a resource constrained mobile environment for effective realtime diagnosis. The testing and evaluation of the algorithm indicates that it is able to detect LAHB with an accuracy of 95.3% and specificity of 100%. © 2016 IEEE.

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PDF iconA-Real-time-Detection-and-Warning-of-Cardiovascular-Disease-LAHB-for-a-Wearable-Wireless-ECG-Device.pdf

2016

Conference Paper

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Discovering Vital Trends for Personalized Healthcare Delivery”, in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, New York, NY, USA, 2016.[Abstract]


Personalization of remote health monitoring and healthcare delivery is a challenging research problem faced by practitioners and researchers alike. In this paper we present techniques for trend analysis and data summarization using personalized severity based motif discovery in large time series medical data.

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2016

Conference Paper

Rahul K Pathinarupothi, Bithin Alangot, and Rangan, V., “Context Aware Dynamic Log Chunking for Mobile Healthcare Applications: Poster”, in Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, New York, NY, USA, 2016.[Abstract]


Time series data from sensor devices are increasingly stored in log data structures across the cloud and mobile devices. Currently, log data is accessed as chunks of fixed size, which enhances performance by prefetching of data. However, in applications such as remote monitoring of patients using mobile devices, data requirement of end users varies significantly depending upon their roles. The fixed chunking approach would lead to unnecessary data download due to the dynamic variability of data access. Also, the requests are more often than not based on fixed time chunks that do not necessarily translate to fixed data size. To overcome this challenge, we present a dynamic log chunking mechanism based on reader access pattern and domain specific data characteristics. The application of this method in the area of remote patient monitoring in bandwidth starved rural areas is shown to result in bandwidth and cost savings of 14% without affecting the prefetch performance. More »»

2016

Conference Paper

Rahul K Pathinarupothi, Dr. Maneesha V. Ramesh, and Ekanath Srihari Rangan, “Multi-Layer Architectures for Remote Health Monitoring”, in 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany, 2016.[Abstract]


Remote health monitoring and delivery through mobile devices and wireless networks offers unique challenges related to performance, reliability, data size, power management, and analytical complexity. We present a multi-layered architecture that matches communication performance to medical importance of data being monitored. The priority of vital data and the context of sensing are used to select the communication medium and the power management policies. Further smartness is introduced into data summarization by employing a severity level quantizer, followed by a consensus abnormality motif discovery and an alert mechanism that prioritizes doctors' consultative time. We also present our successful implementation of the above multi-layered architecture in a system developed to remotely monitor cardiac patients.

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PDF iconMulti-Layer-Architectures-for-Remote-Health-Monitoring.pdf

2016

Conference Paper

Rahul K Pathinarupothi, Ekanath Srihari Rangan, Bithin Alangot, and Dr. Maneesha V. Ramesh, “RASPRO: rapid summarization for effective prognosis in wireless remote health monitoring”, in 2016 IEEE Wireless Health (WH), 2016.[Abstract]


Consistent power and cost effective health monitoring has become the need of the hour especially for the unstable, chronically and critically ill. Here we present a novel architecture and algorithmic methodology combining the sensing subsystem, symptom summarization, and data transmission. Physiological parameters from multiple sensors feed into a severity quantizer and a subsequent multiplexer, the output of which is processed by the RASPRO engine to rapidly discover and alert any health criticalities. The architecture is optimized for communication and energy performance, and the algorithms result in lucid presentations to physicians. The whole system is the result of close collaboration between engineering and medical teams at one of the best known multidisciplinary universities, building on a multi-terabyte more than a million patient Amrita Hospital Information System (HIS) database, and is being readied for deployment on a large telemedicine network of more than 60 nodes in the Indian subcontinent and parts of Africa.

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2016

Conference Paper

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Large scale remote health monitoring in sparsely connected rural regions”, in GHTC 2016 - IEEE Global Humanitarian Technology Conference: Technology for the Benefit of Humanity, Conference Proceedings, 2016, pp. 694-700.[Abstract]


Remote health monitoring and intervention systems including wearable sensors, smartphones and advanced communication technologies are slated to be a game changer in the delivery of quality healthcare services, especially in developing parts of the world. However, we are yet to see large scale adoption of remote health monitoring systems due to many factors such as: lack of reliable data network coverage, high power requirements for smartphone analytics, and unreliability in the timely delivery of critical data to remote doctors. In addition to these, the huge volume of sensor data and alerts from multiple remote patients are unmanageable for already overloaded doctors. In this paper, we attempt to address each of these issues. First, we propose a novel healthcare communication architecture that connects remotely stationed telemedicine nodes and village clinics with remote doctors in specialty hospitals. Second, we present the development of disease severity pattern discovery and summarization algorithms, the result of which is a Consensus Abnormality Motif (CAM) and an associated Alert Measure Index, which suggests the immediacy of the patient data for doctor's consultative time. By frequently sending CAM as SMS in the absence of data network, we ensure timely delivery of critical data. Through a Detailed Data on Demand (DD-on-D) pull data mechanism doctors can further investigate complete data from the cloud. The CAM and DD-on-D mechanisms result in energy savings of up to 25%, while the data usage is reduced tremendously. Furthermore, we present a pilot deployment of the systems using a continuous cardiac monitoring device coupled with an intervention framework including more than 60 telemedicine nodes station in villages across India. © 2016 IEEE.

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2016

Conference Paper

Dhara Prathap J, Ekanath Srihari Rangan, and Rahul K Pathinarupothi, “Real-time and offline techniques for identifying obstructive sleep apnea patients”, in IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India, 2016.[Abstract]


Obstructive Sleep Apnea (OSA) is a common sleeping disorder in which persons temporarily stop breathing during their sleep. Untreated OSA may lead to several cardio vascular diseases, diabetes, stroke etc. Currently, overnight Polysomnography (PSG) is the widely used technique to detect sleep apnoea. However, a human expert has to monitor the patient overnight. In this paper, we use the technique of motif discovery to identify long term patterns in vital parameters obtained from a combination of smart phones and body attached sensors. We further extend this work to use hamming distance technique to identify similar patients for case based reasoning. Using this, we reduce the need for having expert intervention. As an initial implementation, we have tested our motif discovery technique on Physionet sleep apnea dataset of ECG and SpO2. © 2016 IEEE.

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2016

Conference Paper

Durga Lakshmi, Ekanath Srihari Rangan, and Rahul K Pathinarupothi, “Real-time identification of ischemic events in high risk cardiac patients”, in IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India, 2016.[Abstract]


There is a worldwide trend of increase in cardiac related deaths. One of the major reasons is the condition of cardiac ischemia, which implies inadequacy of blood supply to heart leading to myocardial infarction. One of the main techniques used for detection of ischemia is 12-lead ECG test. However, on most occasions the patient may not be attached to any such devices so as to provide immediate medical help. This emphasizes the need for real time detection of such events. With advances in the field of communication and smartphone-based computations, we are now able to use body attached sensors and smartphone based solutions for real-Time detection of diseases. In our work, we introduce a real-Time smartphone based ischemia detection system, which combines ECG signals from patients along with their activity for identification of ischemia. As an initial step, the impact of patient activity on ischemia is studied, with a comparison between severity threshold method and contextual severity threshold technique. We also present initial test results of this system. Initial results suggest that activities of patient needs to be considered in any ischemia detection system. © 2016 IEEE.

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2016

Conference Paper

Ekanath Srihari Rangan and Rahul K Pathinarupothi, “Adaptive Motif-Based Alerts for Mobile Health Monitoring”, in Mobihealth 2016 (6th International Conference on Wireless Mobile Communication and Healthcare - Transforming healthcare through innovations in mobile and wireless technologies), , Milan, Italy, 2016.[Abstract]


We have developed a rapid remote health monitoring architecture called RASPRO using wearable sensors and smartphones. RASPRO's novelty comes from its techniques to efficiently compute compact alerts from sensor data. The alerts are computationally fast to run on patients' smartphones, are effective to accurately communicate patients' severity to physicians, take into consideration inter-sensor dependencies, and are adaptive based on recently observed parametric trends. Preliminary implementation with practicing physicians and testing on patient data from our collaborating multi-specialty hospital has yielded encouraging results.

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2016

Conference Paper

Ekanath Srihari Rangan and Rahul K Pathinarupothi, “Rapid Healthcare Alerts using Multiple Sensors”, in 38th IEEE Annual International Conference of Engineering in Medicine and Biology, Orlando, FLorida, USA, 2016.[Abstract]


We combine a multi-sensor network with motif-based data summarization methodology leading to effective prognosis of disease conditions of patients via wide area wireless networks. Physiological parameters from multiple sensors feed into a severity quantizer and a subsequent multiplexer, the output of which is processed by successive physician assist filters to rapidly discover and alert any health criticalities. The whole system is the result of close collaboration between engineering and medical teams at one of the best known multidisciplinary universities. We have developed a prototype of the system consisting of wearable wireless ECG and other healthcare IoT (Internet of Things) devices.

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2016

Conference Paper

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Effective Prognosis Using Wireless Multi-sensors for Remote Healthcare Service”, in Healthwear 2016: International Conference on Wearables in Healthcare in Budapest, Hungary, 2016.[Abstract]


Remote healthcare delivery is one of the most promising solutions to tackle global trends in falling health care access and quality of service. A wireless network of sensors, IoT devices, and cloud is presented here. New innovative algorithms for effective prognosis are designed and developed based on motifs and profile matrices. The system consisting of the sensor network and algorithms together enable delivering remote healthcare services.

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2015

Conference Paper

Rahul K Pathinarupothi, “Immediate Lead Positioning Feedback for ML based Wireless ECG”, in HIMS 2015, International Conference on Health Informatics and Medical Systems, 2015.

2015

Conference Paper

Rahul K Pathinarupothi, Dilraj, N., K, R., and Dr. Maneesha V. Ramesh, “A Low Cost Remote Cardiac Monitoring Framework for Rural Regions”, in MobiHealth 2015: 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies", London, Great Britain, 2015.[Abstract]


Cardiovascular diseases in rural developing countries take a large toll of human lives, due to inadequate quality health care facilities and their limited reach to the patients. The burgeoning population of developing nations make the lin- ear organic scaling of health care facilities impractical to cater the diverse rural geography. Hence it is imperative to scale the health care facilities through wireless communi- cation technologies in an aordable manner. Timely anal- ysis of ECG data is critical for early diagnosis and treat- ment of several cardiovascular diseases. With this aim, a wearable wireless ECG monitoring framework, named as Amrita-Spandanam was designed. This framework consist of a patient wearable device and a patient smart phone with Amrita-Spandanam application, enabling a doctor/hospital to monitor the remote patient through his internet con- nected mobile phone or web browser. The framework does the post analysis of the ECG signal using a backend server to disseminate warnings to the doctor and the patient. Sev- eral de-noising algorithms were applied to the acquired ECG signal prior to this post analysis. The framework was imple- mented successfully enabling real time remote monitoring of the cardiac patients in rural villages.

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PDF iconA-Low-Cost-Remote-Cardiac-Monitoring-Framework-for-Rural-Regions.pdf

2015

Conference Paper

Rahul K Pathinarupothi and Dr. Maneesha V. Ramesh, “QRS axis based classification of electrode interchange in wearable ECG devices”, in MobiHealth 2015: 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies", London, Great Britain, 2015.[Abstract]


Wearable ECG monitoring is becoming a convenient way for patients as well as doctors, in tracking and diagnosing heart diseases among large population in rural areas. Wear- able ECG devices along with the smartphones are used to capture and transmit ECG data to hospitals where medi- cal practitioners diagnose and make suitable interventions. ECG electrode cable misplacement poses signicant chal- lenge when untrained population is the end-user. We present a real-time lead misplacement detection system for Mason- Likar lead conguration to provide immediate feedback to patients. It reduces chances of pseudo-disease diagnosis as well as the need for technicians to conrm the validity and quality of captured ECG data. The eld test results show that six dierent Mason-Likar electrode misplacement can be detected and dierentiated from a normal one with a condence value p=0.05.

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PDF iconQRS-Axis-Based-Classification-Of-Electrode-Interchange-In-Wearable-ECG-Devices.pdf

2012

Conference Paper

Rahul K Pathinarupothi, “GeoBoids - A Mobile AR Application for Exergaming”, in ISMAR 2012 International Symposium on Mixed and Augment Reality (Poster Presentation), 2012.

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

Rahul K Pathinarupothi and Rangan, Eb, “Effective prognosis using wireless multi-sensors for remote healthcare service”, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 181 LNICST, pp. 204-207, 2017.[Abstract]


Remote healthcare delivery is one of the most promising solutions to tackle global trends in falling health care access and quality of service. A wireless network of sensors, IoT devices, and cloud is presented here. New innovative algorithms for effective prognosis are designed and developed based on motifs and profile matrices. The system consisting of the sensor network and algorithms together enable delivering remote healthcare services. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

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2017

Journal Article

E. Rangan and Rahul K Pathinarupothi, “Adaptive motif-based alerts for mobile health monitoring”, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 192, pp. 168-176, 2017.[Abstract]


We have developed a rapid remote health monitoring architecture called RASPRO using wearable sensors and smartphones. RASPRO’s novelty comes from its techniques to efficiently compute compact alerts from sensor data. The alerts are computationally fast to run on patients’ smartphones, are effective to accurately communicate patients’ severity to physicians, take into consideration inter-sensor dependencies, and are adaptive based on recently observed parametric trends. Preliminary implementation with practicing physicians and testing on patient data from our collaborating multi-specialty hospital has yielded encouraging results. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

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2015

Journal Article

Rahul K Pathinarupothi, ,, ,, and Uma, G., “Automatic multi-perspective switching for gaze alignment in eLearning systems”, International Journal of Applied Engineering Research (IJAER), vol. 10, 2015.

2015

Journal Article

Rahul K Pathinarupothi, P. Rangan, V., Balaji Hariharan, and Uma, R. N. G., “Real- time Spatial Video Panorama using Iterative Compositing”, International Journal of Applied Engineering Research (IJAER), vol. 10, 2015.

Publication Type: Patent

Year of Publication Publication Type Title

2016

Patent

P. V. Rangan, Balaji Hariharan, Rahul K Pathinarupothi, Narayanankutty, R., Gopakumar, S. A., and Uma Gopalakrishnan, “System and Method for Synthesizing, Preserving Consistent Relative Neighborhood positions in Multiperspective Multipoint Tele-Immersive Environment (2D)”, U.S. Patent US 15/180,4142016.[Abstract]


An e-learning system has a local classroom with an instructor station and a microphone and a local student station with a microphone, a plurality of remote classrooms with an instructor display and a student station with a microphone, and planar displays and video cameras in each of the classrooms, the remote and local classrooms connected over a network, with a server monitoring feeds and enforcing exclusive states, with sets of video displays, each set dedicated to a remote classroom, arrayed along a line orthogonal to a line between the instructor station and the local student station, with one display in each set facing toward the instructor station, and one display in each set facing toward the local student station.

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2016

Patent

P. V. Rangan, Balaji Hariharan, Rahul K Pathinarupothi, Narayanankutty, R., Gopakumar, S. A., and Uma Gopalakrishnan, “System and Method for Synthesizing, Preserving Consistent Relative Neighborhood positions in Multiperspective Multipoint Tele-Immersive Environment (3D)”, U.S. Patent US 15/180,6042016.[Abstract]


An e-learning system has a local classroom comprising a local student station and an instructor station, such that local students at the local student station and an instructor at the instructor station face each other directly along a first viewing line, a plurality of remote classrooms each having a student station, video cameras in each of the remote classrooms positioned and oriented to capture video images of subjects, video displays in the local classroom arranged along a line orthogonal to the first viewing line and all facing the local student station, in sets of at least two displays, arranged vertically one above another, each first set of at least two displays dedicated to one of the remote classrooms, a second plurality of video displays like the first, but facing the instructor, connection apparatus between classrooms, a server coordinating video feeds with displays.

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2015

Patent

P. V. Rangan, Balaji Hariharan, Rahul K Pathinarupothi, Narayanankutty, R., Gopakumar, S. A., and Uma Gopalakrishnan, “System and Method for Synthesizing and Preserving Consistent Relative Neighborhood Position in Multi-Perspective Multi-Point Tele-Immersive Environments”, U.S. Patent US20150062284A12015.[Abstract]


An e-learning system has a local classroom with an instructor station and a microphone and a local student station with a microphone, a remote classroom with an instructor display and a student station with a microphone, and planar displays and video cameras in each of the classrooms, the remote and local classrooms connected over a network, with a server monitoring feeds and enforcing exclusive states, such that audio and video feeds are managed in a manner that video and audio of the instructor, the local students and the first remote students, as seen and heard either directly or via speakers and displays by each of the instructor, the local students and the remote students presents to each as though all are interacting in the same room. (India Provisional Patent Application No. 3888/CHE/2013)

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RANK(INDIA):
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