Explore the Ideal Human Body Part for Medical Wearable Sensors

Abstract

Wearing wearable devices has become a part of daily life for many people worldwide. Wearables provide various applications, including health monitoring, diagnosis, treatment, and rehabilitation. Since wearable devices are in close contact with the human body, considering human factors aspects during the design and development stages is essential for the success of future wearables. This research aims to assess the optimal body part for medical wearable sensors’ placement based on the wearable’s expert opinion questionnaire. The study focused on four main categories: cardiovascular monitoring, neuromuscular monitoring, biofluids, and gait disorders, considering three placement criteria: comfort, accuracy, and simplicity. Corresponding to the findings, there is a gap between the recent research outcomes and the experts’ answers. In particular, the answers to the questions related to the placements of wearable sensors for neurological and gait disorders were limited to the traditional clinical diagnosis techniques. This could be attributed to insufficient knowledge and collaboration between engineers and medical professionals. This highlights the need for a systematic way to evaluate the wearability of wearables based on their performance alongside wearability criteria by establishing a wearability assessment human-centric framework for each wearable sensor application supported by clinical studies and experimental data, which could reduce the time for development and commercialization of accurate, reliable, and comfortable wearable medical devices.

Keywords

medical wearables / cardiovascular diseases / neurological disorders / wearable sensors

References

[1] Erboz, G. (2017). How to define Industry 4.0: The main pillars of Industry 4.0. Managerial Trends in the Development of Enterprises in Globalization Era, 761(2), 761-767.

[2] Sahoo, S., & Lo, C.—Y. (2022). Smart manufacturing powered by recent technological advancements: A review. Journal of Manufacturing Systems, 64, 236-250. https://doi.org/10.1016/j.jmsy.2022.06.008

[3] Dian, F. J., Vahidnia, R., & Rahmati, A. (2020). Wearables and the Internet of Things (IoT), applications, opportunities, and challenges: A survey. IEEE Access, 8, 69200-69211. https://doi.org/10.1109/ACCESS.2020.2986329

[4] Mahamuni, C. V. (2023). Design and simulation of microstrip patch antennas with textile dielectrics for body sensor networks (BSNs) and wearable IoT applications. In Advances in Data Science and Computing Technologies: Select Proceedings of ADSC 2022, 511-522. https://doi.org/10.1007/978-981-99-3656-4_52

[5] Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., ..., & Seneviratne, A. (2017). A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials, 19(4), 2573-2620. https://doi.org/10.1109/COMST.2017.2731979

[6] Ometov, A., Shubina, V., Klus, L., Skibińska, J., Saafi, S., Pascacio, P., ..., & Lohan, E. S. (2021). A survey on wearable technology: History, state—of—the—art and current challenges. Computer Networks, 193, 108074. https://doi.org/10.1016/j.comnet.2021.108074

[7] Al—Daraghmeh, M. Y., & Stone, R. T. (2023). A review of medical wearables: Materials, power sources, sensors, and manufacturing aspects of human wearable technologies. Journal of Medical Engineering & Technology, 47(1), 67-81. https://doi.org/10.1080/03091902.2022.2097743

[8] Sarhaddi, F., Kazemi, K., Azimi, I., Cao, R., Niela—Vilén, H., Axelin, A., ..., & Rahmani, A. M. (2022). A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability. PLOS ONE, 17(12), e0268361. https://doi.org/10.1371/journal.pone.0268361

[9] Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C., & Nazeran, H. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics, 4(4), 195-202. https://doi.org/10.15406/ijbsbe.2018.04.00125

[10] Zhang, Y., Liu, B., & Zhang, Z. (2015). Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist—type photoplethysmography. Biomedical Signal Processing and Control, 21, 119-125. https://doi.org/10.1016/j.bspc.2015.05.006

[11] Ferlini, A., Montanari, A., Min, C., Li, H., Sassi, U., & Kawsar, F. (2022). In—ear PPG for vital signs. IEEE Pervasive Computing, 21(1), 65-74. https://doi.org/10.1109/MPRV.2021.3121171

[12] Shcherbina, A., Mattsson, C., Waggott, D., Salisbury, H., Christle, J., Hastie, T., ..., & Ashley, E. (2017). Accuracy in wrist—worn, sensor—based measurements of heart rate and energy expenditure in a diverse cohort. Journal of Personalized Medicine, 7(2), 3. https://doi.org/10.3390/jpm7020003

[13] Lee, B., Hwang, S., & Kim, H. (2021). The feasibility of information—entropy—based behavioral analysis for detecting environmental barriers. International Journal of Environmental Research and Public Health, 18(21), 11727. https://doi.org/10.3390/ijerph182111727

[14] Carcassi, G., Aidala, C. A., & Barbour, J. (2021). Variability as a better characterization of Shannon entropy. European Journal of Physics, 42(4), 045102. https://doi.org/10.1088/1361-6404/abe361

[15] Dobbs, W. C., Fedewa, M. V., MacDonald, H. V., Holmes, C. J., Cicone, Z. S., Plews, D. J., & Esco, M. R. (2019). The accuracy of acquiring heart rate variability from portable devices: A systematic review and meta—analysis. Sports Medicine, 49(3), 417-435. https://doi.org/10.1007/s40279-019-01061-5

[16] Nelson, B. W., & Allen, N. B. (2019). Accuracy of consumer wearable heart rate measurement during an ecologically valid 24—hour period: Intraindividual validation study. JMIR mHealth and uHealth, 7(3), e10828. https://doi.org/10.2196/10828

[17] Schoot, T. S., Weenk, M., van de Belt, T. H., Engelen, L. J., van Goor, H., & Bredie, S. J. (2016). A new cuffless device for measuring blood pressure: A real—life validation study. Journal of Medical Internet Research, 18(5), e85. https://doi.org/10.2196/jmir.5414

[18] Kumar, S., Yadav, S., & Kumar, A. (2024). Blood pressure measurement techniques, standards, technologies, and the latest futuristic wearable cuff—less know—how. Sensors & Diagnostics, 3(2), 181-202. https://doi.org/10.1039/D3SD00201B

[19] Arakawa, T. (2018). Recent research and developing trends of wearable sensors for detecting blood pressure. Sensors, 18(9), 2772. https://doi.org/10.3390/s18092772

[20] Kuwabara, M., Harada, K., Hishiki, Y., & Kario, K. (2019). Validation of two watch—type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060—2:2013 guidelines: Omron HEM—6410T—ZM and HEM—6410T—ZL. The Journal of Clinical Hypertension, 21(6), 853-858. https://doi.org/10.1111/jch.13499

[21] Lunardi, M., Muhammad, F., Shahzad, A., Nadeem, A., Combe, L., Simpkin, A. J., ..., & McEvoy, J. W. (2024). Performance of wearable watch—type home blood pressure measurement devices in a real—world clinical sample. Clinical Research in Cardiology, 113(10), 1393-1404. https://doi.org/10.1007/s00392-023-02353-7

[22] Heydari, F., Ebrahim, M. P., Redoute, J.—M., Joe, K., Walker, K., & Yuce, M. Y. (2020). A chest—based continuous cuffless blood pressure method: Estimation and evaluation using multiple body sensors. Information Fusion, 54, 119-127. https://doi.org/10.1016/j.inffus.2019.07.001

[23] Shenoy, N., Luchtel, R., & Gulani, P. (2020). Considerations for target oxygen saturation in COVID—19 patients: Are we under—shooting? BMC Medicine, 18(1), 260. https://doi.org/10.1186/s12916-020-01735-2

[24] Ali, M. M., Haxha, S., Alam, M. M., Nwibor, C., & Sakel, M. (2020). Design of Internet of Things (IoT) and android based low cost health monitoring embedded system wearable sensor for measuring SpO₂, heart rate and body temperature simultaneously . Wireless Personal Communications, 111(4), 2449-2463. https://doi.org/10.1007/s11277-019-06995-7

[25] Mirjalali, S., Peng, S., Fang, Z., Wang, C., & Wu, S. (2022). Wearable sensors for remote health monitoring: Potential applications for early diagnosis of Covid—19. Advanced Materials Technologies, 7(1), 2100545. https://doi.org/10.1002/admt.202100545

[26] Khan, Y., Han, D., Ting, J., Ahmed, M., Nagisetty, R., & Arias, A. C. (2019). Organic multi—channel optoelectronic sensors for wearable health monitoring. IEEE Access, 7, 128114-128124. https://doi.org/10.1109/ACCESS.2019.2939798

[27] Williams, G. J., Al—Baraikan, A., Rademakers, F. E., Ciravegna, F., van de Vosse, F. N., Lawrie, A., ..., & Morris, P. D. (2023). Wearable technology and the cardiovascular system: The future of patient assessment. The Lancet Digital Health, 5(7), e467-e476. https://doi.org/10.1016/S2589-7500(23)00087-0

[28] Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., ..., & Elshazly, M. B. (2021). Smart wearable devices in cardiovascular care: Where we are and how to move forward. Nature Reviews Cardiology, 18(8), 581-599. https://doi.org/10.1038/s41569-021-00522-7

[29] Esgalhado, F., Fernandes, B., Vassilenko, V., Batista, A., & Russo, S. (2021). The application of deep learning algorithms for PPG signal processing and classification. Computers, 10(12), 158. https://doi.org/10.3390/computers10120158

[30] Rundo, F., Conoci, S., Ortis, A., & Battiato, S. (2018). An advanced bio—inspired photoplethysmography (PPG) and ECG pattern recognition system for medical assessment. Sensors, 18(2), 405. https://doi.org/10.3390/s18020405

[31] Shao, H., & Chen, X. (2017). Motion artifact detection and reduction in PPG signals based on statistics analysis. In 2017 29th Chinese Control and Decision Conference, 3114-3119. https://doi.org/10.1109/CCDC.2017.7979043

[32] McKeown, M. J., Torpey, D. C., & Gehm, W. C. (2002). Non—invasive monitoring of functionally distinct muscle activations during swallowing. Clinical Neurophysiology, 113(3), 354-366. https://doi.org/10.1016/S1388-2457(02)00007-X

[33] Roudjane, M., Tam, S., Mascret, Q., Fall, C. L., Bielmann, M., Adriano Dorledo de Faria, R., ..., & Messaddeq, Y. (2019). Detection of neuromuscular activity using new non—invasive and flexible multimaterial fiber dry—electrodes. IEEE Sensors Journal, 19(23), 11624-11633. https://doi.org/10.1109/JSEN.2019.2933751

[34] Sethi, A., Ting, J., Allen, M., Clark, W., & Weber, D. (2020). Advances in motion and electromyography based wearable technology for upper extremity function rehabilitation: A review. Journal of Hand Therapy, 33(2), 180-187. https://doi.org/10.1016/j.jht.2019.12.021

[35] di Giminiani, R., Cardinale, M., Ferrari, M., & Quaresima, V. (2020). Validation of fabric—based thigh—wearable EMG sensors and oximetry for monitoring quadriceps activity during strength and endurance exercises. Sensors, 20(17), 4664. https://doi.org/10.3390/s20174664

[36] Jose, J. M., Jose, J. V., & Mahamuni, C. V. (2020). Multi—biosensor based Wireless Body Area Networks (WBAN) for critical health monitoring of patients in mental health care centers: An interdisciplinary study. International Journal of Research in Engineering, Science and Management, 3(8), 105-114.

[37] Zhou, M., Tian, C., Cao, R., Wang, B., Niu, Y., Hu, T., ..., & Xiang, J. (2018). Epileptic seizure detection based on EEG signals and CNN. Frontiers in Neuroinformatics, 12, 95. https://doi.org/10.3389/fninf.2018.00095

[38] van Andel, J., Thijs, R. D., de Weerd, A., Arends, J., & Leijten, F. (2016). Non—EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care? Epilepsy & Behavior, 57, 82-89. https://doi.org/10.1016/j.yebeh.2016.01.003

[39] Marathe, M. (2024). Inpatient experiences of long—term monitoring: The case of EEG. In Companion Publication of the 2024 Conference on Computer—Supported Cooperative Work and Social Computing, 511-518. https://doi.org/10.1145/3678884.3681899

[40] Kalitzin, S., Petkov, G., Velis, D., Vledder, B., & Lopes da Silva, F. (2012). Automatic segmentation of episodes containing epileptic clonic seizures in video sequences. IEEE Transactions on Biomedical Engineering, 59(12), 3379-3385. https://doi.org/10.1109/TBME.2012.2215609

[41] Stirling, R. E., Grayden, D. B., D’Souza, W., Cook, M. J., Nurse, E., Freestone, D. R., ..., & Karoly, P. J. (2021). Forecasting seizure likelihood with wearable technology. Frontiers in Neurology, 12, 704060. https://doi.org/10.3389/fneur.2021.704060

[42] Milosevic, B., Benatti, S., & Farella, E. (2017). Design challenges for wearable EMG applications. In Design, Automation & Test in Europe Conference & Exhibition, 1432-1437. https://doi.org/10.23919/DATE.2017.7927217

[43] Al—Ayyad, M., Owida, H. A., de Fazio, R., Al—Naami, B., & Visconti, P. (2023). Electromyography monitoring systems in rehabilitation: A review of clinical applications, wearable devices and signal acquisition methodologies. Electronics, 12(7), 1520. https://doi.org/10.3390/electronics12071520

[44] Vigersky, R., & Shrivastav, M. (2017). Role of continuous glucose monitoring for type 2 in diabetes management and research. Journal of Diabetes and Its Complications, 31(1), 280-287. https://doi.org/10.1016/j.jdiacomp.2016.10.007

[45] Dicembrini, I., Cosentino, C., Monami, M., Mannucci, E., & Pala, L. (2021). Effects of real—time continuous glucose monitoring in type 1 diabetes: A meta—analysis of randomized controlled trials. Acta Diabetologica, 58(4), 401-410. https://doi.org/10.1007/s00592-020-01589-3

[46] Klonoff, D. C., Ahn, D., & Drincic, A. (2017). Continuous glucose monitoring: A review of the technology and clinical use. Diabetes Research and Clinical Practice, 133, 178-192. https://doi.org/10.1016/j.diabres.2017.08.005

[47] Mian, Z., Hermayer, K. L., & Jenkins, A. (2019). Continuous glucose monitoring: Review of an innovation in diabetes management. The American Journal of the Medical Sciences, 358(5), 332-339. https://doi.org/10.1016/j.amjms.2019.07.003

[48] Saha, T., del Caño, R., Mahato, K., de la Paz, E., Chen, C., Ding, S., ..., & Wang, J. (2023). Wearable electrochemical glucose sensors in diabetes management: A comprehensive review. Chemical Reviews, 123(12), 7854-7889. https://doi.org/10.1021/acs.chemrev.3c00078

[49] Pullano, S. A., Greco, M., Bianco, M. G., Foti, D., Brunetti, A., & Fiorillo, A. S. (2022). Glucose biosensors in clinical practice: Principles, limits and perspectives of currently used devices. Theranostics, 12(2), 493-511. https://doi.org/10.7150/thno.64035

[50] Faccioli, S., del Favero, S., Visentin, R., Bonfanti, R., Iafusco, D., Rabbone, I., ..., & Cobelli, C. (2017). Accuracy of a CGM sensor in pediatric subjects with type 1 diabetes. Comparison of three insertion sites: Arm, abdomen, and gluteus. Journal of Diabetes Science and Technology, 11(6), 1147-1154. https://doi.org/10.1177/1932296817706377

[51] Wood, A., O’Neal, D., Furler, J., & Ekinci, E. I. (2018). Continuous glucose monitoring: A review of the evidence, opportunities for future use and ongoing challenges. Internal Medicine Journal, 48(5), 499-508. https://doi.org/10.1111/imj.13770

[52] Bariya, M., Nyein, H. Y. Y., & Javey, A. (2018). Wearable sweat sensors. Nature Electronics, 1(3), 160-171. https://doi.org/10.1038/s41928-018-0043-y

[53] Hossain, P. S., & Marasco, E. (2024). Capturing sweat essence: Exploring techniques for collection and metabolite applications. TechRxiv. https://doi.org/10.36227/techrxiv.172565659.91260897/v1

[54] Patle, S., & Rotake, D. (2024). Recent advances, technological challenges and requirements to predict the future treads in wearable sweat sensors: A critical review. Microchemical Journal, 200, 110457. https://doi.org/10.1016/j.microc.2024.110457

[55] Moonen, E. J. M., Haakma, J. R., Peri, E., Pelssers, E., Mischi, M., & den Toonder, J. M. J. (2020). Wearable sweat sensing for prolonged, semicontinuous, and nonobtrusive health monitoring. View, 1(4), 20200077. https://doi.org/10.1002/VIW.20200077

[56] Shajari, S., Salahandish, R., Zare, A., Hassani, M., Moossavi, S., Munro, E., ..., & Sanati Nezhad, A. (2023). MicroSweat: A wearable microfluidic patch for noninvasive and reliable sweat collection enables human stress monitoring. Advanced Science, 10(7), 2204171. https://doi.org/10.1002/advs.202204171

[57] D’Souza, B., Kasar, A. K., Jones, J., Skeete, A., Rader, L., Kumar, P., & Menezes, P. L. (2022). A brief review on factors affecting the tribological interaction between human skin and different textile materials. Materials, 15(6), 2184. https://doi.org/10.3390/ma15062184

[58] Yeung, K. K., Huang, T., Hua, Y., Zhang, K., Yuen, M. M. F., & Gao, Z. (2021). Recent advances in electrochemical sensors for wearable sweat monitoring: A review. IEEE Sensors Journal, 21(13), 14522-14539. https://doi.org/10.1109/JSEN.2021.3074311

[59] Ghaffari, R., Rogers, J. A., & Ray, T. R. (2021). Recent progress, challenges, and opportunities for wearable biochemical sensors for sweat analysis. Sensors and Actuators B: Chemical, 332, 129447. https://doi.org/10.1016/j.snb.2021.129447

[60] McLaren, R., Joseph, F., Baguley, C., & Taylor, D. (2016). A review of e—textiles in neurological rehabilitation: How close are we? Journal of NeuroEngineering and Rehabilitation, 13(1), 59. https://doi.org/10.1186/s12984-016-0167-0

[61] Sarkodie—Gyan, T., & Yu, H. (2023). Challenges and concerns to society: The human locomotor system. In T. Sarkodie—Gyan & H. Yu (Eds.), The human locomotor system: Physiological and technological foundations (pp. 99-122). Springer. https://doi.org/10.1007/978-3-031-32781-0_3

[62] Gu, C., Lin, W., He, X., Zhang, L., & Zhang, M. (2023). IMU—based motion capture system for rehabilitation applications: A systematic review. Biomimetic Intelligence and Robotics, 3(2), 100097. https://doi.org/10.1016/j.birob.2023.100097

[63] Homayounfar, S. Z., & Andrew, T. L. (2020). Wearable sensors for monitoring human motion: A review on mechanisms, materials, and challenges. SLAS Technology, 25(1), 9-24. https://doi.org/10.1177/2472630319891128

[64] Al—Lami, S. S., Salman, A. M., & Al—Janabi, A. (2023). Weaving—based wearable sensing instrument designed for the joint motion monitoring of the elbow and knee flexion angle. Optics Continuum, 2(9), 1967-1980. https://doi.org/10.1364/OPTCON.500786

[65] Zhang, R., Chen, L., Hu, Y., Zhang, Y., Chen, J., Liang, T., ..., & Wang, Q. (2024). Textile—sensing wearable systems for continuous motion angle estimation: A systematic review. International Journal of Human—Computer Interaction, 40(23), 7819-7839. https://doi.org/10.1080/10447318.2023.2276991

[66] Alam, T., Saidane, F., Faisal, A. A., Khan, A., & Hossain, G. (2022). Smart—textile strain sensor for human joint monitoring. Sensors and Actuators A: Physical, 341, 113587. https://doi.org/10.1016/j.sna.2022.113587

[67] Vaillancourt, D. E., & Newell, K. M. (2002). Changing complexity in human behavior and physiology through aging and disease. Neurobiology of Aging, 23(1), 1-11. https://doi.org/10.1016/S0197-4580(01)00247-0