Ferdaus Kawsar

Ferdaus Kawsar

Software Developer @Infosys/Apple | Former Assistant Professor of Computer Science @ETSU

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

Comprehensive Diabetic Care

I have been working to build a system for both patients and providers to create an extra channel between them.

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

Published in , 1900

Published in , 1900

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.