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

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

PDF
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.

Abstract 1082 | PDF Downloads 537

##plugins.themes.bootstrap3.article.details##

Keywords

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

References
Agha, M. A., & El Wahsh, R. A. (2013). Basal metabolic rate in bronchial asthma and chronic obstructive pulmonary disease patients. Egyptian Journal of Chest Diseases and Tuberculosis, 62(1), 39-44. doi:https://doi.org/10.1016/j.ejcdt.2013.01.007
Armstrong, L. E., et al. (1998). Urinary indices during dehydration, exercise, and rehydration. International Journal of Sport Nutrition, 8(4), 345-355. doi:10.1123/ijsn.8.4.345
Barns, S., et al. (2017). Investigation of red blood cell mechanical properties using AFM indentation and coarse-grained particle method. Biomedical engineering online, 16(1), 140-140. doi:10.1186/s12938-017-0429-5
Benetos, A., et al. (2004). Short telomeres are associated with increased carotid atherosclerosis in hypertensive subjects. Hypertension, 43(2), 182-185. doi:10.1161/01.HYP.0000113081.42868.f4
Bennett, J., et al. (2004). Unrecognized Chronic Dehydration in Older Adults: Examining Prevalence Rate and Risk Factors. Journal of gerontological nursing, 30, 22-28; quiz 52. doi:10.3928/0098-9134-20041101-09
Binette, J., & Vasold, K. (2018). 2018 Home and Community Preferences: A National Survey of Adults Age 18-Plus. Retrieved from doi: https://doi.org/10.26419/res.00231.001
Boulgarides, L. K., et al. (2003). Use of clinical and impairment-based tests to predict falls by community-dwelling older adults. Physical Therapy, 83(4), 328-339.
Brouilette, S., et al. (2003). White cell telomere length and risk of premature myocardial infarction. Arterioscler Thromb Vasc Biol, 23(5), 842-846. doi:10.1161/01.atv.0000067426.96344.32
Chan, E., et al. (2012). Spherical indentation testing of poroelastic relaxations in thin hydrogel layers. Soft Matter, 8, 1492-1498. doi:10.1039/C1SM06514A
Charlton, P. H., et al. (2017). Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physioloigical Measurement(1361-6579 (Electronic)).
Chu, M., et al. (2019). Respiration rate and volume measurements using wearable strain sensors. NPJ Digital Medicine, 2, 8. doi:10.1038/s41746-019-0083-3
Daubney, M. E., & Culham, E. G. (1999). Lower-extremity muscle force and balance performance in adults aged 65 years and older. Physical Therapy, 79, 1177-1185.
Dimitriadis, E. K., et al. (2002). Determination of elastic moduli of thin layers of soft material using the atomic force microscope. Biophysical journal, 82(5), 2798-2810. doi:10.1016/S0006-3495(02)75620-8
Doheny, E. P., et al. (2012). Displacement of centre of mass during quiet standing assessed using accelerometry in older fallers and non-fallers. Conf Proc IEEE Eng Med Biol Soc, 2012, 3300-3303. doi:10.1109/embc.2012.6346670
Frames, C. W., et al. (2018). Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults. Sensors, 18(6), 1692.
Gago, M. F., et al. (2014). Postural stability analysis with inertial measurement units in Alzheimer's disease. Dementia and geriatric cognitive disorders extra, 4(1), 22-30. doi:10.1159/000357472
Gohring, J., et al. (2014). TeloTool: a new tool for telomere length measurement from terminal restriction fragment analysis with improved probe intensity correction. Nucleic Acids Res.
(1362-4962 (Electronic)).
Hugli, O., et al. (1996). The daily energy expenditure in stable chronic obstructive pulmonary disease. American Journal of Respiratory and Critical Care Medicine, 153(1), 294-300. doi:10.1164/ajrccm.153.1.8542132
Kanazawa, A., et al. (2018, 18-23 June 2018). End-to-End Recovery of Human Shape and Pose. Paper presented at the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
Kim, J., et al. (2019). Soft Wearable Pressure Sensors for Beat-to-Beat Blood Pressure Monitoring. Advanced Healthcare Materials, 8(13), 1900109. doi:10.1002/adhm.201900109
Lavizzo-Mourey, R. J. (1987). Dehydration in the elderly: a short review. Journal of the National Medical Association, 79(10), 1033-1038.
Lockhart, T. E., et al. (2014). Fall risks assessment among community dwelling elderly using wearable wireless sensors (Vol. 9091): SPIE.
Loper, M., et al. (2015). SMPL: a skinned multi-person linear model. ACM Trans. Graph., 34(6), 1-16. doi:10.1145/2816795.2818013
McArdle, W. D. K., F. I.; Katch, V. L. (2010). Exercise physiology: nutrition, energy, and human performance.: Lippincott Williams & Wilkins.
Melzer, I., et al. (2004). Postural stability in the elderly: a comparison between fallers and non-fallers. Age Ageing, 33(6), 602-607. doi:10.1093/ageing/afh218
Mender, I., & Shay, J. W. (2015). Telomere Restriction Fragment (TRF) Analysis. Bio-protocol, 5(22), e1658. doi:10.21769/bioprotoc.1658
Meunier, N., et al. (2005). Basal metabolic rate and thyroid hormones of late-middle-aged and older human subjects: the ZENITH study. European Journal of Clinical Nutrition, 59(2), S53-S57. doi:10.1038/sj.ejcn.1602299
Mollaeian, K., et al. (2018). Nonlinear Cellular Mechanical Behavior Adaptation to Substrate Mechanics Identified by Atomic Force Microscope. Int J Mol Sci, 19(11). doi:10.3390/ijms19113461
Mora, S. J., et al. (2020). Validation of Resting Energy Expenditure (REE) Measurement of new Breezing Pro device through Douglas Bag Method. Global Journal of Obesity, Diabetes and Metabolic Syndrome, 7(1).
Mullur, R., et al. (2014). Thyroid hormone regulation of metabolism. Physiological reviews, 94(2), 355-382. doi:10.1152/physrev.00030.2013
Musich, S., et al. (2018). The impact of mobility limitations on health outcomes among older adults. Geriatric Nursing, 39, 162-169.
Nguyen, T. Y. V., et al. (2016). Comparison of Resting Energy Expenditure Between Cancer Subjects and Healthy Controls: A Meta-Analysis. Nutrition and Cancer, 68(3), 374-387. doi:10.1080/01635581.2016.1153667
U.S. Census Bureau. Older People Projected to Outnumber Children for First Time in U.S. History. (2018).
Oren, S., et al. (2017). High-Resolution Patterning and Transferring of Graphene-Based Nanomaterials onto Tape toward Roll-to-Roll Production of Tape-Based Wearable Sensors. Advanced Materials Technologies, 2(12), 1700223. doi:10.1002/admt.201700223
Panossian, L. A., et al. (2003). Telomere shortening in T cells correlates with Alzheimer's disease status. Neurobiol Aging, 24(1), 77-84. doi:10.1016/s0197-4580(02)00043-x
Perdue, P. W., et al. (1998). Differences in mortality between elderly and younger adult trauma patients: geriatric status increases risk of delayed death. Journal of Trauma(0022-5282 (Print)).
Picetti, D., et al. (2017). Hydration health literacy in the elderly. Nutrition and healthy aging, 4(3), 227-237. doi:10.3233/NHA-170026
Piers, L. S., et al. (1998). Is there evidence for an age-related reduction in metabolic rate? Journal of Applied Physiology, 85(6), 2196-2204. doi:10.1152/jappl.1998.85.6.2196
Popkin, B. M., et al. (2010). Water, hydration, and health. Nutrition Reviews(1753-4887 (Electronic)).
Riebl, S. K., & Davy, B. M. (2013). The Hydration Equation: Update on Water Balance and Cognitive Performance. ACSM's health & fitness journal, 17(6), 21-28. doi:10.1249/FIT.0b013e3182a9570f
Rizvi, S., et al. (2014). Telomere length variations in aging and age-related diseases. Curr Aging Sci, 7(3), 161-167. doi:10.2174/1874609808666150122153151
Ruiz, I., et al. (2018). Assessing metabolic rate and indoor air quality with passive environmental sensors. Journal of Breath Research, 12(3), 036012. doi:10.1088/1752-7163/aaaec9
Salomon, F., et al. (1992). Basal metabolic rate in adults with growth hormone deficiency and in patients with acromegaly: relationship with lean body mass, plasma insulin level and leucocyte sodium pump activity. Clinical Science(0143-5221 (Print)).
Shammas, M. A. (2011). Telomeres, lifestyle, cancer, and aging. Current opinion in clinical nutrition and metabolic care, 14(1), 28-34. doi:10.1097/MCO.0b013e32834121b1
Shumway-Cook, A., et al. (2000). Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Physical Therapy, 80(9), 896-903.
Woods, A. L., et al. (2017). New approaches to determine fatigue in elite athletes during intensified training: Resting metabolic rate and pacing profile. PLoS One, 12(3), e0173807. doi:10.1371/journal.pone.0173807
Zhang, Y. L., et al. (2011). Radial pulse transit time is an index of arterial stiffness. Hypertens Res, 34(7), 884-887. doi:10.1038/hr.2011.41
Issue
Vol. 12 No. 4 (2021): Special Issue on PHM for Human Health & Performance II
Section
Technical Papers