IoT-based Technologies for Addressing the Unique Healthcare Needs of the Elderly Population

Older Adults' Reasons for Using Technology while Aging in Place

Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,852

Internet of Things (IoT): A vision, architectural elements, and future directions

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293

Chapter 12: What does the Anthropocene hold for citizenship?

(2019) · edoc (University of Basel) · doi:10.1007/978-3 · Cited by 11,533

Machine learning and deep learning

Janiesch, C., Zschech, P., & Heinrich, K. (2021) · Electronic Markets · doi:10.1007/s12525-021-00475-2 · Cited by 2,959

Osteoporosis: now and the future

Rachner, T. D., Khosla, S., & Hofbauer, L. C. (2011) · The Lancet · doi:10.1016/s0140-6736(10)62349-5 · Cited by 2,696

A review of wearable sensors and systems with application in rehabilitation

Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396

An overview and management of osteoporosis

Sözen, T., Özışık, L., & Çalık Başaran, N. (2019) · European Journal of Rheumatology · doi:10.5152/eurjrheum.2016.048 · Cited by 2,262

Big data in healthcare: management, analysis and future prospects

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0217-0 · Cited by 1,747

Wearable sensors: modalities, challenges, and prospects

Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., Li, R., Khine, M., Kim, J., Wang, J., et al. (2018) · Lab on a Chip · doi:10.1039/c7lc00914c · Cited by 1,434

Delirium

Wilson, J. E., Mart, M. F., Cunningham, C. M., Shehabi, Y., Girard, T. D., MacLullich, A. M. J., Slooter, A. J. C., & Ely, E. W. (2020) · Nature Reviews Disease Primers · doi:10.1038/s41572-020-00223-4 · Cited by 1,371

Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017) · IEEE Access · doi:10.1109/access.2017.2694446 · Cited by 1,311

Blockchain technology in healthcare: The revolution starts here

Mettler, M. (2016) · 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2016.7749510 · Cited by 1,237

A survey on ambient-assisted living tools for older adults

Rashidi, P., & Mihailidis, A. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2012.2234129 · Cited by 1,128

Electronic health records in ambulatory care—a national survey of physicians

DesRoches, C. M., Campbell, E. G., Rao, S. R., Donelan, K., Ferris, T. G., Jha, A., Kaushal, R., Levy, D. E., Rosenbaum, S., Shields, A. E., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0802005 · Cited by 1,111

Delirium in elderly adults: diagnosis, prevention and treatment

Fong, T. G., Tulebaev, S. R., & Inouye, S. K. (2009) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.24 · Cited by 1,005

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani, B., Firouzi, F., Chang, V. I. C., Badaroglu, M., Constant, N. P., & Mankodiya, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.036 · Cited by 991

Medical Internet of Things and Big Data in Healthcare

Dimitrov, D. V. (2016) · Healthcare Informatics Research · doi:10.4258/hir.2016.22.3.156 · Cited by 975

Age-Related Diseases and Clinical and Public Health Implications for the 85 Years Old and Over Population

Jaul, E., & Barron, J. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2017.00335 · Cited by 946

A wearable chemical–electrophysiological hybrid biosensing system for real-time health and fitness monitoring

Imani, S., Bandodkar, A. J., Mohan, A. M. V., Kumar, R., Yu, S., Wang, J., & Mercier, P. P. (2016) · Nature Communications · doi:10.1038/ncomms11650 · Cited by 861

Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology

Dagher, G. G., Mohler, J., Milojkovic, M., & Marella, P. B. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.02.014 · Cited by 854

Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions

Boonstra, A., & Broekhuis, M. (2010) · BMC Health Services Research · doi:10.1186/1472-6963-10-231 · Cited by 851

Mobile devices and apps for health care professionals: uses and benefits.

Ventola, C. L. (2014) · PubMed Central · doi:10.57945/manara.23966214 · Cited by 847

Sodium Intake and Hypertension

Grillo, A., Salvi, L., Coruzzi, P., Salvi, P., & Parati, G. (2019) · Nutrients · doi:10.3390/nu11091970 · Cited by 819

Evolution of ageing

Kirkwood, T. B. L. (2002) · Mechanisms of Ageing and Development · doi:10.1016/s0047-6374(01)00419-5 · Cited by 801

Age-related decrease in physical activity and functional fitness among elderly men and women

Milanovic, Z., Jorgić, B., Trajković, N., Sporis, Pantelić, S., & James (2013) · Clinical Interventions in Aging · doi:10.2147/cia.s44112 · Cited by 779

Telemedicine

Wootton, R. (2001) · BMJ · doi:10.1136/bmj.323.7312.557 · Cited by 715

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Fuller, D., Colwell, E., Low, J., Orychock, K., Tobin, M. A., Simango, B., Buote, R., Van Heerden, D., Luan, H., Cullen, K., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/18694 · Cited by 715

Aging, frailty and age-related diseases

Fülöp, T., Larbi, A., Witkowski, J. M., McElhaney, J. E., Loeb, M. B., Mitnitski, A., & Pawelec, G. (2010) · Biogerontology · doi:10.1007/s10522-010-9287-2 · Cited by 687

Fifty Years of Moore's Law

Mack, C. A. (2011) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2010.2096437 · Cited by 658

Untitled work

(2014) · doi:10.5271/sjweh.3388 · Cited by 648

Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare

Guk, K., Han, G., Lim, J., Jeong, K., Kang, T., Lim, E.-K., & Jung, J. (2019) · Nanomaterials · doi:10.3390/nano9060813 · Cited by 624

COVID-19 mortality risk for older men and women

Yanez, N. D., Weiss, N. S., Romand, J.-A., & Treggiari, M. M. (2020) · BMC Public Health · doi:10.1186/s12889-020-09826-8 · Cited by 616

Chronic Kidney Disease and Its Complications

Thomas, R., Kanso, A. A., & Sedor, J. R. (2008) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2008.01.008 · Cited by 606

The interface between delirium and dementia in elderly adults

Fong, T. G., Davis, D., Growdon, M. E., Albuquerque, A., & Inouye, S. K. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00101-5 · Cited by 595

Vision Impairment and Hearing Loss Among Community-Dwelling Older Americans: Implications for Health and Functioning

Crews, J. E., & Campbell, V. A. (2004) · American Journal of Public Health · doi:10.2105/ajph.94.5.823 · Cited by 589

Socially Assistive Robots in Elderly Care: A Systematic Review into Effects and Effectiveness

Bemelmans, R., Gelderblom, G. J., Jonker, P., & de Witte, L. (2012) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2010.10.002 · Cited by 585

Blockchain Technology in Business and Information Systems Research

Beck, R., Avital, M., Rossi, M., & Thatcher, J. B. (2017) · Business & Information Systems Engineering · doi:10.1007/s12599-017-0505-1 · Cited by 580

A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Ometov, A., Shubina, V., Klus, L., Skibińska, J., Saafi, S., Pascacio, P., Flueratoru, L., Quezada-Gaibor, D., Chukhno, N., Chukhno, O., et al. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108074 · Cited by 572

Re-examining rates of lithium-ion battery technology improvement and cost decline

Ziegler, M. S., & Trancik, J. E. (2021) · Energy & Environmental Science · doi:10.1039/d0ee02681f · Cited by 568

The Internet of Things for Ambient Assisted Living

Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., & Schreier, G. (2010) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2010.104 · Cited by 566

Clinical neurophysiology of aging brain: From normal aging to neurodegeneration

Rossini, P. M., Rossi, S., Babiloni, C., & Polich, J. M. (2007) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2007.07.010 · Cited by 529

Delirium in the Elderly Patient

Desforges, J. F., & Lipowski, Z. J. (1989) · New England Journal of Medicine · doi:10.1056/nejm198903023200907 · Cited by 496

World Population Ageing 2019

United Nations Department of Economic and Social Affairs (2020) · doi:10.18356/6a8968ef-en · Cited by 487

Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective

Gassert, R., & Dietz, V. (2018) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-018-0383-x · Cited by 464

Impact of the COVID-19 Pandemic on Older Adults: Rapid Review

Lebrasseur, A., Fortin-Bédard, N., Lettre, J., Raymond, É., Bussières, È.-L., Lapierre, N., Faieta, J. M., Vincent, C., Duchesne, L., Ouellet, M., et al. (2021) · JMIR Aging · doi:10.2196/26474 · Cited by 445

Pathophysiology of Hypertensive Renal Damage

BIDANI, A. K., & Griffin, K. A. (2004) · Hypertension · doi:10.1161/01.hyp.0000145180.38707.84 · Cited by 433

The Effect of Long Working Hours and Overtime on Occupational Health: A Meta-Analysis of Evidence from 1998 to 2018

Wong, K., Chan, A. H. S., & Ngan, S. C. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16122102 · Cited by 427

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

Stavropoulos, T. G., Papastergiou, A., Mpaltadoros, L., Nikolopoulos, S., & Kompatsiaris, I. Y. (2020) · Sensors · doi:10.3390/s20102826 · Cited by 385

Content Analysis of Mobile Health Applications on Diabetes Mellitus

Izahar, S., Lean, Q. Y., Hameed, M. A., Murugiah, M. K., Patel, R. P., Al‐Worafi, Y. M., Wong, T. W., & Ming, L. C. (2017) · Frontiers in Endocrinology · doi:10.3389/fendo.2017.00318 · Cited by 381

Relationship between the gut microbiome and brain function

Mohajeri, M. H., La Fata, G., Steinert, R. E., & Weber, P. (2018) · Nutrition Reviews · doi:10.1093/nutrit/nuy009 · Cited by 376

Vitamin D and intestinal calcium absorption

Christakos, S., Dhawan, P., Porta, A., Mady, L. J., & Seth, T. (2011) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2011.05.038 · Cited by 373

Problems associated with glucose toxicity: Role of hyperglycemia-induced oxidative stress

Kawahito, S., Kitahata, H., & Oshita, S. (2009) · World Journal of Gastroenterology · doi:10.3748/wjg.15.4137 · Cited by 373

Re-examining rates of lithium-ion battery technology improvement and cost decline

Ziegler, M. S., & Trancik, J. E. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 361

Fall detection system using Kinect’s infrared sensor

Mastorakis, G., & Makris, D. (2012) · Journal of Real-Time Image Processing · doi:10.1007/s11554-012-0246-9 · Cited by 358

How dangerous are falls in old people at home?

Wild, D., Nayak, U. S., & Isaacs, B. (1981) · BMJ · doi:10.1136/bmj.282.6260.266 · Cited by 344

Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90

Fleming, J., & Brayne, C. (2008) · BMJ · doi:10.1136/bmj.a2227 · Cited by 324

The scope and impact of mobile health clinics in the United States: a literature review

Yu, S. W. Y., Hill, C., Ricks, M. L., Bennet, J., & Oriol, N. E. (2017) · International Journal for Equity in Health · doi:10.1186/s12939-017-0671-2 · Cited by 321

Machine Learning for Healthcare Wearable Devices: The Big Picture

Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321

The state of diabetes treatment coverage in 55 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 680 102 adults

Flood, D., Seiglie, J. A., Dunn, M. R., Tschida, S., Theilmann, M., Marcus, M. E., Brian, G. R., Норов, Б., Mayige, M. T., Gurung, M. S., et al. (2021) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(21)00089-1 · Cited by 304

The depressive spectrum: diagnostic classification and course

Angst, J., & Merikangas, K. (1997) · Journal of Affective Disorders · doi:10.1016/s0165-0327(97)00057-8 · Cited by 302

Edge and fog computing for IoT: A survey on current research activities & future directions

Laroui, M., Nour, B., Moungla, H., Cherif, M. A., Afifi, H., & Guizani, M. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.09.003 · Cited by 290

Comorbidity of depression with other medical diseases in the elderly

Krishnan, K. R., DeLong, M. R., KRAEMER, H. C., Carney, R. M., Spiegel, D., Gordon, C. J., McDonald, W. M., Dew, M. A., Alexopoulos, G. S., Buckwalter, K., et al. (2002) · Biological Psychiatry · doi:10.1016/s0006-3223(02)01472-5 · Cited by 289

Depth-Based Human Fall Detection via Shape Features and Improved Extreme Learning Machine

Ma, X., Wang, H., Xue, B., Zhou, M., Ji, B., & Li, Y. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2304357 · Cited by 286

Blockchain-based Personal Health Data Sharing System Using Cloud Storage

Zheng, X., Mukkamala, R. R., Vatrapu, R., & Ordieres-Mere, J. (2018) · 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531125 · Cited by 281

On-Shoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson's Disease

Mariani, B., Jiménez, M. C., Vingerhoets, F. J. G., & Aminian, K. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2227317 · Cited by 277

Disability Incidence and Functional Decline among Older Adults with Major Chronic Diseases

Fong, J. H. Y. (2019) · BMC Geriatrics · doi:10.1186/s12877-019-1348-z · Cited by 274

Exploring the ambient assisted living domain: a systematic review

Calvaresi, D., Cesarini, D., Sernani, P., Marinoni, M., Dragoni, A. F., & Sturm, A. (2016) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-016-0374-3 · Cited by 267

Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review

Talal, M., Zaidan, A. A., Zaidan, B. B., Albahri, A. S., Alamoodi, A. H., Albahri, O. S., Alsalem, M. A., Lim, C. K., Tan, K. L., Shir, W. L., et al. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1158-z · Cited by 260

The IoT-based heart disease monitoring system for pervasive healthcare service

Li, C., Hu, X., & Zhang, L. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.265 · Cited by 255

The changing relation between mortality and level of economic development

Preston, S. H. (2007) · International Journal of Epidemiology · doi:10.1093/ije/dym075 · Cited by 251

Application of telemedicine and eHealth technology for clinical services in response to COVID‑19 pandemic

Anthony, B. (2021) · Health and Technology · doi:10.1007/s12553-020-00516-4 · Cited by 251

Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: a survey, current challenges and future directions

Krishnamoorthy, S., Dua, A., & Gupta, S. R. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03302-w · Cited by 250

Management of Hypertension in the Digital Era

Kario, K. (2020) · Hypertension · doi:10.1161/hypertensionaha.120.14742 · Cited by 247

HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

Tong, L., Song, Q., Ge, Y., & Liu, M. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2245231 · Cited by 238

A Floor Sensor System for Gait Recognition

Middleton, L., Buss, A., Bazin, A., et al. (2006) · IEEE Workshop on Automatic Identification Advanced Technologies (AutoID) · doi:10.1109/autoid.2005.2 · Cited by 228

Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark

Vestergaard, P., Rejnmark, L., & Mosekilde, L. (2004) · Osteoporosis International · doi:10.1007/s00198-004-1680-8 · Cited by 228

Telemedicine in India: Where do we stand?

Chellaiyan, V. G., Nirupama, A. Y., & Taneja, N. (2019) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_264_19 · Cited by 220

A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living

Baig, M. M., Afifi, S. M., GholamHosseini, H., & Mirza, F. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1365-7 · Cited by 216

From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems

Chaccour, K., Darazi, R., Hassani, A. H. E., & Andrès, E. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2628099 · Cited by 215

Internet of Things (IoT): Definitions, Challenges and Recent Research Directions

Zainab, H. E., Ali, H., & Mahmoud, M. S. (2015) · International Journal of Computer Applications · doi:10.5120/ijca2015906430 · Cited by 215

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review

Qi, J., Yang, P., Waraich, A., Deng, Z., Zhao, Y., & Yang, Y. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.09.002 · Cited by 206

Recent directions in telemedicine: Review of trends in research and practice

Wilson, L. S., & Maeder, A. J. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.4.213 · Cited by 203

Living Alone, Loneliness, and Psychological Well-Being of Older Persons in Singapore

Lim, L. L., & Kua, E. H. (2011) · Current Gerontology and Geriatrics Research · doi:10.1155/2011/673181 · Cited by 200

Evaluation of an Assistive Telepresence Robot for Elderly Healthcare

Koceski, S., & Koceska, N. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0481-x · Cited by 195

An implementation of IoT for healthcare

Kodali, R. K., Swamy, G., & Lakshmi, B. (2015) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics.2015.7488451 · Cited by 195

The double burden of diabetes and global infection in low and middle-income countries

Dunachie, S. J., & Chamnan, P. (2018) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1093/trstmh/try124 · Cited by 195

Monitoring patients via a secure and mobile healthcare system

Ren, Y., Werner, R. P., Pazzi, N., & Boukerche, A. (2010) · IEEE Wireless Communications · doi:10.1109/mwc.2010.5416351 · Cited by 194

5G and intelligence medicine—how the next generation of wireless technology will reconstruct healthcare?

Li, D. (2019) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbz020 · Cited by 191

Telemedicine: A new horizon in public health in India

Dasgupta, A., & Deb, S. (2008) · Indian Journal of Community Medicine · doi:10.4103/0970-0218.39234 · Cited by 187

An overview of health forecasting

Soyiri, I. N., & Reidpath, D. D. (2012) · Environmental Health and Preventive Medicine · doi:10.1007/s12199-012-0294-6 · Cited by 181

Causes and prognosis of delirium in elderly patients admitted to a district general hospital

George, J. M., Bleasdale, S., & SINGLETON, S. J. (1997) · Age and Ageing · doi:10.1093/ageing/26.6.423 · Cited by 181

An evolutionary perspective on human physical activity: implications for health

Eaton, S. B., & Eaton, S. B. (2003) · Comparative Biochemistry and Physiology Part A Molecular & Integrative Physiology · doi:10.1016/s1095-6433(03)00208-3 · Cited by 174

Patients' and caregivers' experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study

Lawton, J., Blackburn, M., Allen, J., Campbell, F., Elleri, D., Leelarathna, L., Rankin, D., Tauschmann, M., Thabit, H., & Hovorka, R. (2018) · BMC Endocrine Disorders · doi:10.1186/s12902-018-0239-1 · Cited by 174

Challenges Experienced by Older People During the Initial Months of the COVID-19 Pandemic

Heid, A. R., Cartwright, F. P., Wilson-Genderson, M., & Pruchno, R. A. (2020) · The Gerontologist · doi:10.1093/geront/gnaa138 · Cited by 168

Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis

Valenza, G., Nardelli, M., Lanatà, A., Gentili, C., Bertschy, G., Paradiso, R., & Scilingo, E. P. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2290382 · Cited by 166

Smartphones in mental health: a critical review of background issues, current status and future concerns

Bauer, M., Glenn, T., Geddes, J., Gitlin, M., Grof, P., Kessing, L. V., Monteith, S., Faurholt-Jepsen, M., Severus, E., & Whybrow, P. C. (2020) · International Journal of Bipolar Disorders · doi:10.1186/s40345-019-0164-x · Cited by 163

Infrared gait recognition based on wavelet transform and support vector machine

Xue, Z., Ming, D., Song, W. G., Wan, B., & Jin, S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.03.011 · Cited by 155

Nutrition and immunity in the elderly

Lesourd, B., & Mazari, L. (1999) · Proceedings of The Nutrition Society · doi:10.1017/s0029665199000907 · Cited by 154

A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

Chelli, A., & Pätzold, M. (2019) · IEEE Access · doi:10.1109/access.2019.2906693 · Cited by 154

Evidence and Mechanisms of Fat Depletion in Cancer

Ebadi, M., & Mazurak, V. C. (2014) · Nutrients · doi:10.3390/nu6115280 · Cited by 152

The Misdiagnosis of Delirium

Armstrong, S. C., Cozza, K. L., & Watanabe, K. S. (1997) · Psychosomatics · doi:10.1016/s0033-3182(97)71420-8 · Cited by 143

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Soon, S., Svavarsdottir, H. S., Downey, C. L., & Jayne, D. G. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000354 · Cited by 137

Technical and Clinical Progress in Telemedicine

Strode, S. W. (1999) · JAMA · doi:10.1001/jama.281.12.1066 · Cited by 136

Anxiety and agitation in mechanically ventilated patients

Tate, J. A., Devito Dabbs, A., Hoffman, L. A., Milbrandt, E., & Happ, M. B. (2012) · Qualitative Health Research · doi:10.1177/1049732311421616 · Cited by 136

Review on Smart Electro-Clothing Systems (SeCSs)

Sayem, A. S. M., Teay, S. H., Shahariar, H., Fink, P. L., & Albarbar, A. (2020) · Sensors · doi:10.3390/s20030587 · Cited by 133

Accelerometer-based fall detection for smartphones

de Aguiar, B. G. M., Rocha, T., da Silva, J. K. A., et al. (2014) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2014.6860110 · Cited by 131

An IoT System for Remote Health Monitoring in Elderly Adults through a Wearable Device and Mobile Application

Durán-Vega, L. A., Santana-Mancilla, P. C., Buenrostro‐Mariscal, R., Contreras-Castillo, J. J., Anido, L., García-Ruiz, M. Á., Montesinos-López, O. A., & Estrada-González, F. (2019) · Geriatrics · doi:10.3390/geriatrics4020034 · Cited by 128

IOT AND AI IN HEALTHCARE: A SYSTEMATIC LITERATURE REVIEW

(2018) · Issues in Information Systems · doi:10.48009/3_iis_2018_33-41 · Cited by 125

A Healthcare Integration System for Disease Assessment and Safety Monitoring of Dementia Patients

Lin, C., Lin, P.-Y., Lu, P.-K., Hsieh, G.-Y., Lee, W., & Lee, R.-G. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.917914 · Cited by 124

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Chang, T., Li, H., Zhang, N., Jiang, X., Yu, X., Yang, Q., Jin, Z., Meng, H., & Chang, L. (2022) · Microsystems & Nanoengineering · doi:10.1038/s41378-022-00355-5 · Cited by 115

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Faedda, G. L., Ohashi, K., Hernandez, M., McGreenery, C. E., Grant, M. C., Baroni, A., Polcari, A., & Teicher, M. H. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12520 · Cited by 111

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Jones, M. L., Morris, J., & DeRuyter, F. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15030515 · Cited by 109

Delirium phenomenology: What can we learn from the symptoms of delirium?

Gupta, N., de Jonghe, J. F. M., Schieveld, J. N. M., Leonard, M. M., & Meagher, D. J. (2008) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2008.05.020 · Cited by 108

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Rosen, T., Mack, K. A., & Noonan, R. K. (2012) · Journal of Injury and Violence Research · doi:10.5249/jivr.v5i1.177 · Cited by 108

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Ahmadi, V., Benjelloun, S., Kik, M. E., Sharma, T., Chi, H., & Zhou, W. (2020) · International Conference on Mobile and Secure Services (MobiSecServ) · doi:10.1109/mobisecserv48690.2020.9042950 · Cited by 108

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Morone, G., Cocchi, I., Paolucci, S., & Iosa, M. (2020) · Expert Review of Medical Devices · doi:10.1080/17434440.2020.1733408 · Cited by 106

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Yang, L., Ren, Y., & Zhang, W. (2016) · Digital Communications and Networks · doi:10.1016/j.dcan.2015.12.001 · Cited by 104

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Fang, H. S. A., Tan, T. H., Tan, C. Y. F., & Tan, M. C. J. (2021) · Journal of Medical Internet Research · doi:10.2196/25094 · Cited by 104

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Vallabh, P., Malekian, R., Ye, N., et al. (2016) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.1109/softcom.2016.7772142 · Cited by 103

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Ibrahim, M. M. (2018) · Current Hypertension Reports · doi:10.1007/s11906-018-0839-1 · Cited by 98

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Belikov, A. V. (2018) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.002 · Cited by 95

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Leroux, A., Lynn, R. R., Crainiceanu, C., & Sharma, T. (2021) · Digital Biomarkers · doi:10.1159/000515576 · Cited by 94

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Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A. O., Asghari, P., Souri, A., Mazaherinezhad, A., Bohlouli, M., & Rawassizadeh, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09049-4 · Cited by 91

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Najafi, B., Khan, T., & Wrobel, J. S. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091605 · Cited by 88

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Ali, H., Bensaali, F., & Jaber, F. T. (2017) · IEEE Access · doi:10.1109/access.2017.2707384 · Cited by 87

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Sharma, A., Kaur, J., & Singh, I. (2020) · SN Computer Science · doi:10.1007/s42979-020-00248-2 · Cited by 87

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Hilty, D. M., Chan, S. R., Hwang, T., Wong, A., & Bauer, A. M. (2017) · mHealth · doi:10.21037/mhealth.2017.06.02 · Cited by 86

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Almeida, L., Menezes, P. J., & Dias, J. M. M. (2022) · Applied Sciences · doi:10.3390/app12115557 · Cited by 84

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Sun, J. D., Ren, L., Wang, S., & Yao, X. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239946 · Cited by 83

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Braybrook, M., O'Connor, S. A., Churchward, P., Perera, T., Farzanehfar, P., & Horne, M. K. (2016) · Journal of Parkinson s Disease · doi:10.3233/jpd-160898 · Cited by 80

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Enshaeifar, S., Barnaghi, P. M., Skillman, S., Markides, A., Elsaleh, T., Acton, S. T., Nilforooshan, R., & Rostill, H. (2018) · IEEE Internet Computing · doi:10.1109/mic.2018.112102418 · Cited by 78

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Ojo, A., & Adebayo, S. (2017) · Public Administration and Information Technology · doi:10.1007/978-3-319-63743-3_11 · Cited by 78

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O'Brien, J. T., Gallagher, P., Stow, D., Hammerla, N., Ploetz, T., Firbank, M., Ladha, C., Ladha, K., Jackson, D., McNaney, R., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291716002166 · Cited by 76

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Jetter, M., Laudage, S., & Stadelmann, D. (2016) · Social Science Quarterly · doi:10.1111/ssqu.12638 · Cited by 74

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Xiao, Y., Xu, B., Jiang, W., & Wu, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/13556 · Cited by 71

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Oskouei, R. J., MousaviLou, Z., Bakhtiari, Z., & Jalbani, K. B. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/8822598 · Cited by 65

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Orlandini, A., Kristoffersson, A., Almquist, L., Björkman, P., Cesta, A., Cortellessa, G., Galindo, C., González-Jiménez, J., Gustafsson, K., Kiselev, A., et al. (2016) · PRESENCE Virtual and Augmented Reality · doi:10.1162/pres_a_00262 · Cited by 62

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Sivaparthipan, C. B., Muthu, B., Manogaran, G., Maram, B., Sundarasekar, R., Krishnamoorthy, S., Hsu, C., & Chandran, K. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3838 · Cited by 60

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Kase, Y., Shimazaki, T., & Okano, H. (2020) · Inflammation and Regeneration · doi:10.1186/s41232-020-00122-x · Cited by 51

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Suganyadevi, S., Shamia, D., & Balasamy, K. (2021) · doi:10.1002/9781119792253.ch8 · Cited by 51

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Mobed, A., Razavi, S., Ahmadalipour, A., et al. (2021) · Clinica Chimica Acta · doi:10.1016/j.cca.2021.03.009 · Cited by 49

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Koyama, A., Hashimoto, M., Tanaka, H., Fujise, N., Matsushita, M., Miyagawa, Y., Hatada, Y., Fukuhara, R., Hasegawa, N., Todani, S., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157053 · Cited by 48

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Park, S. J., Subramaniyam, M., Kim, S. E., et al. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-41652-6_29 · Cited by 45

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Smith, B. (2008) · Computer · doi:10.1109/mc.2008.142 · Cited by 44

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Acerbi, G., Rovini, E., Betti, S., Tirri, A., Rónai, J. F., Sirianni, A., Agrimi, J., Eusebi, L., & Cavallo, F. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-54283-6_3 · Cited by 44

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Miramontes, R., Aquino-Santos, R., Flores, A., et al. (2017) · Sensors · doi:10.3390/s17010176 · Cited by 43

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Aljehani, S., Alhazmi, R. A., Aloufi, S. S., et al. (2018) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/cais.2018.8442010 · Cited by 43

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Tetteh, J., Fordjour, G., Ekem‐Ferguson, G., Yawson, A. O., Boima, V., Entsua-Mensah, K., Biritwum, R. B., Essuman, A., Mensah, G., & Yawson, A. E. (2020) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2020-000492 · Cited by 43

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Menon, K. A. U., Hemachandran, D., & Abhishek, T. K. (2013) · doi:10.1109/iccsp.2013.6577220 · Cited by 41

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Uday, S., Jyotsna, C., & Amudha, J. (2018) · International Conference Inventive Communication and Computational Technologies · doi:10.1109/icicct.2018.8473010 · Cited by 41

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Gebretsadik, M., Jayaprabhu, S., & Grossberg, G. T. (2006) · Medical Clinics of North America · doi:10.1016/j.mcna.2006.05.015 · Cited by 39

Posttraumatic stress symptoms in older adults hospitalized for fall injury

Jayasinghe, N., Sparks, M. A., Kato, K., Wyka, K., Wilbur, K. L., Chiaramonte, G. R., Barie, P. S., Lachs, M. S., O’Dell, M. W., Evans, A. T., et al. (2014) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2014.08.003 · Cited by 39

Hypertensive Target Organ Damage and Longitudinal Changes in Brain Structure and Function

van der Veen, P. H., Geerlings, M. I., Visseren, F. L. J., et al. (2015) · Hypertension · doi:10.1161/hypertensionaha.115.06268 · Cited by 39

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Gupta, A., Srivastava, R., Gupta, H., et al. (2020) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon50219.2020.9376569 · Cited by 36

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Schultz, T. (2010) · Journal of African Economies · doi:10.1093/jae/ejq015 · Cited by 35

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Ali, M., Albasha, L., & Al-Nashash, H. A. (2011) · European Microwave Conference (EuMC) · doi:10.23919/eumc.2011.6101909 · Cited by 34

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Páez, D. G., Aparicio, F., Rodríguez, M. D. B., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13102-3_68 · Cited by 33

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Liu, G., Su, Y., Liu, Y., et al. (2016) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2016/8273716 · Cited by 33

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Cohen, S., Bataille, L., & Martig, A. K. (2016) · mHealth · doi:10.21037/mhealth.2016.04.02 · Cited by 33

Heartbeat Complexity Modulation in Bipolar Disorder during Daytime and Nighttime

Nardelli, M., Lanatà, A., Bertschy, G., Scilingo, E. P., & Valenza, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-18036-z · Cited by 33

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Chen, Y., Li, P., & Yang, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17093265 · Cited by 33

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Brodie, M. A. D., Lovell, N. H., Canning, C. G., Menz, H. B., Delbaere, K., Redmond, S. J., Latt, M. D., Sturnieks, D. L., Menant, J. C., Smith, S., et al. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944988 · Cited by 32

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Schwartz, S. S., Richardson, J. R. J., & Glasziou, P. (1993) · Australian Journal of Public Health · doi:10.1111/j.1753-6405.1993.tb00149.x · Cited by 32

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Bogue, R. W. (2013) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-07-2013-372 · Cited by 32

Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study

Gentili, C., Valenza, G., Nardelli, M., Lanatà, A., Bertschy, G., Weiner, L., Mauri, M., Scilingo, E. P., & Pietrini, P. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.11.008 · Cited by 32

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Bazgir, O., Frounchi, J., Habibi, S. A. H., et al. (2015) · Iranian Conference on Biomedical Engineering · doi:10.1109/icbme.2015.7404105 · Cited by 31

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Mezghani, N., Ouakrim, Y., Islam, M. R., Yared, R., & Abdulrazak, B. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897308 · Cited by 31

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Tousignant, M., Boissy, P., Corriveau, H., Moffet, H., & Cabana, F. (2009) · International Journal of Telerehabilitation · doi:10.5195/ijt.2009.5997 · Cited by 30

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Kamdar, N. V., & Jalilian, L. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004513 · Cited by 30

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Spoladore, D., Arlati, S., & Sacco, M. (2017) · Mobile Information Systems · doi:10.1155/2017/3185481 · Cited by 29

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Goswami, S. A., Padhya, B. P., & Patel, K. D. (2019) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac47947.2019.9032474 · Cited by 29

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Singh, G., & Chawla, S. (2006) · Medical Journal Armed Forces India · doi:10.1016/s0377-1237(06)80151-6 · Cited by 28

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Nave, C., & Postolache, O. A. (2018) · International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI) · doi:10.1109/issi.2018.8538210 · Cited by 28

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Knippenberg, E., Timmermans, A. A. A., Palmaers, S., & Spooren, A. I. F. (2021) · BMC Geriatrics · doi:10.1186/s12877-021-02021-3 · Cited by 28

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López–de–Ipiña, D., Blanco, S., Laiseca, X., et al. (2011) · Atlantis ambient and pervasive intelligence · doi:10.2991/978-94-91216-05-3_5 · Cited by 27

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Saraubon, K., Anurugsa, K., & Kongsakpaibul, A. (2018) · International Conference on Software and e-Business (ICSEB) · doi:10.1145/3301761.3301769 · Cited by 27

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Korzun, D. G., Borodin, A. V., Paramonov, I. V., et al. (2015) · International Journal of Embedded and Real-Time Communication Systems · doi:10.4018/ijertcs.2015010101 · Cited by 26

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Chatterjee, S., & Roy, S. (2021) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2021.04.021 · Cited by 26

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Vukićević, S., Stamenković, Z., Murugesan, S., et al. (2016) · Facta universitatis - series Electronics and Energetics · doi:10.2298/fuee1603395v · Cited by 24

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Ha, V. K. L., Chai, R., & Nguyen, H. T. (2020) · Applied Sciences · doi:10.3390/app10010369 · Cited by 24

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Kauw, D., Huisma, P. R., Medlock, S. K., Koole, M. A. C., Wierda, E., Abu–Hanna, A., Schijven, M. P., Mulder, B. J. M., Bouma, B. J., Winter, M. M., et al. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000418 · Cited by 24

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Marcus, E., Rudensky, B., & Sonnenblick, M. (1992) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1992.tb02010.x · Cited by 22

Global geriatric evaluation is feasible during interactive telemedicine in nursing homes

Salles, N., Lafargue, A., Cressot, V., Glénisson, L., Barateau, M., Thiel, E., Doutre, M. ‐., Bourdin, N., Prigent, A.-M., Libert, K., et al. (2017) · European Research in Telemedicine / La Recherche Européenne en Télémédecine · doi:10.1016/j.eurtel.2017.06.002 · Cited by 22

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Ferré, X., Villalba‐Mora, E., Mora, M. Á. C., Sanchez, A., Aguilera, W., García-Grossocordón, N., Nuñez-Jimenez, L., Rodríguez‐Mañas, L., Liu, Q., & del Pozo, F. (2017) · Mobile Information Systems · doi:10.1155/2017/1310345 · Cited by 22

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Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228

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Hawkins, D. (2017)

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Jeong, S.-K., & Ahn, J. (2022) · Journal of the Korea Academia-Industrial cooperation Society · doi:10.5762/kais.2022.23.11.303

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(2020) · doi:10.3403/30419733 · Cited by 3

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겸임교수, 백. 디. 산., & Hongtaek Yoo (2015) · Journal of Cultural Product and Design · doi:10.18555/kicpd.2015.43.165 · Cited by 1

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Hu, S. (2000) · Modern Computer