I am an Associate Professor in the Department of Rehabilitation and Movement Sciences at Rutgers University. I also hold a position as a Core Member of the Brain Health Institute and serve as an affiliate in the Center for Advanced Human Brain Imaging Research. In addition, I act as Secondary Faculty in the Department of Neurology at the New Jersey Medical School. My research spans biomechanical modeling, physiological measurement techniques, innovative body-worn sensor technologies, and nonlinear dynamic analysis. I apply these methods in patient-centered clinical research focused on assessing physical and cognitive frailty and improving fall rehabilitation in older adults.
Lab mission: evaluating physical and cognitive frailty through the analysis of physiological alterations and dysregulation in cardiac, motor, and cognitive systems.
Changing current risk-stratification and adverse health outcome prediction paradigms among older adults using novel biomechanical approaches and wearable sensor technology to objectively assess frailty, cognitive impairment, and functional capacity.
Learn More →Evaluating improvement in motor performance after clinical treatments and predicting potential treatment failures from initial functional measurements, spanning Parkinson's disease, degenerative facet osteoarthropathy, and peripheral artery disease.
Using vibratory stimulation and wearable sensors to validate an objective balance assessment test, understand proprioceptive feedback from ankle versus hip muscles, and develop a lower-extremity vibratory device to enhance gait and postural stability.
Learn More →A novel game based on tracking a flying target through ankle movements using a smartwatch on the foot and a tablet, engaging ankle proprioceptive function via open-loop reflexive and closed-loop central nervous system responses.
Evaluating functional differences in the aging brain across cognitively normal, amnestic mild cognitive impairment, and early-stage Alzheimer's groups using fMRI and fNIRS with machine learning for early detection.
Learn More →A sensor-based approach combined with machine learning to characterize heart behavior during a localized upper-extremity task in older adults with advanced heart disease, to predict therapy outcomes.
Learn More →A time-dependent finite element model with viscoelastic properties to describe trunk load-relaxation and creep behaviors and predict changes in spine loads following trunk flexion exposures.
Biomedical engineer with a strong foundation in developing objective, sensor-based approaches to complex health challenges, with a focus on older adults' care.
Dedicated research assistants and collaborators.
Celebrating the people behind our success.
Questions about the research, collaboration inquiries, or prospective students — reach out below.
Email
nima.toosizadeh@rutgers.edu
Department
Rehabilitation and Movement Sciences
Rutgers University, Newark, NJ