Ivan Lee
Associate Professor, Manning College of Information and Computer Sciences | University of Massachusetts Amherst
Amherst, MA, UNITED STATES
Ivan Lee develops mobile and personalized health solutions.
Expertise (4)
Mobile and Personalized Health
Wearable Health Sensing
Embedded Systems
Sensor Data Analytics
Biography
Ivan Lee does work on mobile and personalized health, focusing on the use of digital technologies to understand health conditions and promote health behavioral change in individuals with motor/cognitive impairments, such as stroke, Parkinson's disease, traumatic brain injuries and osteoarthritis.
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.
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Education (4)
UCLA: Ph.D., Computer Science
UCLA: M.S., Computer Science
UCLA: M.S., Electrical Engineering
Simon Fraser University: B.A.Sc.,, Computer Engineering
Links (4)
Select Recent Media Coverage (3)
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 (5)
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.
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.
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.
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.
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.
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