Ph.D Research Assistant Positions Available

There are openings for two Ph.D. students for the 2016-2017 academic year. Regardless of the background, I am looking for students who are crazy about creating things to change the world!! Before sending me an email, please check the following list.

  1. Are you a kind, humble person who is enthusiastic about technology? (i.e. are you a kind geek?)
  2. Do you enjoy making cool gadgets? (e.g., IoT gadgets, LEGOs, etc.)
  3. Do you enjoy learning new things? (e.g., new hardware, software, algorithm, etc.)
  4. Are you a team player? (I mean it. Are you willing to sacrifice your time for other team members?)
  5. Do you believe that you can help others (patients) with technology?
  6. Do you like math or statistics? (Well, let me be honest. Are you GOOD at math or statistics?)
  7. Do you like coding? (C, C#, Java) or scripting? (Python or MATLAB)
  8. Do you have experiences working with embedded systems like TI MSP430, Arduino, Raspberry Pi, or anything similar?
  9. Do you have experiences in mobile software engineering for embedded systems, Android, iPhones, etc.?
  10. Do you have experiences in signal processing, machine learning, or any data analytic algorithms?
If you have at least 7 items to which you can answer YES, please send an email to Professor Lee (silee {at} cs {dot} umass {dot} edu) with your CV, transcript, writing samples (e.g., academic papers), and any supplementary materials that may support your enthusiasm.

NEWS

I am serving as a Guest Editor of the Special Issue of MDPI Applied Sciences on "Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering". Please consider to contribute!

      CALL FOR PAPERS, Deadline: September 30 2017.

Last Updated on 7/23/2017

Research Interest & Biography

 

Hello. I am Sunghoon ivan Lee (I usually go by Ivan), and I am an Assistant Professor in the College of Information and Computer Science at UMass Amherst. I am pursuing end-to-end research in the field of Mobile & Personalized Health, specializing in Physical & Rehab Medicine based on wearable sensors and data analytic methodologies in order to understand the health conditions associated with neurological, neuromuscular, or muscular skeleton disorders such as stroke, Parkinson’s disease, or osteoarthritis. With a primary focus on evolution, my research interests lie in 1) designing and implementing novel sensors and remote monitoring systems that are motivated by practical medical needs, 2) constructing appropriate clinical trials, and 3) analyzing the obtained data to quantify patients’ conditions and validate the systems' clinical efficacy.


Harvard Medical School, Charlestown, MA, USA
(Advisor: Paolo Bonato)
University of California (UCLA), Los Angeles, CA, USA
(Advisor: Majid Sarrafzadeh)
University of California (UCLA), Los Angeles, CA, USA
University of California (UCLA), Los Angeles, CA, USA
Simon Fraser University, Burnaby, BC, Canada
(Advisor: Ivan Bajic)


Postdoctoral Research Fellowship

Ph.D. in Computer Science
with Outstanding Research Award
M.S. in Computer Science
M.S. in Electrical Engineering
B.A.Sc. in Computer Engineering
with Honors


2016

2014

2013
2010
2008

DOWNLOAD CV - last updated on 9/25/2017.

Projects

 

Development of a Novel Flexible Wearable Sensing Solution for Estimating Joint-Angles

To circumvent current limitations of wearable sensors that can be used to assess and monitor joint movements, we developed an accurate, low-cost, flexible wearable sensor comprising a retractable reel, a string, and a potentiometer. This sensor is intended to estimate joint angles in correlation with the amount of skin stretch measured by the change in the length of the string. We achieved an average root mean square error of 4.51 degrees against an gold standard optoelectronic system in 9 healthy individuals. This work demonstrates the accuracy of the proposed system in estimating knee joint angles and provides the basis to develop more complex systems to assess and monitor joints having more degrees of freedom. We believe that our novel low-cost wearable sensing technology has great potential to enable joint kinematic monitoring in ambulatory settings.

 

Remotely Tracking Longitudinal Changes in Movement Quality in Stroke and Traumatic Brain Injury Survivors using Wearable Sensors

Stroke and traumatic brain injury (TBI) significantly affects patients’ sensory and motor functions. These undergo extensive rehabilitation interventions to regain motor abilities. Thus, frequent assessments using accurate tools that can quantify movement quality are in great need. This project investigates the suitability of methods based on data collected using body-worn accelerometer sensors to detect changes in motor abilities in stroke and TBI survivors undergoing rehabilitation. A total of 21 stroke survivors and 23 TBI subjects were recruited for pre- and post-intervention visits. At each visit, they performed a set of eight motor tasks taken from the Wolf Motor Function Test (WMFT). These tasks were observed and evaluated for movement quality by two clinicians using the Functional Ability Scale (FAS). We are currently implementing a machine-learning based algorithm that estimates the FAS scores, aimed at deploying this system at patients' home settings.

 

Assessing the Severity of Levodopa-Induced Dyskinesia in Parkinsonian Patients using Wearable Sensors

Patients with Parkinson’s disease often experience significant changes in the severity of dyskinesia when they undergo titration of their medications. Dyskinesia is marked by involuntary jerking movements that occur randomly in a burst-like fashion. Clinical observations are generally made over intervals of 15-30 s. On the other hand, techniques designed to estimate the severity of dyskinesia based on the analysis of wearable sensor data typically use data segments of approximately 5 s. Consequently, some data segments might include dyskinetic movements, whereas others might not. Herein, we propose a novel method suitable to automatically select data segments from the training dataset that are marked by dyskinetic movements. Results obtained from the analysis of sensor data collected from seven subjects with Parkinson’s disease showed a marked improvement in the accuracy of the estimation of clinical scores of dyskinesia.

 

KROMM: Knee Range of Motion Monitoring System for Osteoarthritis Patients

Monitoring knee function during walking to provide feedback to patients and clinicians could improve post-discharge care and decrease the chances of further injury. This project introduces a wearable system for long-term monitoring of knee kinematics in the home and community settings. The Knee Range of Motion Monitoring System (KROMM) consists of a knee sleeve with embedded sensors to track knee flex/extension movements and daily activities and to monitor adherence of the users. We conducted biomechanical tests with healthy subjects that compares the knee kinematics gathered from the wearable system (KROMM) to a stereophotogrammetric system (Vicon), which showed promising results. Further investigations are currently being made to validate the efficacy of the system.

 

A Prediction Model for Functional Outcomes using Gaussian Process Regression

Predicting the functional outcomes of spinal cord disorder patients after medical treatments, such as a surgical operation, has always been of great interest. This project introduces a prediction method for postoperative functional outcomes (e.g., patient-reported questionnaires) by a novel use of Gaussian process regression. The proposed method specifically considers the restricted value range of the target variables (e.g., possible range of [0, 1]) by modeling the Gaussian process based on a truncated Normal distribution, which significantly improves the prediction results. The proposed method has been validated through a dataset collected from a clinical cohort pilot involving 15 patients with cervical spinal cord disorder. The results show that the proposed method can accurately predict postoperative functional outcomes, Oswestry Disability Index and target tracking scores, based on the patient’s preoperative information with a mean absolute error of 0.079 and 0.014 (out of 1.0), respectively.

 

Wireless Technology for Motor Assessment in Patients with Lumbar Spine Injury

Lumbar disc degeneration is a ubiquitous and debilitating condition which may significantly compromise local neurological and motor function. Current methods of assessment of motor function involve testing of strength, observation of changes in muscle movement, and rating of daily function. This study aims to provide a more objective and quantitative method for assessing motor improvement after lumbar spine surgery. This device could be used to assess and predict recovery after surgery. A wireless gait device was created for this purpose, which is composed of six pressure sensors, a 3-axis accelerometer, and a 3-axis gyroscope. The study attempts to develop and assess the ability of noninvasive digital technology to sensitively record patients’ recovery and improvement.

 

A Low-Cost Quantitative Assessment Framework for Patient with Hand Movement Disorders

Existing methods to measure upper extremity performance are highly subjective. We utilize a novel digital device to objectively quantify the handgrip dexterity of patients with cervical spondylotic myelopathy (CSM). We aim to verify the efficacy of the proposed system that its metric can (i) distinguish patients from the controlled subjects and (ii) assess changes in motor function pre- and post-operatively following surgical decompression. This study involves 7 patients (mean age 62.3 ± 13.1) and 28 healthy subjects (mean age 55.6 ± 8.0). The mean classification accuracy between the preoperative patient data and controlled data was 92.01%. The patient's postoperative data was categorized into functional and non-functional group based on their ODI. The proposed metric achieved 100% accuracy in classifying these postoperative data. Moreover, a linear relationship between the postoperative metric and the ODI was observed (p = 0.087).

 

Remote Health Monitoring Systems: What Impact Can Data Analytics Have on Cost?

While significant effort has been made on designing Remote Monitoring Systems (RMS), limited research has been conducted on the potential cost savings that these systems offer in terms of reduction in readmission costs, as well as the costs associated with human resources involved in the intervention process. This project explores potential cost savings that an analytics engine can provide in presence of intelligent back-end data processing and machine learning algorithms against conventional RMS that operate based on simple thresholding approaches. Using physiological data collected from 486 heart failure patients through a clinical study in collaboration with the UCLA School of Medicine, we conduct retrospective data analysis to estimate prediction accuracy as well as associated costs of the two remote monitoring approaches. Our results show that analytics-based RMS can reduce false negative rates by 61.4% while maintaining a false positive performance close to that of conventional RMS. Furthermore, the proposed analytics engine achieves 61.5% reduction in the overall readmission costs.

 

Publications

 

Journal Publications

[J14] Xin Liu, Smia Rajan, Nathan Ramasarma, Paolo Bonato, Sunghoon Ivan Lee, "The Use of A Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings," IEEE Journal of Biomedical and Health Informatics (J-BHI), To be submitted

[J13] Sunghoon Ivan Lee, Catherin P. Adans-Dester, Matteo Grimaldi, Ariel V. Dowling, Peter C. Horak, Randie M. Black-Schaffer, Paolo Bonato, Joseph T. Gwin, "Wrist-worn Movement Sensors for At-home Rehabilitation of the Hemiparetic Upper-Extremity," IEEE Journal of Translational Engineering in Health and Medicine (J-TEHM), Submitted for review

[J12] Bjoern M. Eskofier, Sunghoon Ivan Lee, Manuela Baron, André Simon, Christine F. Martindale, Heiko Gaßner and Jochen KluckenAn, "Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring," Applied Sciences (Appl. Sci.), vol. 7, no. 986, 2017. [pdf]

[J11] Sunghoon Ivan Lee, Andrew Campion, Alex Huang, Jordan Hayward Garst, Nima Jahanforouz, Marie Espinal, Tiffany Siero, Sophie Pollack, Marwa Afridi, Meelod Daneshvar, Saif Ghias, Majid Sarrafzadeh, Daniel C. Lu, "Finding Predictors for Postoperative Clinical Outcome in Lumbar Spinal Stenosis Patients using Smart-shoe Technology," Journal of NeuroEngineering and Rehabilitation (JNER), vol. 14, no. 77, 2017 [pdf]

[J10] Eunjeong Park, Sunghoon Ivan Lee, HS Nam, Jordan H. Garst, Alex Huang, Andrew Campion, M. Arnell, Nima Ghalehsariand, Monica Arnell, Sangsoo Park, Hyuk-jae Chang, Daniel C. Lu, and Majid Sarrafzadeh, "Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes," Methods of Information in Medicine (Methods Inf Med), vol. 56, no. 1, 2017. [pdf]

[J9] Jean-Francois Daneault, Gloria Vergara-Diaz, Sunghoon Ivan Lee, "Clinical Management of Drug-Induced Dyskinesia in Parkinson’s Disease: Why Current Approaches May Need to be Changed to Optimize Quality of Life," European Medical Journal Reviews (EMJ-R), vol. 1, no. 4, pp 62-69, 2016 [pdf]

[J8] Sunghoon Ivan Lee, Charles Li, Haydn A. Hoffman, Derek S. Lu, Ruth Getachew, Bobak Mortazavi, Jordan H. Garst, Marie Espinal, Mehrdad Razaghy, Nima Ghalehsari, Brian H. Paak, Amir A. Chavam, Marwa Afridi, Arsha Ostowari, Hassan Ghasemzadeh, Daniel C. Lu, Majid Sarrafzadeh, "Quantitative Assessment of Hand Motor Function in Cervical Spinal Disorder Patients Using Target Tracking Tests," Journal of Rehabilitation Research & Development (J-RRD), vol. 54, no. 6, 2016 [pdf]

[J7] Sunghoon Ivan Lee, Eunjeong Park, Alex Huang, Bobak Mortazavi, Jordan Hayward Garst, Nima Jahanforouz, Marie Espinal, Tiffany Siero, Sophie Pollack, Marwa Afridi, Meelod Daneshvar, Saif Ghias, Daniel C. Lu, Majid Sarrafzadeh, "Objectively Quantifying Walking Ability in Degenerative Spinal Disorder Patients using Sensor Equipped Smart Shoes," Medical Engineering & Physics (Med Eng Phys), vol. 38, no. 5, pp 442-449, 2016. [pdf]

[J6] Bobak Mortazavi, Mohammad Pourhomayoun, Sunghoon Ivan Lee, Suneil Nyamathi, Brandon Wu, Majid Sarrafzadeh, "User-Optimized Activity Recognition for Exergaming, " Pervasive and Mobile Computing (PMC), vol. 26, 2016.[pdf]

[J5] Sunghoon Ivan Lee, Bobak Mortazavi, Haydn A. Hoffman, Derek S. Lu, Charles Li, Brian H. Paak, Jordan H. Garst, Mehrdad Razaghy, Marie Espinal, Eunjeong park, Daniel C. Lu, Majid Sarrafzadeh, "A Prediction Model for Functional Outcomes in Spinal Cord Disorder Patients using Gaussian Process Regression," IEEE Journal of Biomedical and Health Informatics (J-BHI), vol. 20, no. 1, 2016. [Nominated for the Cover Article of the Issue] [pdf]

[J4] Haydn A. Hoffman, Sunghoon Ivan Lee, Jordan H. Garst, Derek S. Lu, Charles Li, Daniel T. Nagasawa, Nima Ghalehsari, Nima Jahanforouz, Mehrdad Razaghy, Marie Espinal, Amir Ghavamrezaii, Brian H. Paak, Majid Sarrafzadeh, Daniel C. Lu, "Use of Multivariate Linear Regression and Support Vector Regression to Predict Functional Outcome After Surgery for Cervical Spondylotic Myelopathy," Journal of Clinical Neuroscience (J-Clin Neurosci), vol. 22, no. 9, 2015 [pdf]

[J3] Ruth Getachew, Sunghoon Ivan Lee, Andrew Yew, Jon Kimball, Derek S. Lu, Jordan H. Garst, Nima Ghalehsari, Brian H. Paak, Mehrdad Razaghy, Marie Espinal, Arha Ostowari, Amir Ghavamrezaii, Sahar Pourtaheri, Majid Sarrafzadeh, Daniel C. Lu, “Utilization of A Novel Digital Measurement Tool for Quantitative Assessment of Upper Extremity Motor Dexterity: A Controlled Pilot Study,” Journal of NeuroEngineering and Rehabilitation (J-NER), vol. 11, no. 121, 2014. [pdf]

[J2] Bobak Jack Mortazavi, Suneil Nyamathy, Sunghoon Ivan Lee, T. Wilkerson, Hassan Ghasemzadeh, Majid Sarrafzadeh, "Near-Realistic Mobile Exergames with Wireless Wearable Sensors," IEEE Journal of Biomedical and Health Informatics (J-BHI), vol. 18, no. 2, pp. 449-456, 2014. [Featured Article of the Issue] [pdf]

[J1] Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Jack Mortazavi, Majid Sarrafzadeh, "Pervasive Assessment of Motor Function: A Lightweight Grip Strength Tracking System," IEEE Journal of Biomedical and Health Informatics (J-BHI), vol. 17, no. 6, pp. 1023-1030, 2013. [pdf]

 

Conference and Workshop Publications

[C16] Bjoern M. Eskofier, Member, Sunghoon Ivan Lee, Jean-Francois Daneault, Fatemeh N. Golabchi, Gabriela Ferreira-Carvalho, Gloria Vergara-Diaz, Stefano Sapienza, Gianluca Costante, Thomas Kautz, Jochen Klucken, Paolo Bonato, "Recent Machine Learning Advancements in Sensor-based Mobility Analysis: Deep learning for Parkinson's Disease Assessment," 2016 IEEE Engineering in Medicine and Biology Conference (IEEE EMBC'16), Orlando, USA, August, 2016 [pdf]

[C15] Sunghoon Ivan Lee, Jean-Francois Daneault, Luc Weydert, Paolo Bonato, "A Novel Flexible Wearable Sensor for Estimating Joint-Angles," 2016 IEEE Body Sensor Network Conference (IEEE BSN'16), San Francisco, USA, June, 2016 [pdf]

[C14] Sunghoon Ivan Lee, Jean-Francois Daneault, Fatemeh Noushin Golabchi, Shyamal Patel, Ludy Shih, Paolo Bonato, "A Novel Technique for Assessing the Level of Drug-induced Dyskinesia using Wearable Sensors," 2015 IEEE Engineering in Medicine and Biology Conference (IEEE EMBC'15), Milano, Italy, August, 2015, [pdf]

[C13] Sunghoon Ivan Lee, Muzaffer Yalgin Ozsecen, Luca Della Toffola, Jean-Francois Daneault, Alessandro Puiatti, Shyamal Patel, Paolo Bonato, "Activity Detection in Uncontrolled Free-living Conditions Using a Single Accelerometer," 2015 IEEE Body Sensor Network Conference (IEEE BSN'15), Cambridge, USA, June, 2015 [pdf]

[C12] Bobak Mortazavi, Mohammad Pourhomayoun, Suneil Nyamathi, Brandon Wu, Sunghoon Ivan Lee, Majid Sarrafzadeh, "Multiple Model Recognition for Near-Realistic Exergaming," IEEE PerCom, St. Louis, USA, March, 2015 [pdf]

[C11] Bobak Mortazavi, Mohammad Pourhomayoun, Nabil Alshurafa, Michael Chronley, Sunghoon Ivan Lee, Christian Roberts, Majid Sarrafzadeh, "Support Vector Regression for METs of Exergaming Actions," IEEE Healthcare Innovation Point-of-Care Technologies Conference (HIC-POCT'14), Seattle, USA, October, 2014, [pdf]

[C10] Bobak Mortazavi, Sunghoon Ivan Lee, Majid Sarrafzadeh, "User-centric exergaming with fine-grain activity recognition: a dynamic optimization approach," ACM UbiComp International Workshop on Smart Health Systems and Applications (SmartHealthSys'14), Seattle, USA, September, 2014 [pdf]

[C9] Bobak Mortazavi, Mohammad Pourhomayoun, Gabriel Alsheikh, Nabil Alshurafa, Sunghoon Ivan Lee, Majid Sarrafzadeh, "Determining the Single Best Axis for Exercise Repetition Recognition and Counting with SmartWatches," 2014 IEEE Body Sensor Network Conference (IEEE BSN'14), Zurich, Switzerland, June, 2014 [pdf]

[C8] Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Jack Mortazavi, Mars Lan, Michael Ong, Majid Sarrafzadeh, "Remote Patient Monitoring Systems: What Impact Can Data Analytics Have on Cost?," ACM Wireless Health 2013 (WH2013), Baltimore, USA, November, 2013. [pdf]

[C7] Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Jack Mortazavi, Andrew Yew, Ruth Getachew, Mehrdad Razaghy, Nima Ghalehsari, Brian H. Paak, Jordan H. Garst, Marie Espinal, Jon Kimball, Daniel C. Lu, Majid Sarrafzadeh, "Objective Assessment of Overexcited Hand Movements using a Lightweight Sensory Device," 2013 IEEE Body Sensor Network Conference (IEEE BSN'13), MIT, USA, May, 2013. [pdf]

[C6] Bobak Mortazavi, Nabil Alsharufa, Sunghoon Ivan Lee, Mars Lan, Michael Chronley, Christian K. Roberts, Majid Sarrafzadeh, "MET Calculations from On-Body Accelerometers for Exergaming Movements," 2013 IEEE Body Sensor Network Conference (IEEE BSN'13), MIT, USA, May, 2013. [pdf]

[C5] Haik Kalantarian, Sunghoon Ivan Lee, Anurag Mishra, Hassan Ghasemzadeh, Majid Sarrafzadeh, "Multimodal Energy Expenditure Calculation for Pervasive Health: A Data Fusion Model using Wearable Sensors," Proceedings of IEEE PerCom Workshop on Smart Environment and Ambient Intelligence, San Diego, California, March, 2013. [pdf]

[C4] Taiwoo Park, Inseok Hwan, Uichin Lee, Sunghoon Ivan Lee, Chungkuk Yoo, H. Jang, S. Choe, S. Park, Junehwa Song, "ExerLink: Enabling Pervasive Social Exergames with Heterogeneous Exercise Devices" ACM MobiSys 2012 , Lake District, UK, June, 2012. [pdf]
Acceptance Rate: 17.5% (32/182)

[C3] Sunghoon Ivan Lee, Jonathan Woodbridge, Ani Nahapetian, Majid Sarrafzadeh, "MARHS: Mobility Assessment System with Remote Healthcare Functionality for Movement Disorders," ACM SIGHIT International Health Informatics Symposium 2012 (SIGHIT IHI 2012), Miami, USA, January, 2012. [pdf] [ppt]
Acceptance Rate: 17.8% (48/269)

[C2] Sunghoon Ivan Lee, Charles Ling, Ani Nahapetian, Majid Sarrafzadeh, "A Mechanism for Data Quality Estimation of On-Body Cardiac Sensor Networks," in
Proc. IEEE Concumer Communication and Networking Conference 2012 (CCNC 2012), Las Vegas, Nevada, USA, January 2012. [pdf] [ppt]

[C1] Sunghoon Ivan Lee, Hyunggon Park, and Mihaela van der Schaar, "Foresighted Joint Resource Reciprocation and Scheduling Strategies for Real-time Video Streaming over Peer-to-Peer Networks" Int. Packet Video Workshop 2009 (PV 2009), May 2009. [pdf]

 

Abstracts, Posters, and Demos

[A21] Xin Liu, Smita Rajan, Gabriel Hollander, Nathan Ramasarma, Paolo Bonato, Sunghoon Ivan Lee, "A Novel Finger-Worn Sensor for Ambulatory Monitoring of Hand Use," The 2nd IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies (IEEE/ACM CHASE 2017), Philadelphia, July, 2017.

[A20] Jean-Francois Daneault, Sunghoon Ivan Lee, Fatemeh Golabchi, Shyamal Patel, Ludy Shih, Sabrina Pagnoni, Paolo Bonato, "Estimating Bradykinesia in Parkinson's Disease with a Minimum Number of Wearable Sensors," The 2nd IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies (IEEE/ACM CHASE 2017), Philadelphia, July, 2017.

[A19] Stefano Sapienza, Catherine Adans-Dester, Anne O'Brien, Gloria Vergara Diaz, Sunghoon Ivan Lee, Shyamal Patel, Randie Black-Schaffer, Ross Zafonte, Paolo Bonato, Claire MEagher, Anne-Marie Hughes, Jane Burridge, Danilo Demarchi, "Using a Minimum Set of Wearable Sensors to Assess Quality of Movement in Stroke Survivors," The 2nd IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies (IEEE/ACM CHASE 2017), Philadelphia, July, 2017.

[A18] Smita Rajan, Xin Liu, Gabriel Hollander, Nathan Ramasarma, Paolo Bonato, Sunghoon Ivan Lee, "A Finger-Worn Ring Sensor to Capture Hand Movements in an Ambulatory Setting," 94th Annual Conference of American Congress of Rehabilitation Medicine (ACRM), Atlanta, USA, Oct, 2017.

[A17] Catherine Adans-Dester, Paolo Bonato, Ariel Dowling, Joseph Gwin, Sunghoon Ivan Lee, Anne O'Brian, "Wrist-Worn Sensors for Tele-Rehabilitation of the Hemiparetic Upper-Extremity: Stakeholder Interviews for Feedback and Usability," 94th Annual Conference of American Congress of Rehabilitation Medicine (ACRM), Atlanta, USA, Oct, 2017.

[A16] Gianluca Costante, Edoardo Bonizzoni, Alessandro Puiatti, Sunghoon Ivan Lee, Paolo Bonato, "MercuryLive 4.0 - A Web Platform to Enable Remote Monitoring of Patients with Parkinson’s Disease Using Wearable Sensors," 2017 IEEE Body Sensor Network Conference (IEEE BSN'17), Eindhoven, The Netherlands, May, 2017.

[A15] Christoph M. Kanzler, Sunghoon Ivan Lee, Jean-Francois Daneault, Fatemah Noushin Golachi, Julius Hannink, Cristian Pasluosta, Bjoern M. Eskofier, Paolo Bonato, "Home monitoring of drug response in patients with Parkinson's disease using wearable sensors," IEEE Wireless Health 2016 (IEEE WH'15), Bethesda, MD, October, 2016

[A14] Sunghoon Ivan Lee, Catherine Adans-Dester, Anne O’Brien, Gloria Vergara-Diaz, Ross D. Zafonte, Randie M. Black-Schaffer, Paolo Bonato, "Using Wearable Motion Sensors to Estimate Longitudinal Changes in Movement Quality in Stroke and Traumatic Brain Injury Survivors Undergoing Rehabilitation," 93rd Annual Conference of American Congress of Rehabilitation Medicine (ACRM), Chicago, USA, Oct, 2016

[A13] Jean-Francois Daneault, Fatemah Noushin Golachi, Sunghoon Ivan Lee, Gloria Vergara-Diaz, Gabriela Ferreira Carvalho, Eric Fabara, Stefano Sapienza, Paolo Bonato, "Monitoring dyskinesia severity using wearable sensor data," the 20th International Congress of Parkinson’s Disease and Movement Disorders, Berlin, Germany, June, 2016

[A12] Sunghoon Ivan Lee, Catherine Pierre Adans-Dester, Gloria Vergara Diaz, Giovanni Mascia, Shyamal Patel, Randie Black-Schaffer, Ross Zafonte, Paolo Bonato, "Using Wearable Motion Sensors to Estimate Longitudinal Changes in Movement Quality in Stroke Survivors Undergoing Rehabilitation," Wireless Health 2015 (WH2015), Bethesda, MD, October, 2015

[A11] Monica Arnell, Eunjeong Park, Sunghoon Ivan Lee, Charles Li, Andrew Campion, Ruth Getachew, Daniel C. Lu, "Wireless technology to assess neuromotor function during substance use," Congress of Neurological Surgeons, Boston, MA, October, 2015

[A10] Sunghoon Ivan Lee, Nicolas Menard, Bor-rong Chen, Muzaffer Yalgin Ozsecen, Paolo Bonato, "MercuryLive+:Towards Robust Remote Monitoring of Patients with Neuromotor Deficits Using Wearable Sensor Networks," Mobile Health in Rehabilitation, Boston, MA, October, 2014

[A9] Muzaffer Yalgin Ozsecen, Sunghoon Ivan Lee, Nicolas Menard, N. Chen, Paolo Bonato, "Assessing the Usability of a Knee Range of Motion Monitoring Device (KROMM) for Long-term Home with OA Patients," Mobile Health in Rehabilitation, Boston, MA, October, 2014

[A8] Haydn Hoffman, Charles H. Li, Sunghoon Ivan Lee, Jordan Garst, Marie Espinal, Nima Jahanforouz, Amir Ali Ghavamrezaii, Majid Sarrafzadeh, Daniel C. Lu, "Use of Multivariate Linear Regression Models and Support Vector Regression Models to Predict Outcome in Patients Undergoing Surgery for Cervical Spondylotic Myelopathy," Congress of Neurological Surgeons 2014 Annual Meeting (CNS 2014), Boston, MA, October, 2014

[A7] Charles H. Li, Sunghoon Ivan Lee, Derek S. Lu, Peter Culmer, Daniel C. Lu, Irene Wu, "Novel Mobile Devices for Assessing Efficacy of Cervical Epidural Steroid Injection," Western Regional Anesthesia Conference, Century City, USA, May, 2014.

[A6] Monica Arnell, Sunghoon Ivan Lee, Eunjeong Park, Charles Li, Ruth Getachew, Daniel C. Lu, "Wireless Technology for Motor Assessment in Patients with Lumbar Spine Injury," 82nd American Association of Neurological Surgeons (AANS) Annual Scientific Meeting, San Francisco, USA, April, 2014.

[A5] Nicholas Au Yong, Sunghoon Ivan Lee, Ruth Getachew, Jon Kimball, Majid Sarrafzadeh, Daniel D. Lu, "Characterizing Transient Activation Hypertonia during a Handgrip Force Tracking Task in Cervical Myelopathy," Neuroscience, San Diego, California, USA, November, 2013. [view]

[A4] Sunghoon Ivan Lee, Hassan Ghasemzadeh, Andrew Yew, Ruth Getachew, Jon Kimball, Nima Ghalehsari, Brian H. Paak, Jordan H. Garst, Mehrdad Razaghy, Daniel C. Lu, Majid Sarrafzadeh, "Objective Assessment of Spastic Hand Hypertonia using a Novel Digital Device," The 20th IAGG World Congress of Gerontology and Geriatrics, Seoul, Korea, June, 2013. [pdf]

[A3] Ruth Getachew, Sunghoon Ivan Lee, Jon Kimball, Andrew Yew, Nima Ghalehasari, Brian H. Paak, Daniel C. Lu, Majid Sarrafzadeh, "Utilization of a Novel Digital Measurement Tool for Quantitative Assessment of Upper Extremity Motor Dexterity in Cervical Spondylotic Myelopathy," (Abstract) 29th Annual Meeting of the AANS/CNS Disorders of the Spine and Peripheral Nerves, Phoenix, Arizona, USA, March, 2013. [pdf]

[A2] Sunghoon Ivan Lee, Ruth Getachew, Jon Kimball, Andrew Yew, Nima Ghalehasari, Brian H. Paak, Jordan H. Garst, Daniel C. Lu, Majid Sarrafzadeh, "Utilization of a Novel Digital Device and an Analytic Method for Accurate Measurement of Upper Extremity Motor Function," (Abstract) 81st American Association of Neurological Surgeons (AANS) Annual Scientific Meeting, New Orleans, Louisiana, USA, April, 2013. [pdf]

[A1] Taiwoo Park, Inseok Hwan, Uichin Lee, Sunghoon Ivan Lee, Chungkuk Yoo, H. Jang, S. Choe, S. Park, Junehwa Song, "ExerLink: Enabling Pervasive Social Exergames with Heterogeneous Exercise Devices"ACM MobiSys 2012 Demonstration, Lake District, UK, June, 2012. [Best Demo Award] [pdf] Also demonstrated at IEEE SECON 2012 [Best Demo Honorable Mention]

 

Patents

[P1] Sunghoon Ivan Lee, Bobak J. Mortazavi, Majid Sarrafzadeh, "Method and Apparatus for Mobile Rehabilitation Exergaming" U.S. Provisional Application No. 61/640,643 filed April. 30. 2012

 

Awards, Services, and Teaching

 

Awards / Honors

  • Northrup-Grumman Outstanding Research Student Award, Computer Science Department of UCLA, July 2014
  • Featured Article of the Issue, IEEE Journal of Biomedical and Health Informatics, March 2014
  • Finalist - "funRehab: A Mobile Rehabilitation Gaming System," UCLA Business of Science Center Venture Competition, June, 2012
  • Best Demo Award, ACM MobiSys, June 2012
  • Best Demo Honorable Mention, IEEE Secon, June 2012

 

Invited Talks

  • Sunghoon Ivan Lee, "The Use of Wearable Sensors and Systems in Rehabilitation Medicine," University of Hokkaido – Graduate School of Information Science and Technology, Sapporo, Hokkaido, Japan, March. 2017.
  • Sunghoon Ivan Lee, "The Use of Wearable Sensors and Systems in Rehabilitation Medicine," Worcester Polytechnic Institute (WPI) – Department of Electrical and Computer Engineering, Worcester, USA, December, 2016.
  • Sunghoon Ivan Lee, "The Use of Wearable Sensors and Systems in Rehabilitation Medicine," University of Massachusetts – Department of Kinesiology, Amherst, USA, September, 2016.
  • Sunghoon Ivan Lee, "The Use of Wearable Sensors and Systems in Rehabilitation Medicine," Emerging Information & Technology Conference (EITC 2016), MIT, Cambridge, USA, August, 2016.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System using Wearable Sensors and Networks," Texas A&M University, College Station, USA, April, 2016.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System using Wearable Sensors and Networks," University of Massachusetts (CICS), Amherst, USA, March, 2016.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System using Wearable Sensors and Networks," The Penn State University, State College, USA, March, 2016.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System using Wearable Sensors and Networks," University of Massachusetts (Nursing & Eng), Amherst, USA, February, 2016.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System using Wearable Sensors and Networks," University of Minnesota, Twin Cities, USA, February, 2016.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System," University of Georgia, Athens, USA, November, 2015.
  • Sunghoon Ivan Lee, "Mobile and Connected Health: Towards a Proactive, Preventive, and Patient-centered Healthcare System," Michigan State University - Catalyst Talks, College of Communication Arts and Sciences, Michigan State University, East Lansing, USA, October, 2015.
  • Sunghoon Ivan Lee, "The Use of Wearable Sensors and Systems in Rehabilitation Medicine ," 2015 IEEE Body Sensor Network Conference (IEEE BSN'15), Cambridge, USA, June, 2015.

 

Academic Services

  • Panel Member for the National Science Foundation (NSF)

  • Guest Editor of the Special Issue of MDPI Applied Sciences on "Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering".
  • Guest Editor of the Special Issue of MDPI Information Journal on Smart Health

  • Workshop Chair of the "Tutorial on Machine Learning and Data Mining with a Focus on Human Studies" in conjunction with the ACM Wireless Health Conference 2015
  • TPC Member for the Second IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (IEEE/ACM CHASE'17)
  • TPC Member for the IEEE Consumer Communications & Networking Conference (IEEE CCNC’17)
  • TPC Member for the ACM 6th International Conference on Digital Health (ACM DH'16)
  • TPC Member for the International Workshop on Self-Powered Systems, Engineering Technologies and Applications (SETA'15)
  • TPC Member for the ACM Wireless Health Conference 2015 (WH’15)
  • TPC Member for the IEEE Consumer Communications & Networking Conference (IEEE CCNC’15)
  • TPC Member for the ACM 5th International Workshop on Pervasive Wireless Healthcare (MobileHealth'15)
  • TPC Member for the ACM 5th International Conference on Digital Health (ACM DH'15)
  • TPC Member for the ACM UbiComp Workshop on Smart Health Systems and Applications (SmartHealth'14)

  • Reviewer for the 11th International Conference on Body Area Networks 2016 (BodyNets'16)
  • Reviewer for the Elsevier Computers in Biology and Medicine (CBM)
  • Reviewer for the 20th International Symposium on Wearable Computer 2016 (ISWC’16)
  • Reviewer for the IEEE Journal of Biomedical and Health Informatics
  • Reviewer for the Journal of NeuroEngineering and Rehabilitation (JNER)
  • Reviewer for the ACM Intl. Joint Conf. on Pervasive and Ubiquitous Computing 2015 (ACM UbiComp'15)
  • Reviewer for the IEEE Journal of Translational Engineering in Health and Medicine
  • Reviewer for the Medical Engineering & Physics
  • Reviewer for the IEEE Journal of Biomedical and Health Informatics
  • Reviewer for the IEEE Transactions on Biomedical Engineering
  • Reviewer for the IEEE Sensors Journal
  • Reviewer for the IEEE Transactions on Human-Machine Systems
  • Reviewer for the IEEE Intl. Conf. on Healthcare Informatics 2014 (IEEE ICHI'14)
  • Reviewer for the Intl. Conf. on Brain Informatics and Health 2014 (BIH'14)
  • Reviewer for the ACM Intl. Joint Conf. on Pervasive and Ubiquitous Computing 2014 (ACM UbiComp'14)
  • Reviewer for the IEEE Body Sensor Network Conference 2014 (IEEE BSN'14)
  • Reviewer for the Intl. Conf. on Mobile Computing, Applications and Services 2013 (MobiCase'13)
  • Reviewer for the IEEE Symposium on Industrial Electronics and Applications 2013 (IEEE ISIEA'13)
  • Reviewer for the IEEE Global Conference on Signal and Information Processing 2013 (IEEE GlobeSIP'13)
  • Reviewer for the ACM Wireless Health 2013 (ACM WH'13)
  • Reviewer for the IEEE Body Sensor Network Conference 2013 (IEEE BSN'13)
  • Reviewer for the ACM Wireless Health 2012 (ACM WH'12)
  • Reviewer for the IEEE Body Sensor Network Conference 2012 (IEEE BSN'12)

 

Teaching @ UCLA

  • UMass CS 691WM (Fall 2017): Wearable and Mobile Sensors in Clinical Sciences
  • UMass CS 240 (Fall 2017): Reasoning Under Uncertainty
  • UMass CS 390N (Spring 2017): Internet of Things
  • UCLA CS 152A (Fall 2013): Introductory Digital Design Laboratory
  • UCLA CS 259 (Spring 2013): Wireless Health
  • UCLA CS 152A (Spring 2013): Introductory Digital Design Laboratory
  • UCLA CS 152A (Winter 2013): Introductory Digital Design Laboratory
  • UCLA CS 152A (Fall 2012): Introductory Digital Design Laboratory
  • UCLA CS 152B (Summer 2012): Digital Design Logic Laboratory
  • UCLA CS 152A (Spring 2012): Introductory Digital Design Laboratory
  • UCLA CS 152A (Winter 2012): Introductory Digital Design Laboratory
  • UCLA CS 31 (Summer 2011): Introduction to Computer Science I
  • UCLA CS 152A (Spring 2011): Introductory Digital Design Laboratory
  • UCLA CS 152A (Winter 2011): Introductory Digital Design Laboratory
  • UCLA CS 152A (Fall 2010): Introductory Digital Design Laboratory
  • UCLA CS 152B (Summer 2010): Digital Design Logic Laboratory
  • UCLA CS 152A (Winter 2010): Introductory Digital Design Laboratory
  • UCLA CS 152A (Fall 2009): Introductory Digital Design Laboratory

 

Software

 

MATLAB to Weka Interface

Weka is an open-source platform providing various machine learning algorithms for data mining tasks. Although Weka provides fantastic graphical user interfaces (GUI), sometimes I wished I had more flexibility in programming Weka. For instance, I often needed to perform the analysis based on leave-one-out-subject cross-validation, but it was quite difficult to run this on Weka GUI. I do most of my analyses on MATLAB, so I was searching for an interface between MATLAB and Weka. Fortunately, Weka was implemented in Java, and MATLAB had a wrapper that allows communicating with Java.

Here I introduce an efficient MATLAB to Weka interface, which was implemented based on the initial work of Matt Dunham.

This work is still in-progress and I have only included codes that I mainly use for my work. If you would like to collaborate to improve the code or if you find any bugs, please don't hesitate to reach me at "silee {at} partners {dot} org".

MATLAB CODE & EXAMPLES

CLICK FOR TUTORIAL

IEEE Journal of Biomedical and Health Informatics
IEEE Trans. Biomedical Engineering
IEEE Trans. Neural Systems and Rehabilitation Engineering
IEEE Trans. Biomedical Circuits and Systems
IEEE Journal of Translational Engineering in Health and Medicine
IEEE Life Sciences Letters
IEEE Systems Journal
IEEE Pervasive Computing
Medical Engineering & Physics
Medical & Biological Engineering & Computing
Journal of NeuroEngineering and Rehabilitation
Journal of Rehabilitation Research & Development
IEEE Trans. Systems, Man, and Cybernetics: Systems
IEEE Trans. Human-Machine Systems
IEEE Trans. Cybernetics
IEEE Trans. Mobile Computing
ACM Trans. Embedded Computing Systems
ACM Trans. Intelligent Systems and Technology
IEEE Consumer Electronics Magazine
Isaac Newton's first published paper