Research Projects
Welcome to the dynamic realm of biomedical engineering research, where innovative ideas converge with comprehensive investigation.
Upper-extremity Function (UEF) - Assessing Physical and Cognitive Frailty in Older Adults
This research seeks to change current risk stratification and adverse health outcomes prediction paradigms among older adults, by utilizing novel biomechanical approaches and wearable sensor technology. Using this test we are able to objectively assess:
1. Frailty
2. Cognitive Impairments
3. Functional Capacity
Treatment Evaluation (Patient-centered Studies)
The primary aim of these studies is to evaluate the improvement in motor performance after clinical treatments and to predict potential treatment failures based on initial functional measurements. This includes examining Parkinson's disease through electro-acupuncture effects, assessing motor performance both at home and in clinical settings; exploring degenerative facet osteoarthropathy by analyzing the impact of paravertebral spinal injections; and investigating peripheral artery disease with a focus on frailty and gait difficulties.
Fall Risk Assessment and Fall Prevention
Vibratory stimulation has the potential to disrupt postural balance in individuals with normal sensory performance; conversely, it may enhance balance in older adults at high risk of falls, due to age-related changes in muscle spindle sensory function. The key objectives include validating an objective stimulation-based balance assessment test for community-dwelling older adults, employing innovative biomechanical methods, vibratory stimulation, and wearable sensors. Furthermore, this study aims to comprehend the significance of proprioceptive feedback from ankle versus hip muscles during balance recovery. Another focus is to create a lower-extremity vibratory stimulation device designed to enhance gait and postural stability in elderly individuals facing high fall risk.
Sensor-based Real-time Tracking-game (SRT)
We establish a novel Sensor-based Real-time Tracking-game (SRT), which is based on tracking a flying target by ankle movements using a smartwatch on the foot and a smartphone/tablet. By measuring the amplitude and directional accuracy during the tracking, we engage ankle proprioceptive function based on correction mechanism of tracking error through the open-loop reflexive responses and closed-loop adjustment within the central nervous system. SRT incorporates combined anterior-posterior and medial-lateral ankle maneuvers to replicate realistic real-life sensation experiences.
Early Diagnosis of the Alzheimer’s Disease Using Functional Brain Imaging
This research evaluates the functional differences observed in the aging brain, specifically in individuals aged 65 and older, across three groups: cognitively normal individuals, those with amnestic mild cognitive impairment (aMCI), and patients in the early stages of Alzheimer's Disease (AD). Our objective is to enable the early detection of AD through advanced techniques such as fMRI image processing and functional near-infrared spectroscopy (fNIRS), utilizing machine learning methods that are currently under development.
Heart Rate Dynamics to Predict Frailty in Heart Disease
One main reason for the lack of optimal healthcare performance for treating heart disease patients is the inefficiency in predicting clinical outcomes related to frailty. In this research, we develop a novel objective sensor-based approach in combination with machine learning approaches to characterize heart behavior in response to a localized rapid upper-extremity function task, among older adults with advanced heart disease to predict therapy outcomes:
1. Frailty Assessment Using Combined Motor and Cardiac Functions
2. Frailty Identification Using Heart Rate Dynamics and Deep Learning
3. The Association Between Heart Rate Behavior and Gait Performance
Low Back Disorders (LBDs)
To investigate the risk of LBDs due to prolonged and/or repetitive loadings, we developed a time-dependent finite element model with viscoelastic properties. Using this model we were able to:
1. Describe Trunk Load-relaxation and Creep Behaviors
2. Predict Changes in Spine Loads Following Trunk Flexion Exposures
3. Understand Dependency of Spine Loads on Prior Flexion Angles and Loading Conditions
