Comprehensive Diabetic Care
I have been working to build a system for both patients and providers to create an extra channel between them. 
Posts by Collection
portfolio
Human Activity Recognition
One of my research goals has been the development of software and hard-ware platforms for detecting and measuring different human activities of a remote target subject. 
publications
Preserve Your Privacy with PCO: A Privacy Sensitive Architecture for Context Obfuscation for Pervasive E-Community Based Applications
Published in IEEE International Conference on Social Computing (SocialCom), Minneapolis, 2010
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PryGuard: a secure distributed authentication protocol for pervasive computing environment
Published in international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence (IEA/AIE), 2011
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Findings of e-ESAS: a mobile based symptom monitoring system for breast cancer patients in rural Bangladesh
Published in Conference on Human Factors in Computing Systems (CHI) , Austin, Texas, 2012
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Mobile Based health Care Solution for Breast Cancer Patients
Published in Proceedings of M4D, New Delhi, 2012
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e-ESAS: improving quality of life for breast cancer patients in developing countries
Published in Proceedings of the 2nd ACM international workshop on Pervasive Wireless Healthcare (MobileHealth), 2012
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User privacy protection in pervasive social networking applications using PCO
Published in International Journal of Social Computing and Cyber-Physical Systems, 2012
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Identifying Phases of Gait and Development of Walking Model from Pressure and Accelerometer Data and It’s Ramifications in Elderly Walking
Published in International Conference on Smart Homes and Health Telematics (ICOST ), 2013
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Towards in situ affect detection in mobile devices: a multimodal approach
Published in Proceedings of the 2013 Research in Adaptive and Convergent Systems (RACS), Montreal, Canada, 2013
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In situ affect detection in mobile devices: a multimodal approach for advertisement using social network
Published in ACM SIGAPP Applied Computing Review, 2013
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Smartphone based multimodal activity detection system using plantar pressure sensors
Published in Proceedings of the 29th Annual ACM Symposium on Applied Computing (SAC), 2014
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Findings of mobile based palliative care system: towards formulating a generic framework for measuring QoL
Published in PervasiveHealth 2014: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare, 2014
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Barriers for Breast Cancer (BC) Patients in Rural Bangladesh: Design and Deployment of a Mobile based Solution
Published in AMCIS 2014 Savannah, Georgia, 2014
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e-ESAS: Evolution of a participatory design-based solution for breast cancer (BC) patients in rural Bangladesh
Published in Personal and Ubiquitous Computing, 2014
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Remote Monitoring Using Smartphone Based Plantar Pressure Sensors: Unimodal and Multimodal Activity Detection
Published in International Conference on Smart Homes and Health Telematics (ICOST), 2014
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A Novel Activity Detection System Using Plantar Pressure Sensors and Smartphone
Published in Computer Software and Applications Conference (COMPSAC), 2015
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Assessment of Pain Using Facial Pictures Taken with a Smartphone
Published in Computer Software and Applications Conference (COMPSAC), 2015
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Activity Detection Using Time-Delay Embedding in Multi-modal Sensor System
Published in International Conference on Smart Homes and Health Telematics, 2016
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DeepDSSR: Deep Learning Structure for Human Donor Splice Sites Recognition
Published in Studies in health technology and informatics, 2019
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DeepCNPP: Deep Learning Architecture to Distinguish the Promoter of Human Long Non-Coding RNA Genes and Protein-Coding Genes
Published in Studies in Health Technology and Informatics, 2019
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talks
Remote Monitoring Using Smartphone Based Plantar Pressure Sensors: Unimodal and Multimodal Activity Detection
Published:
Automatic activity detection is important for remote monitoring of elderly people or patients, for context-aware applications, or simply to measure one’s activity level. Recent studies have started to use accelerometers of smart phones. Such systems require users to carry smart phones with them which limit the practical usability of these systems as people place their phones in various locations depending on situation, activity, location, culture and gender. We developed a prototype for shoe based activity detection system that uses pressure data of shoe and showed how this can be used for remote monitoring. We also developed a multimodal system where we used pressure sensor data from shoes along with accelerometers and gyroscope data from smart phones to make a robust system. We present the details of our novel activity detection system, its architecture, algorithm and evaluation.
Smart Phone Based Monitoring of Different Parameters
Published:
The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectationmaximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases.
Remote Monitoring Using Smart Phones
Published:
We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. Development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been discussed. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm.
The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities.
teaching
Fall 2019
Undergraduate course, East Tennessee State University, Department of Computing, 2019
I taught Software Engineering I and Machine Learning in Fall 2019.
Spring 2018
Undergraduate course, East Tennessee State University, Department of Computing, 2020
I taught Software Engineering I and Software Engineering II in Spring 2018.
Spring 2020
Undergraduate and graduate course, East Tennessee State University, Department of Computing, 2020
I taught the following two courses in Spring 2020.