Smart Home Health Analytics -- Spring 2022
Times: Tuesday 4:30pm – 7:00pm
Location: On-campus Meeting Room: Math & Psychology 101
Online Meeting Room: UMBC Blackboard Collaborate (Virtual) -- 1st Two Classes
Instructor: Nirmalya Roy
Instructor’s Office Location and Hours: ITE 421 Thursday 9:00 am – 10:00 am, WebEx or by appointment
Instructor’s Email: nroy at umbc dot edu
Course Descriptions: This course will examine different machine learning methodologies, toolsets, wearable and ambient systems that are warranted to recognize, discover, and learn the human activities, behaviors, and their profound impact on human health and wellness in smart home environments. Human activity recognition is a growing field of research with its far-reaching impact on proactive health care, physical fitness, and healthy living. We will discuss different machine learning algorithms, evaluation metrics, and their application to human activity and behavior recognition. A special emphasize will be given to the techniques with respect to a variety of emerging smart environment contexts spanning across monitoring activities of daily living (ADLs), physiological signals (heart rate, galvanic skin response) and psychological behaviors (emotion, stress, depression, agitation) etc. These human signals can be sensed and measured using wearable and IoT (Internet-of-Things) technologies such as smart phones and wristwatches to keep people informed about their activity, life-style, health, mood, behavior, and surrounding. Students are required to do a group or individual project. The tools necessary to develop the project will be reviewed and made available in class, but students are expected to be comfortable with basic use of software, smart phones and smart watches and have an interest in using technology for smart home health monitoring, assessment, and intervention.
Course Objectives: The purpose of this course is to provide a comprehensive foundation to apply machine learning, data science, and statistical learning methodologies in solving problems in real life applications such as smart home healthcare. This course should enhance students’ reasoning, problem-solving and modeling abilities, particularly in dealing with data science problems. More specifically, the course has the following objectives:
Familiarize students with the concepts and applications of machine and statistical learning techniques to solve real life data science problems.
Teach students how to think and formalize smart environments research problems with real data and what computational techniques to apply.
We will not assume any background beyond high school level mathematics and familiarity with programming concepts. However, students are expected to spend time in learning the concepts in this course, many of which will be covered in detail.
Bayesian Decision Theory
Machine Learning Evaluation
Machine Learning Toolkits
Smart Home Health Technologies
Functional and Behavioral Health Assessment
Qualitative and Quantitative Clinical Health Assessment Tools
Course Prerequisites: Consent of the instructor
Instructional Materials: Given that this is an interdisciplinary IS course cross cutting machine learning, data science, Internet of Things, smart home technologies and clinical psychology, there are no single textbook that fully cover the integrated aspects of the course material. At the first half of the course the instructor will discuss the core methodologies behind the course and in the second half the students will be reading various research articles to assimilate the emerging trend in the subject matter of smart home health analytics. A list of research articles will be made available at the course webpage. There are a few books that are relevant to this course are listed next.
Recommended Textbooks (Optional):
Introduction to Machine Learning, Second Edition, by Ethem Alpaydin, MIT Press, 2010 (Amazon.com)
Machine Learning, Tom Mitchell, McGraw Hill, 1997 (Amazon.com)
Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data by Diane J. Cook, Narayanan C. Krishnan, Wiley, 2015 (Amazon.com)
Course Project: Students are required to do a group project (individual or two students in each group) as part of this course work. Each team is required to propose a study hypothesis, collect their own datasets over a period of time using smartphones and smartwatches for a specific smart environments application as approved by the instructor and use of appropriate data analytics and machine learning methodology to recognize and evaluate the context of the users.
Reading, Writing, and Oral Assignments: Each student will be required to participate in the research reflection group discussion exercise in class, present individually one research paper and the group research project at the end of the semester. Each student is also required to write three critiques on three distinct research papers being presented by the fellow students in the class.
Review of Probability & Statistics
Machine Learning Introduction
Introduction ML (Textbook IML: Chapter 1, Section 1.1 and 1.2)
Research Logistics & Reflection
Supervised (Textbook IML: Chapter 2, Sections 2.1, 2.3, 2.4, 2.5, 2.7, and 2.8)
Bayesian Decision Theory
Bayesian (Textbook IML: Chapter 3, Sections 3.1, 3.2 and 3.6)
Research Reflection Discussion
Research Reflection Discussion
Decision Trees (Contd..)
Research Paper Presentation [Karthik, Pronob]
Critique Writing Guidelines
Research Paper Presentation [Mariam, Saeid, Ahmadur]
Research Paper Presentation [Shreepriya, Leslie, Swamy, Waqas]
Final Research Project Presentation
Final Project Report Writing Guidelines
IEEE Style File
Final Research Project Logistics & Rubrics
Research Paper Presentation Schedule:
In this course we will be discussing various papers recently published in ACM/IEEE conferences such as.... Please check the following papers.
Ferebee, Denise et al. A New Remote Health-Care System Based on Moving Robot Intended for the Elderly at Home, Journal of Healthcare Engineering, Hindawi, 2018 [Swamy Naik Vardthyavath]
Speech Interaction to Control a Hands-Free Delivery Robot for High-Risk Health Care Scenarios, Front. Robot. AI, 08 April 2021 [Karthik Chavali]
Robotics, Smart Wearable Technologies, and Autonomous Intelligent Systems for Healthcare During the COVID-19 Pandemic: An Analysis of the State of the Art and Future Vision, Advanced Intelligent Systems, Wiley July 2020 [Pronob Kumar Barman]
Iris: A Low-Cost Telemedicine Robot to Support Healthcare Safety and Equity During a Pandemic, Pervasive Health 2022 Mariam Yekini
Nisha Raghunath et. al.. Learning-Enabled Robotic Assistive Support: Understanding Older Adult Opinions and Comparing Them to Younger Adult Opinions; Gerontechnology. 2020
Mahmood, S.; Ampadu et. al. Prospects of Robots in Assisted Living Environment. Electronics 2021 [Leslie Leslie]
Garrett Wilson et. al. Robot-enabled support of daily activities in smart home environments, Cognitive Systems Research, Volume 54, 2019 [Shreepriya Dogra]
V. DeFreese et. al. Robotic Solutions for Eldercare, IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019 [Mohammad Saeid Anwar]
Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang, Changhao Chen, Linhai Xie, Mengyu Wang, Niki Trigoni, Andrew Markham: Heart Rate Sensing with a Robot Mounted mmWave Radar. ICRA 2020: 2812-2818 [Ahmadur Rahman]
Nisha Raghunath et. al.. Learning-Enabled Robotic Assistive Support: Understanding Older Adult Opinions and Comparing Them to Younger Adult Opinions; Gerontechnology. 2020 [Waqas Mehmood]
Team No Team Members Topic/Title Devices
Shreepriya & Karthik
May I have my pills please? An Autonomous Medication Delivery System using Robot
Pronob & Mariam
Detect Abnormal Health Conditions in Smart Home using Drone
Ahmadur & Saeid
A Friend In Need is a Friend Indeed. Detecting and Notifying Severe Health Conditions of Older Adults
Leslie, Waqas & Swamy
Autonomous SOS call in case of an Emergency Situation by Robot Pet
Hexapod Robot + Quadruped Robot Dog