Embedded Files
Times: Monday 7:10pm – 9:40pm
Location: Information Technology 229
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.
Course Topics:
Gerontechnology
Supervised Learning
Bayesian Decision Theory
Parametric Methods
Decision Theory
Machine Learning Evaluation
Machine Learning Toolkits
Activity Learning
Smart Home Health Technologies
Functional and Behavioral Health Assessment
Qualitative and Quantitative Clinical Health Assessment Tools
Applications
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.
Week
Date
Topic
Handout/Assignment
Due
Notes
1
1/30
Course Introduction
Review of Probability & Statistics
Introduction
Probability Review
2
2/6
Machine Learning Introduction
Weka Tutorial
Homework 1
Introduction ML (Textbook IML: Chapter 1, Section 1.1 and 1.2)
Weka Tutorial
Example Datasets
3
2/13
Supervised Learning
Research Logistics & Reflection
HW1
Supervised (Textbook IML: Chapter 2, Sections 2.1, 2.3, 2.4, 2.5, 2.7, and 2.8)
Research Logistics
Research Reflection
Research Reflection Sign Up Sheet
4
2/20
Bayesian Decision Theory
Research Reflection Discussion
Homework 2
Bayesian (Textbook IML: Chapter 3, Sections 3.1, 3.2 and 3.6)
5
2/27
Parametric Methods
Research Reflection Discussion
HW2
Parametric
6
3/6
Decision Trees
Research Reflection Discussion
Decision Trees
7
3/13
Guest Lecture: Tutorial on Data Collection from IoT Devices
Data Collection and Analysis
8
3/20
Spring Break
9
3/27
Decision Trees (Contd..)
Homework 3
Decision Trees
10
4/3
Evalaution
Research Paper Presentation [Azim Khan]
Research Proposal
Research Proposal
HW 3
Evaluation
Critique Writing Guidelines
11
4/10
Evaluation
Research Paper Presentation [Nathan Chen, Aksha Matharu, Sultan Ahmed, Mohammadaamir Tirmizi]
12
4/17
Evaluation
Research Paper Presentation [Al Amin, Dhyani Chokshi, Ashish Bhargav Gampa, Prathamesh Keluskar, Medha Kunnath]
Homework 4
13
4/24
Research Paper Presentation
HW 4
14
5/1
Project Pitch
Exam Review
Exam Review
15
5/8
Exam
16
5/15
Final Research Project Presentation
Final Project Report Writing Guidelines
IEEE Style File
Final Research Project Logistics & Rubrics
Research Paper Presentation Schedule:
Research Paper Presentation Schedule:
See above
Research Papers:
Research Papers:
In this course we will be discussing various papers recently published in ACM/IEEE conferences. Please check the following papers.
The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example, MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)
CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry, IEEE PerCom 2022 [Nathan Chen]
AiEEG: Personalized Seizure Prediction Through Partially-Reconfigurable Deep Neural Networks, IEEE PerCom 2022
LuckyChirp: Opportunistic Respiration Sensing Using Cascaded Sonar on Commodity Devices, IEEE PerCom 2022
SmartPhOx: Smartphone-Based Pulse Oximetry Using a Meta-Region Of Interest, IEEE PerCom 2022
FedCLAR: Federated Clustering for Personalized Sensor-Based Human Activity Recognition, IEEE PerCom 2022
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition, IEEE PerCom 2022
EarHealth: An Earphone-based Acoustic Otoscope for Detection of Multiple Ear Diseases in Daily Life, ACM MobiSys 2022 [Prathamesh Keluskar]
SkinProfiler: low-cost 3D scanner for skin health monitoring with mobile devices, ACM MobiSys 2022 workshop [Ashish Bhargav Gampa]
A smart thermostat-based population-level behavioural changes during the COVID-19 pandemic in the United States: a proposed study, ACM MobiSys 2022 workshop [Mohammadaamir Tirmizi]
A multi-sensor deep learning approach for complex daily living activity recognition, ACM MobiSys 2022 workshop
A novel mobile platform for stress prediction: application, protocol and preliminary results, ACM MobiSys 2022 workshop [Dhyani Chokshi]
Monitoring neurological disorders with AI-enabled wearable systems, ACM MobiSys 2022 workshop
Digital biomarkers reflect stress reduction after augmented reality guided meditation: a feasibility study, ACM MobiSys 2022 workshop
Depressive Bangla Text Detection from Social Media Post Using Different Data Mining Techniques, Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer [Sultan Ahmed]
Human-Centered Design of Mobile Health Apps for Older Adults: Systematic Review and Narrative Synthesis, JMIR Mhealth Uhealth. 2022 [Aksha Matharu]
CamSense: A Camera-Based Contact-less Heart Activity Monitoring IEEE/ACM CHASE 2021 [Azim Khan]
Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use Sensors 2019, MDPI [Al Amin]
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
Speech Interaction to Control a Hands-Free Delivery Robot for High-Risk Health Care Scenarios, Front. Robot. AI, 08 April 2021
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
Iris: A Low-Cost Telemedicine Robot to Support Healthcare Safety and Equity During a Pandemic, Pervasive Health 2022
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
Garrett Wilson et. al. Robot-enabled support of daily activities in smart home environments, Cognitive Systems Research, Volume 54, 2019
V. DeFreese et. al. Robotic Solutions for Eldercare, IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019
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
Nisha Raghunath et. al.. Learning-Enabled Robotic Assistive Support: Understanding Older Adult Opinions and Comparing Them to Younger Adult Opinions; Gerontechnology. 2020
Research Projects:
Research Projects:
Team No Team Members Topic/Title Devices
1
Nathan Chen
Knee Injury Prevention Using Accelerometry
MyoWare Muscle Sensor Development Kit & WIT Motion accelerometer kit
2
Aksha Matharu
The Usability of Mobile Apps for Older Adults
3
Sultan Ahmed
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
4
Mohammadaamir Tirmizi & Dhyani Chokshi
Using Machine Learning to Identify Home Appliances Consuming Excessive Power
5
Al Amin
The Impact of Internet Gaming Disorder (IGD) on University Students' Analyzed through Data Mining Techniques
6
Medha Kunnath
Monitoring and Preventing Eating Disorders Using IoTs
7
Ashish Bhargav Gampa & Prathamesh Keluskar
Prediction of Smoking Status
8
Azim Khan
A Non-Intrusive Contact-less Respiratory Rate Measurement System
Vernier Go Direct Respiration Belt
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