Data acquisition and processing for IoT-based intelligent medical monitoring: Applications in biomechanics
Abstract
With the rapid development of Internet of Things (IoT) technology, its integration into intelligent medical monitoring devices has significantly transformed the healthcare landscape. This integration not only enhances the functionality of medical monitoring equipment but also improves the real-time accuracy of data collection. This review comprehensively discusses the data acquisition and processing methods of intelligent medical monitoring devices based on IoT, with a particular focus on their applications in molecular and cellular biomechanics. In the context of biomechanics, IoT technology offers new perspectives and tools for biomechanics research. By accurately monitoring mechanical changes at the cellular and molecular levels, IoT technology enhances our understanding of biological systems, thereby providing a scientific foundation for the early diagnosis and treatment of diseases. For instance, by observing the mechanical responses of cells, we can gain insights into how cells sense and react to changes in their external environment. We summarize the current research progress related to IoT data acquisition and processing methods for these devices, analyze the advantages and limitations of existing technologies, and explore future development trends. The review seeks to foster technological innovation and practical applications within this field, ultimately enhancing the quality of medical care and improving the overall quality of life for patients.
References
1. Sanislav T, Mois GD, Zeadally S, et al. Energy Harvesting Techniques for Internet of Things (IoT). IEEE Access. 2021; 9: 39530-39549. doi: 10.1109/access.2021.3064066
2. Islam SMR, Lloret J, Zikria YB. Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications. Electronics. 2021; 10(2): 148. doi: 10.3390/electronics10020148
3. Hussain AA, Dawood BA. A survey on IoT-cloud task in healthcare system. The Journal of Supercomputing. 2024; 81(1). doi: 10.1007/s11227-024-06629-1
4. Guk K, Han G, Lim J, et al. Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare. Nanomaterials. 2019; 9(6): 813. doi: 10.3390/nano9060813
5. Ota H, Chao M, Gao Y, et al. 3D Printed “Earable” Smart Devices for Real-Time Detection of Core Body Temperature. ACS Sensors. 2017; 2(7): 990-997. doi: 10.1021/acssensors.7b00247
6. Lebedinskii KM, Kovalenko AN, Kurapeev IS, et al. Physical and Physiological Problems of Medical Monitoring. Technical Physics. 2020; 65(9): 1343-1359. doi: 10.1134/s1063784220090212
7. Lu C, Wang X, Jia Q, et al. 3D printed mechanical robust cellulose derived liquid-free ionic conductive elastomer for multifunctional electronic devices. Carbohydrate Polymers. 2024; 324: 121496. doi: 10.1016/j.carbpol.2023.121496
8. Cereatti A, Gurchiek R, Mündermann A, et al. ISB recommendations on the definition, estimation, and reporting of joint kinematics in human motion analysis applications using wearable inertial measurement technology. Journal of Biomechanics. 2024; 173: 112225. doi: 10.1016/j.jbiomech.2024.112225
9. Yang L, Sinsurin K, Shen F, et al. Biomechanical changes in lower extremity in individuals with knee osteoarthritis in the past decade: A scoping review. Heliyon. 2024; 10(11): e32642. doi: 10.1016/j.heliyon.2024.e32642
10. Fandaros M, Kwok C, Wolf Z, et al. Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use. Cardiovascular Engineering and Technology. 2024; 15(5): 503-521. doi: 10.1007/s13239-024-00731-4
11. Zarei M. Portable biosensing devices for point-of-care diagnostics: Recent developments and applications. TrAC Trends in Analytical Chemistry. 2017; 91: 26-41. doi: 10.1016/j.trac.2017.04.001
12. Qin J. A design and optimization of CMOS ECG amplifier applied to medical monitoring system. Highlights in Science, Engineering and Technology. 2023; 32: 56-64. doi: 10.54097/hset.v32i.4939
13. Correia Pinheiro E, Postolache OA, Silva Girão P. Implementation of Compressed Sensing in Telecardiology Sensor Networks. International Journal of Telemedicine and Applications. 2010; 2010: 1-12. doi: 10.1155/2010/127639
14. Ahmadpour A, Yetisen AK, Tasoglu S. Piezoelectric Metamaterial Blood Pressure Sensor. ACS Applied Electronic Materials. 2023; 5(6): 3280-3290. doi: 10.1021/acsaelm.3c00344
15. Miao F, Liu ZD, Liu JK, et al. Multi-Sensor Fusion Approach for Cuff-Less Blood Pressure Measurement. IEEE Journal of Biomedical and Health Informatics. 2020; 24(1): 79-91. doi: 10.1109/jbhi.2019.2901724
16. Saffati G, Wiatrowski A, Khera M, et al. (136) Development and Evaluation of a Novel Penile Blood Oxygen Sensor for Assessing Tissue Oxygenation. The Journal of Sexual Medicine. 2024; 21(Supplement_1). doi: 10.1093/jsxmed/qdae001.130
17. Kannan Loganathan P, O’Shea JE, Harikumar C, et al. Effect of opaque wraps for pulse oximeter sensors: randomised cross-over trial. Archives of Disease in Childhood - Fetal and Neonatal Edition. 2020; 106(1): 57-61. doi: 10.1136/archdischild-2020-319049
18. Liu R, He L, Cao M, et al. Flexible Temperature Sensors. Frontiers in Chemistry. 2021; 9. doi: 10.3389/fchem.2021.539678
19. Litvinova O, Eitenberger M, Bilir A, et al. Patent analysis of digital sensors for continuous glucose monitoring. Frontiers in Public Health. 2023; 11. doi: 10.3389/fpubh.2023.1205903
20. Arman Kuzubasoglu B, Kursun Bahadir S. Flexible temperature sensors: A review. Sensors and Actuators A: Physical. 2020; 315: 112282. doi: 10.1016/j.sna.2020.112282
21. Nascimento RAS, Mulato M. Microelectronic sensor for continuous glucose monitoring. Applied Physics A. 2019; 125(3). doi: 10.1007/s00339-019-2455-6
22. An BW, Shin JH, Kim SY, et al. Smart Sensor Systems for Wearable Electronic Devices. Polymers. 2017; 9(8): 303. doi: 10.3390/polym9080303
23. Abbas N, Yu F, Fan Y. Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks. Applied Sciences. 2018; 8(3): 348. doi: 10.3390/app8030348
24. Jin J, Zhang C, Zhao J, et al. An adaptive bionic sensor: enhancing ankle joint tracking with high sensitivity and superior cushioning performance. Chemical Engineering Journal. 2024; 500: 157332. doi: 10.1016/j.cej.2024.157332
25. Justino CIL, Gomes AR, Freitas AC, et al. Graphene based sensors and biosensors. TrAC Trends in Analytical Chemistry. 2017; 91: 53-66. doi: 10.1016/j.trac.2017.04.003
26. Schroeder V, Savagatrup S, He M, et al. Carbon Nanotube Chemical Sensors. Chemical Reviews. 2018; 119(1): 599-663. doi: 10.1021/acs.chemrev.8b00340
27. Wu H, Chai S, Zhu L, et al. Wearable fiber-based visual strain sensors with high sensitivity and excellent cyclic stability for health monitoring and thermal management. Nano Energy. 2024; 131: 110300. doi: 10.1016/j.nanoen.2024.110300
28. Sripriya T, Juliette AA. Theoretical analysis and comparative study of assorted diaphragm primarily based Micro Electro Mechanical System (MEMS) optical pressure sensors. Expert Systems with Applications. 2024; 245: 122993. doi: 10.1016/j.eswa.2023.122993
29. Chen Y, Feng T, Li C, et al. Comprehensive and Robust Anti‐Jamming Dual‐Electrode Pair Sensor. Small. 2024; 20(51). doi: 10.1002/smll.202406739
30. Cao X, Jiang K. Design of intelligent terminal app for digital manufacturing technology based on virtual reality and wireless sensor network technology. The International Journal of Advanced Manufacturing Technology; 2024.
31. Khalili M, GholamHosseini H, Lowe A, et al. Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques. Medical & Biological Engineering & Computing. 2024; 62(12): 3599-3622. doi: 10.1007/s11517-024-03165-1
32. Jin Z, Yim W, Retout M, et al. Colorimetric sensing for translational applications: from colorants to mechanisms. Chemical Society Reviews. 2024; 53(15): 7681-7741. doi: 10.1039/d4cs00328d
33. Mei X, Ye D, Zhang F, et al. Implantable application of polymer‐based biosensors. Journal of Polymer Science. 2021; 60(3): 328-347. doi: 10.1002/pol.20210543
34. Baek S, Jo Y, Lee Y, et al. Design and Integration of Organic Printed Thin-Film Transistor-Based Soft Biosensors for Wearable Applications. ACS Applied Electronic Materials. 2024; 6(11): 7657-7678. doi: 10.1021/acsaelm.4c01632
35. Zampolli S, Elmi I, Bruschi P, et al. An ASIC-based system-in-package MEMS gas sensor with impedance spectroscopy readout and AI-enabled identification capabilities. Sensors and Actuators B: Chemical. 2025; 424: 136924. doi: 10.1016/j.snb.2024.136924
36. Cubells-Beltrán MD, Reig C, Madrenas J, et al. Integration of GMR Sensors with Different Technologies. Sensors. 2016; 16(6): 939. doi: 10.3390/s16060939
37. Zaras I, Sokal M, Jarczewska M. Studies on the ssDNA-Based Biosensor Regeneration and Miniaturization for Electrochemical Detection of miRNAs. Journal of The Electrochemical Society. 2024; 171(11): 117520. doi: 10.1149/1945-7111/ad91e5
38. Chen J, Li J, Li Y, et al. Design and Fabrication of a Miniaturized GMI Magnetic Sensor Based on Amorphous Wire by MEMS Technology. Sensors. 2018; 18(3): 732. doi: 10.3390/s18030732
39. Chatterjee B, Mohseni P, Sen S. Bioelectronic Sensor Nodes for the Internet of Bodies. Annual Review of Biomedical Engineering. 2023; 25(1): 101-129. doi: 10.1146/annurev-bioeng-110220-112448
40. Kwon SK, Kim JN, Byun HG, et al. Low-power and cost-effective readout circuit design for compact semiconductor gas sensor systems. Electrochemistry Communications. 2024; 169: 107834. doi: 10.1016/j.elecom.2024.107834
41. Rivera Velázquez JM, Mailly F, Nouet P. System-level simulations of multi-sensor systems and data fusion algorithms. Microsystem Technologies. 2018; 28(6): 1399-1408. doi: 10.1007/s00542-018-4204-8
42. Habash O, Mizouni R, Singh S, et al. Gaussian process-based online sensor selection for source localization. Internet of Things. 2024; 28: 101388. doi: 10.1016/j.iot.2024.101388
43. Song Y, Li M, Wang F, et al. Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm. Micromachines. 2022; 13(7): 1053. doi: 10.3390/mi13071053
44. Pan N. A sensor data fusion algorithm based on suboptimal network powered deep learning. Alexandria Engineering Journal. 2022; 61(9): 7129-7139. doi: 10.1016/j.aej.2021.12.058
45. Wang K, Zhang L. Integrated design of high‐speed permanent‐magnet machines considering sensorless operation. IEEJ Transactions on Electrical and Electronic Engineering. 2018; 13(8): 1189-1195. doi: 10.1002/tee.22682
46. Landi G, Avallone G, Barone C, et al. Design of an Environmental Sensor Board for Energy Harvesting: Integration of Conventional and Eco-friendly Sensors with Power Generation Sources. Electronics. 2024; 13(19): 3801.
47. Sheng H, Ma Y, Zhang H, et al. Integration of Supercapacitors with Sensors and Energy‐Harvesting Devices: A Review. Advanced Materials Technologies. 2024; 9(21). doi: 10.1002/admt.202301796
48. Lee J, Kim S, Kim JW, et al. Self‐Healing and Antifreezing/Antidrying Conductive Eutectohydrogel‐Based Biosignal Monitoring Multisensors with Integrated Supercapacitor. Small. 2024; 21(3). doi: 10.1002/smll.202409365
49. Hu X, Cao J, Wu H. A wearable device for collecting multi-signal parameters of newborn. Computer Communications. 2020; 154: 269-277. doi: 10.1016/j.comcom.2020.02.082
50. Ates HC, Nguyen PQ, Gonzalez-Macia L, et al. End-to-end design of wearable sensors. Nature Reviews Materials. 2022; 7(11): 887-907. doi: 10.1038/s41578-022-00460-x
51. Bassoli M, Bianchi V, Munari ID. A Plug and Play IoT Wi-Fi Smart Home System for Human Monitoring. Electronics. 2018; 7(9): 200. doi: 10.3390/electronics7090200
52. Yu L, Nazir B, Wang Y. Intelligent power monitoring of building equipment based on Internet of Things technology. Computer Communications. 2020; 157: 76-84. doi: 10.1016/j.comcom.2020.04.016
53. Li J, Ma Q, Chan AHS, et al. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics. 2019; 75: 162-169. doi: 10.1016/j.apergo.2018.10.006
54. Zhu J, Fu J, Sun Y, et al. Design of Intelligent Safety Monitoring System for Power Supply Bureau Based on ZigBee Technology and Information Fusion. Journal of Physics: Conference Series. 2020; 1486(2): 022017. doi: 10.1088/1742-6596/1486/2/022017
55. Vitazkova D, Kosnacova H, Turonova D, et al. Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders. Biosensors. 2025; 15(2): 117. doi: 10.3390/bios15020117
56. Huang C, Sun CC, Duan N, et al. Smart Meter Pinging and Reading Through AMI Two-Way Communication Networks to Monitor Grid Edge Devices and DERs. IEEE Transactions on Smart Grid. 2022; 13(5): 4144-4153. doi: 10.1109/tsg.2021.3133952
57. Wang Q, Li H, Wang H, et al. A Remote Calibration Device Using Edge Intelligence. Sensors. 2022; 22(1): 322. doi: 10.3390/s22010322
58. Su C, Chen W. Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network. Zeng W, ed. Journal of Sensors. 2022; 2022: 1-10. doi: 10.1155/2022/6228440
59. Si Y, Korada N, Ayyanar R, et al. A High Performance Communication Architecture for a Smart Micro-Grid Testbed Using Customized Edge Intelligent Devices (EIDs) With SPI and Modbus TCP/IP Communication Protocols. IEEE Open Journal of Power Electronics. 2021; 2: 2-17. doi: 10.1109/ojpel.2021.3051327
60. Rodríguez-Ríos A, Espinoza-Téllez G, Martínez-Ezquerro JD, et al. Information and Communication Technology, Mobile Devices, and Medical Education. Journal of Medical Systems. 2020; 44(4). doi: 10.1007/s10916-020-01559-w
61. Yang LQ, Ruan SJ, Cheng KH, et al. Model-Based Deep Encoding Based on USB Transmission for Modern Edge Computing Architectures. IEEE Access. 2020; 8: 112553-112561. doi: 10.1109/access.2020.3002844
62. Ayub MF, Saleem MA, Altaf I, et al. Fuzzy extraction and PUF based three party authentication protocol using USB as mass storage device. Journal of Information Security and Applications. 2020; 55: 102585. doi: 10.1016/j.jisa.2020.102585
63. Joh H, Ryoo I. A hybrid Wi-Fi P2P with bluetooth low energy for optimizing smart device’s communication property. Peer-to-Peer Networking and Applications. 2014; 8(4): 567-577. doi: 10.1007/s12083-014-0276-0
64. Islam R, Rahman MdW, Rubaiat R, et al. LoRa and server-based home automation using the internet of things (IoT). Journal of King Saud University - Computer and Information Sciences. 2022; 34(6): 3703-3712. doi: 10.1016/j.jksuci.2020.12.020
65. Faye I, Fam PA, Ndiaye ML. Energy Consumption of IoT Devices: An Accurate Evaluation to Better Predict Battery Lifetime. Radio Science. 2022; 57(12). doi: 10.1029/2021rs007423
66. Sultania AK, Mahfoudhi F, Famaey J. Real-Time Demand Response Using NB-IoT. IEEE Internet of Things Journal. 2020; 7(12): 11863-11872. doi: 10.1109/jiot.2020.3004390
67. Boni A, Bianchi V, Ricci A, et al. NB-IoT and Wi-Fi Technologies: An Integrated Approach to Enhance Portability of Smart Sensors. IEEE Access. 2021; 9: 74589-74599. doi: 10.1109/access.2021.3082006
68. Martiradonna S, Piro G, Boggia G. On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures. Sensors. 2019; 19(14): 3237. doi: 10.3390/s19143237
69. Xia Y, Chen J, Lu X, et al. Big traffic data processing framework for intelligent monitoring and recording systems. Neurocomputing. 2016; 181: 139-146. doi: 10.1016/j.neucom.2015.07.140
70. Talaat M, Alsayyari AS, Alblawi A, et al. Hybrid-cloud-based data processing for power system monitoring in smart grids. Sustainable Cities and Society. 2020; 55: 102049. doi: 10.1016/j.scs.2020.102049
71. Zhang N, Wu C, Wu Y, et al. An improved target tracking algorithm and its application in intelligent video surveillance system. Multimedia Tools and Applications. 2018; 79(23-24): 15965-15983. doi: 10.1007/s11042-018-6871-y
72. Zhang Q, Pan S. An AI-Augmented Kalman Filter Approach to Monitoring Network Traffic Matrix. IEEE Transactions on Network Science and Engineering. 2024; 11(3): 2426-2437. doi: 10.1109/tnse.2023.3297660
73. R R, S M. Frequency response masking based FIR filter using approximate multiplier for bio-medical applications. Sādhanā. 2019; 44(11). doi: 10.1007/s12046-019-1186-x
74. Pérez-Bailón J, Calvo B, Medrano N. A CMOS Low Pass Filter for SoC Lock-in-Based Measurement Devices. Sensors. 2019; 19(23): 5173. doi: 10.3390/s19235173
75. Lin WC, Wang JW. Edge detection in medical images with quasi high-pass filter based on local statistics. Biomedical Signal Processing and Control. 2018; 39: 294-302. doi: 10.1016/j.bspc.2017.08.011
76. Yuan H, Ma L, Yuan Z, et al. On-Chip Cascaded Bandpass Filter and Wavelength Router Using an Intelligent Algorithm. IEEE Photonics Journal. 2021; 13(4): 1-8. doi: 10.1109/jphot.2021.3100357
77. Bhadoria RS, Bajpai D. Stabilizing Sensor Data Collection for Control of Environment-Friendly Clean Technologies Using Internet of Things. Wireless Personal Communications. 2019; 108(1): 493-510. doi: 10.1007/s11277-019-06414-x
78. Cormane J, de O. Nascimento FA. Spectral Shape Estimation in Data Compression for Smart Grid Monitoring. IEEE Transactions on Smart Grid. 2016; 7(3): 1214-1221. doi: 10.1109/tsg.2015.2500359
79. Landau-Feibish S, Liu Z, Rexford J. Compact Data Structures for Network Telemetry. ACM Computing Surveys. 2025; 57(8): 1-31. doi: 10.1145/3716819
80. Li X, Yu Q, Alzahrani B, et al. Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey. IEEE Access. 2021; 9: 47069-47083. doi: 10.1109/access.2021.3060631
81. King RC, Villeneuve E, White RJ, et al. Application of data fusion techniques and technologies for wearable health monitoring. Medical Engineering & Physics. 2017; 42: 1-12. doi: 10.1016/j.medengphy.2016.12.011
82. Jan MA, Zhang W, Khan F, et al. Lightweight and smart data fusion approaches for wearable devices of the Internet of Medical Things. Information Fusion. 2024; 103: 102076. doi: 10.1016/j.inffus.2023.102076
83. Ghosh S, Manna D, Chatterjee A, et al. Remote Appliance Load Monitoring and Identification in a Modern Residential System With Smart Meter Data. IEEE Sensors Journal. 2021; 21(4): 5082-5090. doi: 10.1109/jsen.2020.3035057
84. Zong X, Zhang C, Wu D. Research on Data Mining of Sports Wearable Intelligent Devices Based on Big Data Analysis. Discrete Dynamics in Nature and Society. 2022; 2022(1). doi: 10.1155/2022/3723269
85. Ma Z, Xie J, Li H, et al. The Role of Data Analysis in the Development of Intelligent Energy Networks. IEEE Network. 2017; 31(5): 88-95. doi: 10.1109/mnet.2017.1600319
86. Salehi H, Das S, Biswas S, et al. Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals. Expert Systems with Applications. 2019; 135: 259-272. doi: 10.1016/j.eswa.2019.05.051
87. Masterson Creber RM, Hickey KT, Maurer MS. Gerontechnologies for Older Patients with Heart Failure: What is the Role of Smartphones, Tablets, and Remote Monitoring Devices in Improving Symptom Monitoring and Self-Care Management? Current Cardiovascular Risk Reports. 2016; 10(10). doi: 10.1007/s12170-016-0511-8
88. Choi HS, Yoon S, Kim J, et al. Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure. Sensors. 2024; 24(15): 4765. doi: 10.3390/s24154765
89. Farago E, Chan ADC. Motion artifact synthesis for research in biomedical signal quality analysis. Biomedical Signal Processing and Control. 2021; 68: 102611. doi: 10.1016/j.bspc.2021.102611
90. Zhao Y, Yin Y, Gui G. Lightweight Deep Learning Based Intelligent Edge Surveillance Techniques. IEEE Transactions on Cognitive Communications and Networking. 2020; 6(4): 1146-1154. doi: 10.1109/tccn.2020.2999479
91. Hu D, Huang Z, Yin K, et al. Multidimensional heterogeneous data clustering algorithm for power transmission and transformation equipment. Journal of Intelligent & Fuzzy Systems. 2023; 44(4): 5871-5878. doi: 10.3233/jifs-222924
92. Liu X, Yuan J, Zhao H. Efficient and Intelligent Density and Delta-Distance Clustering Algorithm. Arabian Journal for Science and Engineering. 2018; 43(12): 7177-7187. doi: 10.1007/s13369-017-3060-7
Copyright (c) 2025 Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.