Nima Toosizadeh, Ph.D.
Dr. Toosizadeh’s research focus is computational model, sensor-based engineering approach, and machine learning tools to diagnose and treat older adults with aging-related conditions, focusing on frailty and cognitive impairment assessment and fall rehabilitation.
Keywords: Aging, Wearable Sensors, Biomechanics, Machine Learning, Frailty and Fall
Mehran Asghari, Ph.D.
Postdoc Fellow
Kelsi Petrillo
PhD student
Grant Funding
Title: Heart Rate Dynamics in Response to Upper-Extremity Function Test to Identify Irreversible Frailty After Invasive Therapy in Older Adults with Advanced Heart Disease
Sponsor Agency: NIH 1 R01 AG076774-01A1
Funding Period: 01/2023-01/2027
Total Dollar Amount: $1,313,940
Brief Overview of Aims: Advanced heart diseases lead to a reduced blood supply from the heart and consequently fatigue and deficits in performing physical activity. In the proposed research, we will assess the lack of physiological reserve in older adults with advanced heart disease, focusing on motor and cardiac function, to develop a novel, objective, quick, and accurate frailty score. We designed this approach to enhance candidate selection of older adults going through invasive therapies for advanced heart diseases.
Title: CAREER: Dynamic Modeling of Cardiac, Brain, and Motor Systems in Response to Provocative Testing for Frailty Assessment
Sponsor Agency: NSF 2236689
Funding Period: 03/2023-02/2028
Total Dollar Amount: $580,246
Brief Overview of Aims: In this career award I will establish a novel mathematical frailty model based on interaction between several physiological systems during a stress-response testing module.
Title: WARE-Care: a novel RF-based system to assess and prevent falling
Sponsor Agency: NIH R21EB033454 (Trailblazer):
Funding Period: 07/2022-12/2024 (Cao)
Total Dollar Amount: $582,741
Brief Overview of Aims: We propose a complementary sensor for frail and fall risk assessment in the nursing facility during the night. To achieve this goal, we will build and evaluate a robust, non-invasive millimeter wave (mmWave) based sensing system for fall risk and fall detection to work during the night to collect and assess older adults’ falling data. We have formed a research team with expertise in radar signal processing, machine learning, frailty and fall risk analysis, telehealth, clinical trials, and medical experts within the University of Arizona (UArizona) and have connected with a local nursing center to test this low-cost, small, and portable motion-monitoring system: WARE-Care: mmWave based fall Assessment and pRevEntion.
Selected Publications
- Toosizadeh, N., Mohler, J., Najafi, B., (2015) Assessing Upper Extremity Motion: An Innovative Method to Identify Frailty. Journal of the American Geriatrics Society, 63 (6), 1181-1186.
- Eskandari-Nojehdehi M., Parvaneh S., Ehsani H., Fain M., Toosizadeh N., (2022) Frailty Identification using Heart Rate Dynamics: A Deep Learning Approach, IEEE Journal of Biomedical and Health Informatics, 26 (7), 3409-3417.
- Kumar D.P., Toosizadeh N., Mohler J., Ehsani H., Mannier C., Laksari K., (2020) Sensor-based Characterization of Daily Walking: A New Paradigm in Pre-frailty/Frailty Assessment, BMC Geriatrics, BMC geriatrics 20, 1-11.
- Toosizadeh N., Ehsani H., Miramonte M., Mohler J., (2018) Proprioceptive Impairments in High Fall Risk Older Adults: The Effect of Mechanical Calf Vibration on Postural Balance, BioMedical Engineering OnLine, 17 (1), 51.
- Peña M., Petrillo K., Bosset M., Fain M., Chou YH., Rapcsak S., Toosizadeh N., (2022) Brain function complexity during dual‐tasking is associated with cognitive impairment and age, Journal of Neuroimaging, 32 (6), 1211-1223.
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