IS 698/800: Smart Home Health Analytics — Spring 2017

Times: Monday 4:30pm – 7:00pm
Location: Sherman Hall 011
Instructor: Nirmalya Roy
Instructor’s Office Location and Hours: ITE 421 Monday 3:00 – 4:00pm, or by appointment
Instructor’s Email: nroy at umbc dot edu

Course Webpage: http://mpsc.umbc.edu/is698shhasp17/

Course Descriptions: This course will examine different machine learning methodologies, toolsets, and wearable and ambient systems that are warranted to recognize, discover, and learn the human activities, behaviors, and their profound impact on human health and wellbeing 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 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 details.

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: IS 733 (Data Mining) or 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):

Course Project: Students are required to do a group project (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, smartwatches or any other smart devices for a specific smart environment application as approved by the instructor and leverage 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 a group research project. Each student is also required to write three critiques on three distinct research papers being presented by the fellow students in the class.

Course Requirements and Grading:

Course Participation 5%
Research Reflection 5%
Individual Research Paper Presentation 5%
Critique Writing 5%
Homework (4) 20%
1 Midterm Exam 25%
Group Research Project & Final Project Report 35%

Tentative Course Schedule:
(Subject to change as the semester progresses)

Week Date Topic Handout/Assignment Due Notes
 1 1/30 Course Introduction, Review of Probability & Statistics Course Syllabus Introduction 

Probability Review

 2 2/06 Machine Learning: Introduction

Weka Tutorial

Homework 1 Introduction ML (Textbook IML: Chapter 1, Sections  1.1 and  1.2)

Weka Tutorial

Example Datasets

Research Reflection Sign Up Sheet

 3  2/13 Supervised Learning, Research Logistics & Research Reflection Homework 1 Supervised (Textbook IML: Chapter 2, Sections  2.1,  2.3, 2.4, 2.5, 2.7, and 2.8)

Research Logistics

Research Reflection

 4 2/20 Bayesian Decision Theory, Research Reflection Discussion Presentation Logistics

Homework 2

Bayesian (Textbook IML: Chapter 3, Sections  3.1,  3.2 and 3.6)

 

 5 2/27 Parametric Methods, Research Reflection Discussion Homework 2 Parametric
 6 3/06 Research Reflection Discussion
7 3/13 Conference Travel
8 3/20 Spring Break
9 3/27 Decision Trees Homework 3 Decision Trees
10  4/03 Evaluation

Research Paper Presentation

 

Homework 4

Paper Critique Writing

Homework 3 Evaluation

Yogesh Sharma, Raghavendra Shukla, Gourav Singh

11 4/10 Research Paper Presentation Sensor Data Collection Tutorial Kris Singh, Neha Singh, Lingge Suo, Wenbin Zhang, Abu Zaher Md Faridee, Dan Li, Ann Matejka, Amanda Moskowitz, Geethika MudunuriViraj Raiker
12 4/17 Research Paper Presentation Homework 4 Sreenivasan     RamamurthyCharu Saklecha , Sagun Saru, Deep Shah, Manav Shah, Hardik  Anvekar, Caroline BrunschwylerGokul Devunuri, Arash Fallah
13 4/24 Research Paper Presentation

Exam Review

Dileep  Chappidi, Kaitlin Ricker, Prachi Dharurkar, Hussein Hazazi, Anand Jain, Goram  Alshmrani

Exam Review

14 5/01 Exam
15 5/08 Final Research Project Group Presentation Wenbin (Team 11)
Geethika & Gourav (Team 8)
Caroline & Kaitlin (Team 3)
Charu & Anand (Team 4)
Dan & Lingge (Team 10)
Hussein & Dileep (Team 12)
Raghavendra & Yogesh (Team 14)
16 5/15 Final Research Project Group Presentation Final Project Report Template

Final Project Report Logistic

Sample IEEE Style File

Arash & Gokul (Team 13)
Amanda & Ann (Team 5)
Abu & Sreeni (Team 1)
Manav & Hardik (Team 7)
Kris & Sagun (Team 9)
Viraj & Prachi (Team 15)
Neha & Deep (Team 2)

Course Resources:

Smartphone and Smartwatch Data Collection Apps:

iPhone Users:

Android Users:

Research Paper Presentation Schedule:
(To be determined)

Group No. Team Members Project Title Devices
1 Abu Zaher Md Faridee, Sreenivasan Ramamurthy Comparison study of Dance activities of a Professional to a Learner Microsoft Band, Actigraph
2 Neha Singh, Deep Shah Exploiting the Deep Relationship between Music and Stress for Healthy Living Empatica +Embrace
3 Caroline Brunschwyler, Kaitlin Ricker Personality Driven Fitness Microsoft Band, Samsung Gear
4 Anand Jain, Charu Saklecha Measuring the students’ drinking behavior during the on-campus desk and facility job Empatica
5 Amanda Moskowitz, Ann Matejka The effects of smart device use and TV watching on sleep quality Beddit 3 Sleep Tracker
6 Goram Alshmrani Control Diet: keeping an eye on your diet Hapifork, Microsoft Band
7 Hardik Anvekar, Manav Shah SHOTFinder: Comparing and analyzing the wrist movement to obtain the type of shot played Microsoft Band
8 Geethika Mudunuri, Gourav Singh Monitoring and Mitigating the Bad Habits Pavlok Wristband
9 Kris Singh, Sagun Saru Capturing the Performance Differences in Morning and Evening Workouts Microsoft Band
10 Dan Li, Lingge Suo Does swimming help to reduce the back pain of computer savvy users? Fitbit flex 2
11 Wenbin Zhang Playing Soccer in the Morning or Afternoon Sessions: Does it make a difference in the players’ performance? Microsoft Band
12 Hussein Hazazi, Dileep Chappidi Lakshmana Rekha: Keeping the Movements of the College Students Safe at the University Campuses Smartphones
13 Arash Fallah, Gokul Devunuri Effects of Sleep on Cardiovascular Endurance and Heartbeat Microsoft Band
14 Raghavendra Shukla, Yogesh Sharma How smoking affects your lifestyle? Microsoft Band
15  Viraj Raiker, Prachi Dharurkar Activity Data Collection Apps: Pros and Cons Smartphones

Relevant conferences and workshops in the broad area of  the course material (machine learning, data mining, pervasive and ubiquitous computing, pervasive health, health IT, HCC):

  • IEEE ICDM, ICDE, PerCom, ICML, Wireless Health, EMBC
  • ACM KDD, SenSys, IPSN, Ubicomp, MobiSys, CHI
  • AAAI, Pervasive Health, ICOST, IE, WristSense

Reading List:

Analysis of Long-term Abnormal Behaviors for Early Detection of Cognitive Decline,
Daniele Riboni, Gabriele Civitarese, Claudio Bettini, IEEE PASTA 2016

Elderly Medication Adherence Monitoring with the Internet of Things,
Xiaoping Toh, Hwee-Xian Tan, Huiguang Liang, Hwee Pink Tan, IEEE PASTA 2016

Beacon-Based Multi-Person Activity Monitoring System for Day Care Center 
Kiyoaki Komai, Manato Fujimoto, Yukata Arakawa, Suwa Hirohiko, Yukitoshi Kashimoto,
Keiichi Yasumoto, IEEE PASTA 2016

Improving the Sensitivity of Unobstrusive Inactivity Detection in Sensor-Enabled Homes for the Elderly
Alvin C Valera, Hwee Pink Tan, Liming Bai, IEEE PASTA 2016

On the impact of activity recognition in monitoring cognitive decline
Claudio Bettini, Keynote Talk, IEEE PerCom 2016 workshop

Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition
Allan Stisen et. al., ACM SenSys 2015 (Sreenivasan Ramamurthy)

Connecting Personal-scale Sensing and Networked Community Behavior to Infer Human Activities
Nicholas D. Lane, Li Pengyu, Lin Zhou, Feng Zhao, ACM Ubicomp 2014

SymDetector: detecting sound-related respiratory symptoms using smartphones
Xiao Sun, Zongqing Lu, Wenjie Hu, Guohong Cao, ACM Ubicomp 2015

Evaluating tooth brushing performance with smartphone sound data
Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo Tamagawa; ACM Ubicomp 2015

SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors
Eun Kyoung Choe, Bongshin Lee, Matthew Kay, Wanda Pratt, Julie A. Kientz, ACM Ubicomp 2015 (Lingge Suo)

RunBuddy: a smartphone system for running rhythm monitoring
Tian Hao, Guoliang Xing, Gang Zhou; ACM Ubicomp 2015  (Kaitlin Ricker)

MyoVibe: vibration based wearable muscle activation detection in high mobility exercises
Frank Mokaya, Roland Lucas, Hae Young Noh, Pei Zhang, ACM Ubicomp 2015

DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar
Tauhidur Rahman, Alexander T. Adams, Ruth Vinisha Ravichandran, Mi Zhang, Shwetak N. Patel, Julie A. Kientz, Tanzeem Choudhury; ACM Ubicomp 2015

DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning
Nicholas D. Lane, Petko Georgiev, Lorena Qendro, ACM Ubicomp 2015  (Abu Zaher Md Faridee)

puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation,
Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa al’Absi, Santosh Kumar; ACM Ubicomp 2015

I did not smoke 100 cigarettes today!: avoiding false positives in real-world activity recognition,
Le T. Nguyen, Ming Zeng, Patrick Tague, Joy Zhang; ACM Ubicomp 2015 (Yogesh Sharma)

Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis,
Luca Canzian, Mirco Musolesi; ACM Ubicomp 2015 (Wenbin Zhang)

Invisible Sensing of Vehicle Steering with Smartphones
Dongyao Chen, Kyong-Tak Cho, Sihui Han, Zhizhuo Jin, Kang G. Shin, ACM MobiSys 2015

Recognizing Social Gestures with a Wrist-Worn SmartBand
Jamie Payton, Jonathan S Knighten, Stephen McMillan and Tori Chambers, IEEE WristSense 2015  (Charu Saklecha)

Gesture Control By Wrist Surface Electromyography
Abhishek Nagar and Xu Zhu, IEEE WristSense 2015  (Gourav Singh)

Gait, Wrist and Sensors: Detecting Freezing of Gait in Parkinson’s Disease from Wrist Movement
Sinziana Mazilu, Ulf Blanke, Gerhard Tröster, IEEE WristSense 2015 (Viraj Raikar)

The Case for Smartwatch-based Diet Monitoring
Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh K Balan and Youngki Lee, IEEE WristSense 2015  (Goram Alshmrani)

Towards Detection of Bad Habits by Fusing Smartphone and Smartwatch Sensors
Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans Scholten and Paul Havinga, IEEE WristSense 2015  (Raghavendra Shukla)

Detecting Self-harming Activities with Wearable Devices
Levi Malott, Sriram Chellappan, Pratool Bharti, Nicholas Hilbert, Ganesh Gopalakrishna, IEEE WristSense 2015

Never Skip Leg Day: A Novel Wearable Approach to Monitoring Gym Leg Exercises
Bo Zhou, Mathias Sundholm, Jingyuan Cheng and Heber Cruz, Paul Lukowicz, IEEE PerCom 2016 (Gokul Devunuri)

IRIS: Tapping Wearable Sensing to Capture In-Store Retail Insights on Shoppers
Meeralakshmi Radhakrishnan, Sharanya Eswaran, Archan Misra, Deepthi Chander, Koustuv Dasgupta, IEEE PerCom 2016 (Hussein Hazazi)

MT-Diet: Automated Smartphone based Diet Assessment with Infrared Images
Junghyo Lee, Ayan Banerjee and Sandeep Gupta, IEEE PerCom 2016 (Prachi Dharurkar)

A Probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring
Abdur Rahim Mohammad Forkan, Ibrahim Khalil, IEEE PerCom 2016

HealthyOffice: Mood Recognition At Work Using Smartphones and Wearable Sensors
Alexandros Zenonos, Aftab Khan, Georgios Kalogridis, Stefanos Vatsikas, Tim Lewis, Mahesh Sooriyabandara, IEEE WristSense 2016 (Deep Shah)

From Smart to Deep: Robust Activity Recognition on Smartwatches Using Deep Learning
Sourav Bhattacharya, Nicholas D. Lane, IEEE WristSense 2016

Did You Take a Break Today?: Detecting Playing Foosball Using Your Smartwatch
Sougata Sen et. al., IEEE WristSense 2016 (Dan Li)

weSport: Utilising Wrist-Band Sensing to Detect Player Activities in Basketball Games
Lu Bai, Christos Efstratiou, Chee Siang Ang, IEEE WristSense 2016 (Arash Fallah)

WatchUDrive: Differentiating Drivers and Passengers Using Smartwatches
Alex Mariakakis, Vijay Srinivasan, Kiran Rachuri, Abhishek Mukherji, IEEE WristSense 2016 (Geethika Mudunuri)

Recognizing Text Using Motion Data From a Smartwatch
Luca Arduser, Pascal Bissig, Philipp Brandes, Roger Wattenhofer, IEEE WristSense 2016 (Hardik Anvekar)

Automated detection of activity transitions for prompting
Feuz K., Robertson K., Rosasco C., Cook D. & Schmitter-Edgecombe, M. IEEE Transactions on Human-Machine Systems, 2015

Re-Active Learning: Active Learning With Relabeling
Christopher H. Lin, Mausam, Daniel S. Weld, AAAI 2016

iSleep: unobtrusive sleep quality monitoring using smartphones
Tian Hao, Guoliang Xing, Gang Zhou, ACM SenSys ’13

EmotionCheck: Leveraging Bodily Signals and False Feedback to Regulate our Emotions 
J. Costa, A. T. Adams, M. F. Jung, F. Guimbetiere, and T. Choudhury, ACM UbiComp 2016 (Neha Singh)

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang; ACM MobiCASE 2014

Accurate Caloric Expenditure of Bicyclists using Cellphones
Andong Zhan, Marcus Chang, Yin Chen, Andreas Terzis, ACM SenSys 2012

Analysis of chewing sounds for dietary monitoring 
O. Amft, M. Stager, P. Lukowicz, and G. Troster, UbiComp’05

Virtual reality and mobile phones in the treatment of generalized anxiety disorders: a phase-2 clinical trial
Claudia Repetto et. al., Personal and Ubiquitous Computing 2013  (Ann Matejka)

Deep-Crowd-Label: A Deep-Learning based Crowd-Assisted System for Location Labeling
Mo Mahdi Moazzami, Jasvinder Singh and Vijay Srinivasan, Guoliang Xing, IEEE PerCom 2017 CASPer workshop

Crowd and Event Detection by Fusion of Camera Images and Micro Blogs
Sohei Kojima, Akira Uchiyama, Masumi Shirakawa, Akihito Hiromori, Hirozumi Yamaguchi and Teruo Higashino, IEEE PerCom 2017 CASPer workshop

HeatWatch: Preventing Heatstroke Using a Smart Watch
Takashi Hamatani, Akira Uchiyama and Teruo Higashino, IEEE PerCom 2017 WristSense workshop

Using Wrist-worn Sensors to Measure and Compare Physical Activity Changes for Patients Undergoing Rehabilitation
Jordana Dahmen, Alyssa La Fleur, Gina Sprint and Diane J. Cook, Douglas Weeks, IEEE PerCom 2017 WristSense workshop

UStress: Understanding College Student Subjective Stress Using Wrist-Based Passive Sensing
Begum Egilmez, Emirhan Poyraz, Wenting Zhou, Gokhan Memik, Peter Dinda and Nabil Alshurafa, IEEE PerCom 2017 WristSense workshop (Dileep Chappidi)

A Natural Language Query Interface for Searching Personal Information on Smartwatches
Reza Rawassizadeh, Chelsea Dobbins, Manouchehr Nourizadeh, Zahra Ghamchili, Michael Pazzani, IEEE PerCom 2017 WristSense workshop

Inferring Smartphone KeyPress via Smartwatch Inertial Sensing
Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra, IEEE PerCom 2017 WristSense workshop

SoccerMate: A Personal Soccer Attribute Profiler using Wearables
H M Sajjad Hossain, Md Abdullah Al Hafiz Khan, Nirmalya Roy, IEEE PerCom 2017 BICA workshop (Sagun Saru)

Your Data in Your Hands: Privacy-preserving User Behavior Models for Context Computation
Rahul Murmuria, Angelos Stavrou, Daniel Barbara, Vincent Sritapan, IEEE PerCom 2017 BICA workshop

SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation
Moustafa Alzantot, Supriyo Chakraborty, Mani B. Srivastava, IEEE PerCom 2017 BICA workshop

Smartphone-based Mobile Gunshot Detection
David Welsh and Nirmalya Roy,  IEEE PerCom 2017 CoMoRea workshop

Using Change Point Detection to Automate Daily Activity Segmentation
Samaneh Aminikhanghahi and Diane J. Cook, IEEE PerCom 2017 CoMoRea workshop

A Mobile Lifelogging Platform to Measure Anxiety and Anger During Real-Life Driving
Chelsea Dobbins and Stephen Fairclough, IEEE PerCom 2017 EmotionAware workshop (Amanda Moskowitz)

Towards Using Situational Information to Detect an Individual’s Perceived Stress Level
Svenja Neitzel, Frank Englert, Rahul Dwarakanath, Katharina Schneider, Kathrin Reinke, Gisela Gerlach, Christoph Rensing, Doreen Böhnstedt and Ruth Stock, IEEE PerCom 2017 EmotionAware workshop

From the Lab to the Real-world: an Investigation on the Influence of Human Movement on Emotion Recognition using Physiological signals
Yaqian Xu, Isabel F Hübener, Ann-Kathrin Seipp, Sandra Ohly and Klaus David, IEEE PerCom 2017 EmotionAware workshop  (Anand Jain)

EmoBGM: estimating sound’s emotion for creating slideshows with suitable BGM
N’djabli cedric ange Konan, Hirohiko Suwa; Yutaka Arakawa, Keiichi Yasumoto, IEEE PerCom 2017 EmotionAware workshop

Interruptibility Map: Geographical Analysis of Users’ Interruptibility in Smart Cities
Mikio Obuchi, Tadashi Okoshi, Takuro Yonezawa, Jin Nakazawa and Hideyuki Tokuda, IEEE PerCom 2017 EmotionAware workshop

EyeAssist: A Communication Aid through Gaze Tracking for Patients with Neuro-Motor Disabilities
Anwesha Khasnobish, Rahul Gavas, Debatri Chatterjee, Ved Raj and Sapna Naitam, IEEE PerCom 2017 PerHealth workshop

Monitoring Objects Manipulations to Detect Abnormal Behaviors
Gabriele Civitarese and Claudio Bettini, IEEE PerCom 2017 PerHealth workshop

SmartCare: an Introduction
Gergely Zaruba, Manfred Huber, Nicholas Burns, Kathryn Daniel, IEEE PerCom 2017 PerHealth workshop (Manav Shah)

SwallowNet: Recurrent Neural Network Detects and Characterizes Eating Patterns
Dzung Tri Nguyen, Nabil Alshurafa and Eli Cohen, IEEE PerCom 2017 PerHealth workshop

Investigating Barriers and Facilitators to Wearable Adherence in Fine-Grained Eating Detection
Rawan Alharbi, Nilofar Vafaie, Kitty Liu, Kevin Moran, Gwendolyn Ledford, Angela Pfammatter, Bonnie Spring and Nabil Alshurafa, IEEE PerCom 2017 PerHealth workshop

Swallowing Detection for Game Control: Using Skin-Like Electronics to Support People with Dysphagia
Benjamin Nicholls, Yongkuk Lee, Woon Yeo, Chee Ang and Christos Efstratiou, IEEE PerCom 2017 PerHealth workshop

Smart Cushion: A Practical System for Fine-grained Sitting Posture Recognition
Guanqing Liang, Jiannong Cao, Xuefeng Liu, IEEE PerCom 2017 PerHealth workshop

Measuring Changes in Gait and Vehicle Transfer Ability During Inpatient Rehabilitation with Wearable Inertial Sensors
Gina Sprint, Vladimir Borisov, Diane J. Cook, Douglas Weeks, IEEE PerCom 2017 PerHealth workshop

ActivityAware: An App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device
George Boateng, Ryan Halter, John Batsis, David Kotz, IEEE PerCom 2017 PerHealth workshop

RFexpress! – RF Emotion Recognition in the Wild
Muneeba Raja, Stephan Sigg, IEEE perCom 2017 Work-in-progress

Challenges of Collecting Empirical Sensor Data from People with Dementia in a Field Study
Albert Hein, Frank Krüger, Sebastian Bader, Peter Eschholz and Thomas Kirste, IEEE perCom 2017 Work-in-progress

FamilyLog: A Mobile System for Monitoring Family Mealtime Activities
Chongguang Bi and Guoliang Xing, Hao Tian, Jina Huh, Wei Peng and Mengyan Ma, IEEE PerCom 2017

Deep Learning Parkinson’s from Smartphone Data
Cosmin Stamate, George Magoulas, Stefan Kueppers, Effrosyni Nomikou, Ioannis Daskalopoulos, Marco Luchini, Theano Moussouri, George Roussos, IEEE PerCom 2017  (Kris Singh)

Online Personalization of Cross-Subjects based Activity Recognition Models on Wearable Devices
Timo Sztyler and Heiner Stuckenschmidt, IEEE PerCom 2017  (Caroline Brunschwyler)

Preclude: Conflict Detection in Textual Health Advice
Sarah Masud Preum, Md Abu Sayeed Mondol, Meiyi Ma, Hongning Wang and John Stankovic, IEEE PerCom 2017

Just In Case: Diminished mental health can interfere with optimal academic performance. The source of symptoms might be related to your course work; if so, please speak with me. However, problems with other parts of your life can also contribute to decreased academic performance. UMBC provides cost-free and confidential mental health services through the Counseling Center to help you manage personal challenges that threaten your personal or academic well-being.

Remember, getting help is a smart and courageous thing to do — for yourself and for those who care about you. For more resources get the Just in Case mental health resources Mobile and Web App. This app can be accessed by clicking http://counseling.umbc.edu/justincase

The UMBC Counseling Center is in the Student Development & Success Center (between Chesapeake and Susquehanna Halls). Phone: 410-455-2472. Hours: Monday-Friday 8:30am-5:00pm.

Student Support Services: UMBC is committed to eliminating discriminatory obstacles that disadvantage students based on disability. Student Support Services (SSS) is the UMBC department designated to receive and maintain confidential files of disability-related documentation, certify eligibility for services, determine reasonable accommodations, develop with each student plans for the provision of such accommodations, and serve as a liaison between faculty members and students regarding disability-related issues.  If you have a disability and want to request accommodations, contact SSS in the Math/Psych Bldg., room 213 or at 410-455-2459.  SSS will require you to provide appropriate documentation of disability.  If you require accommodations for this class, make an appointment to meet with me to discuss your SSS-approved accommodations.

Academic Integrity: Cheating in any form, will be subject to discipline according to university regulations. Projects that contain plagiarized materials will receive an automatic letter grade of ‘F’. Multiple violations will be handled according to university regulation. Please refer to Academic Integrity for more information.

Acknowledgement: Prof. Diane Cook and Prof. Larry Holder