Integrated Sensing Systems for Monitoring Interrelated Physiological Parameters in Young and Aged Adults A Pilot Study

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Published Aug 24, 2021
DOI https://doi.org/10.36001/ijphm.2021.v12i4.2914
Mark Sprowls Michael Serhan
School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
En-Fan Chou
Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, USA
Lancy Lin
Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, USA
Christopher Frames
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
Ivan Kucherenko
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Keyvan Mollaeian
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Yang Li
School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
Varun Jammula
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
Dhenugen Logeswaran
School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
Michelle Khine
Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, USA
Yezhou Yang
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
Thurmon Lockhart
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
Jonathan Claussen
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Liang Dong
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
Julian J.-L. Chen
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
Juan Ren
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Carmen Gomes
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Daejin Kim
Department of Interior Design, Iowa State University, Ames, IA 50011, USA
Teresa Wu
School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
Jennifer Margrett
Department of Human Development and Family Studies, Iowa State University, Ames, IA 50011, USA
Balaji Narasimhan
Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa 50011
Erica Forzani
School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA

Abstract

Acute injury to aged individuals represents a significant challenge to the global healthcare community as these injuries are frequently treated in a reactive method due to the infeasibility of frequent visits to the hospital for biometric monitoring. However, there is potential to prevent a large number of these cases through passive, at-home monitoring of multiple physiological parameters related to various causes that are common to aged adults in general. This research strives to implement wearable devices, ambient “smart home” devices, and minimally invasive blood and urine analysis to test the feasibility of implementation of a multitude of research-level (i.e. not yet clinically validated) methods simultaneously in a “smart system”. The system comprises measures of balance, breathing, heart rate, metabolic rate, joint flexibility, hydration, and physical performance functions in addition to lab testing related to biological aging and mechanical cell strength. A proof-of-concept test is illustrated for two adult males of different ages: a 22-year-old and a 73-year-old matched in body mass index (BMI). The integrated system is test in this work, a pilot study, demonstrating functionality and age-related clinical relevance. The two subjects had physiological measurements taken in several settings during the pilot study: seated, biking, and lying down. Balance measurements indicated changes in sway area of 45.45% and 25.44%, respectively for before/after biking. The 22-year-old and the 73-year-old saw heart rate variabilities of 0.11 and 0.02 seconds at resting conditions, and metabolic rate changes of 277.38% and 222.23%, respectively, in comparison between the biking and seated conditions. A smart camera was used to assess biking speed and the 22- and 73-year-old subjects biked at 60 rpm and 28.5 rpm, respectively. The 22-year-old subject saw a 7 times greater electrical resistance change using a joint flexibility sensor inside of their index finger in comparison with the 73-year-old male. The 22 and 73-year-old males saw respective 28% and 48% increases in their urine ammonium concentration before/after the experiment. The average lengths of the telomere DNA from the two subjects were measured to be 12.1 kb (22-year-old) and 6.9 kb (73-year-old), consistent with their biological ages. The study probed feasibility of 1) multi-metric assessment under free living conditions, and 2) tracking of the various metrics over time.

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Keywords

Biomedical Healthcare Devices, Smart Home Sensors, Point of care Medical Devices, Physiological Tracking, Geriatric Medicine

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Issue
Vol. 12 No. 4 (2021): Special Issue on PHM for Human Health & Performance II
Section
Technical Papers