Ivan Lee
Associate Professor, Manning College of Information and Computer Sciences
Expertise
Biography
With a primary focus on evolution, his specific research interests include developing novel sensors and remote monitoring solutions that are motivated by practical medical needs, designing appropriate human studies, and applying human-centered approaches to quantitatively and qualitatively analyze the efficacy of the developed solutions.
Social Media
Video
Education
Simon Fraser University
B.A.Sc.,
Computer Engineering
UCLA
M.S.
Electrical Engineering
UCLA
M.S.
Computer Science
UCLA
Ph.D.
Computer Science
Links
Select Recent Media Coverage
Wearable Wrist Device Could Boost Stroke Rehab
US News & World Report online
2026-10-01
A device worn on the wrist and powered by an artificial intelligence (AI) algorithm can assess rehab progress in real time for people who've lost arm movement due to stroke, new research shows. The new wrist device appeared to outperform doctors in assessing how effective a particular rehabilitation program is in helping patients regain arm function. “We are the first group to actually show that, using wearable data, we can extract information about patients’ motor severity, which clinicians can actually use to determine whether their intervention is effective or not,” said study co-author Sunghoon Ivan Lee, an associate professor of information and computer science at the university.
Low-cost, sensor-equipped insole
Today's Medical Developments online
2022-05-22
The insole “measures two important kinetic parameters that are relevant to how people walk; that is, the ground reaction force (GRF) and center of pressure (CoP),” says lead investigator Sunghoon Ivan Lee, assistant professor in the Manning College of Information and Computer Sciences. “Those parameters contain very important information, especially for people who have gait problems.”
Researchers Develop a Low-Cost Shoe Insole Using Force-Sensitive Resistors
AtoZ Sensors online
2022-05-05
The insole measures two important kinetic parameters that are relevant to how people walk; that is, the ground reaction force (GRF) and center of pressure (CoP). Those parameters contain very important information, especially for people who have gait problems.
Sunghoon Ivan Lee, Study Lead Investigator and Assistant Professor, Manning College of Information and Computer Sciences, University of Massachusetts Amherst
Smartwatches charged by the human body? Why not?
Mint Lounge online
2021-07-05
"Quality of sleep and its patterns contain a lot of important information about patients' health conditions," says Sunghoon Ivan Lee, assistant professor in the University of Massachusetts Amherst College of Information and Computer Sciences and director of the Advanced Human Health Analytics Laboratory.
Select Publications
Assessment of upper-limb motor recovery after stroke using a wrist-worn accelerometer digital biomarker
Science Translational Medicine2026
Existing clinical assessments for upper-limb motor rehabilitation poststroke pose limitations as end points for efficient clinical trials. This study aimed to develop a digital metric for assessing motor recovery using accelerometer data collected in naturalistic environments. We constructed the digital arm performance scale (DAPS) by analyzing ∼23,000 hours of data from 215 participants, including healthy individuals and subacute and chronic stroke survivors. We decomposed continuous upper-limb accelerometer data into lower-level movement segments, from which key features were extracted and aggregated using a linear mixed-effects model to produce an interpretable digital biomarker. DAPS demonstrated excellent reliability, sensitivity, concurrent validity, known-groups validity, discriminant validity, and responsiveness. Power analysis indicated that DAPS could reduce the required sample size for clinical trials with upper-limb motor recovery end points by more than 60% compared with traditional assessments. These findings highlight the potential of DAPS as a low-burden, scalable assessment tool for upper-limb motor recovery, with potential applications in both clinical trials and practice.
Analysis of gait sub-movements to estimate ataxia severity using ankle inertial data
IEEE Transactions on Biomedical Engineering2022
Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia.
A kinematic data-driven approach to differentiate involuntary choreic movements in individuals with neurological conditions
IEEE Transactions on Biomedical Engineering2022
The ability to differentiate similar choreic involuntary movements could lay the groundwork for the development of a minimally-invasive screening tool for their etiology and provide in-depth understandings of pathophysiology. As a first step, we investigate kinematic differences between Huntington’s disease (HD) chorea and Parkinson’s disease (PD) choreic levodopa-induced dyskinesia (LID), which have distinct pathological causes yet share a great kinematic resemblance.
Charging Wearable Devices Through Natural Interactions with Instrumented Everyday Objects
GetMobile: Mobile Computing and Communications2022
Recent advancements in semiconductor technologies have stimulated the growth of ultra-low power wearable devices. However, these devices often pose critical constraints in usability and functionality because of the on-device battery as the primary power source. For example, periodic charging of wearable devices hampers the continuous monitoring of users' fitness or health conditions, and batteries and charging equipment have been identified as one of the most rapidly growing electronic waste streams.
Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors’ and occupational therapists’ perspectives
Plos one2022
The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients’ functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients’ arm use patterns in their naturalistic environments.
Estimation of ataxia severity in children with ataxia-telangiectasia using ankle-worn sensors
Journal of Neurology2023
Ataxia-telangiectasia (AT) is a neurodegenerative disease characterized by onset of ataxia in early childhood with impairments in gait, balance, and coordination. As drug development efforts in AT accelerate, it is increasingly important to develop objective motor assessments that can detect early disease features to reduce delays in diagnosis and support early therapies, and to monitor progression in support of clinical trials.