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JRM Vol.37 No.3 pp. 752-761
doi: 10.20965/jrm.2025.p0752
(2025)

Review:

A Review of Research on Intelligent Spinal Orthosis Monitoring System Based on Distributed Pressure Sensing

Liang Xuan ORCID Icon and Jiaxin Dong ORCID Icon

Hubei Engineering Research Center for Intelligent Detection and Control of Specialized Equipment, Jianghan University
No.8 Delta Lake Road, Wuhan Economic and Technological Development Zone, Wuhan 430056, China

Received:
April 9, 2024
Accepted:
January 8, 2025
Published:
June 20, 2025
Keywords:
intelligent spinal orthosis monitoring system, distributed pressure detection, temperature and humidity compensation algorithm
Abstract

Intelligent rehabilitation aids achieve real-time tracking of patient treatment and personalized medical support by integrating sensing and detection technologies, data processing, and machine learning. In light of the lack of sensor monitoring systems in spinal orthosis therapy, this article systematically reviews the current research status of intelligent spinal orthosis monitoring systems. It focuses on discussing several key technologies for realizing the digital and intelligent technologies of spinal orthoses, such as distributed pressure sensing technology and pressure sensor software compensation algorithms. Finally, this article proposes a technical roadmap for developing an intelligent spinal orthosis monitoring system based on distributed pressure sensors. This solution provides theoretical support and technical implementation paths for the digitalization and intelligence of spinal corrective device treatment processes. It is expected to enhance the effectiveness of spinal scoliosis treatment and further promote the intelligent development of medical rehabilitation aids.

Design process of spinal orthosis monitoring system

Design process of spinal orthosis monitoring system

Cite this article as:
L. Xuan and J. Dong, “A Review of Research on Intelligent Spinal Orthosis Monitoring System Based on Distributed Pressure Sensing,” J. Robot. Mechatron., Vol.37 No.3, pp. 752-761, 2025.
Data files:
References
  1. [1] Y. Fan and F. Pu, “Biomechanics and rehabilitation technical aids,” J. of Medical Biomechanics, Vol.31, No.6, pp. 476-477, 2016 (in Chinese).
  2. [2] Y. Fan, S. Han, D. Antoniojevic, and Y. Fan, “Research on the development model of China’s rehabilitation aids industrial park in the age of Industry 4.0,” J. of Guangzhou Sport University, Vol.39, No.3, pp. 62-67, 2019 (in Chinese). https://doi.org/10.13830/j.cnki.cn44-1129/g8.2019.03.017
  3. [3] Q. Tao et al., “Clinical applications of smart wearable sensors,” iScience, Vol.26, No.9, Article No.107485, 2023. https://doi.org/10.1016/j.isci.2023.107485
  4. [4] Y. J. Choo and M. C. Chang, “Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review,” Prosthetics and Orthotics Int., Vol.47, No.3, pp. 226-240, 2023. https://doi.org/10.1097/PXR.0000000000000199
  5. [5] C. Wei et al., “A self-powered body motion sensing network integrated with multiple triboelectric fabrics for biometric gait recognition and auxiliary rehabilitation training,” Advanced Functional Materials, Vol.33, No.35, Article No.2303562, 2023. https://doi.org/10.1002/adfm.202303562
  6. [6] I. Shafi et al., “Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing,” PLOS ONE, Vol.19, No.3, Article No.e0298582, 2024. https://doi.org/10.1371/journal.pone.0298582
  7. [7] F. R. Labrom, M. T. Izatt, A. P. Claus, and J. P. Little, “Adolescent idiopathic scoliosis 3D vertebral morphology, progression and nomenclature: A current concepts review,” European Spine J., Vol.30, No.7, pp. 1823-1834, 2021. https://doi.org/10.1007/s00586-021-06842-z
  8. [8] Z. Zhang, J. Zhang, M. Feng, L. Kong, and B. He, “Research progress on the treatment of adolescent idiopathic scoliosis with braces,” Orthopaedics, Vol.14, No.1, pp. 87-91, 2023 (in Chinese).
  9. [9] A. Kumar and V. Jadav, “Orthoses in spinal cord injury rehabilitation management and improving quality of life,” L. Ricciardi, G. Lofrese, A. Perna, and S. Trungu (Eds.), “Spinal Cord Injury—Current Trends in Acute Management, Function Preservation and Rehabilitation Protocols,” IntechOpen, 2023. https://doi.org/10.5772/intechopen.105427
  10. [10] Y. Gao, “Research on system design of adolescent idiopathic scoliosis orthosis,” Master’s thesis, Zhengzhou University of Light Industry, 2021.
  11. [11] B. A. Bache, O. Iftikhar, and O. Dehzangi, “Brace treatment monitoring solution for idiopathic scoliosis patients,” 2017 16th IEEE Int. Conf. on Machine Learning and Applications, pp. 580-585, 2017. https://doi.org/10.1109/ICMLA.2017.00-98
  12. [12] H. Li, R. Chen, and A. Song, “Research on intelligent spinal orthopedic device based on distributed tactile perception,” Chinese J. of Sensor and Actuators, Vol.33, No.3, pp. 351-357, 2020 (in Chinese).
  13. [13] P. Tymińska, K. Zaborowska-Sapeta, D. Janczak, and Tomasz Giżewski, “TLSO with graphene sensors—An application to measurements of corrective forces in the prototype of intelligent brace,” Sensors, Vol.22, No.11, Article No.4015, 2022. https://doi.org/10.3390/s22114015
  14. [14] Y. Hayakawa, Y. Kimata, and K. Kida, “Study on human behavior classification by using high-performance shoes equipped with pneumatic actuators,” J. Robot. Mechatron., Vol.32, No.5, pp. 947-957, 2020. https://doi.org/10.20965/jrm.2020.p0947
  15. [15] X. Wu, W. Li, J. Li, and H. Gu, “Wearable gait information detection insole design and application,” Transducer and Microsystem Technologies, Vol.40, No.11, pp. 111-114, 2021 (in Chinese). https://doi.org/10.13873/J.1000-9787(2021)11-0111-04
  16. [16] Q. Zeng, Q. Wan, C. Xue, and C. Li, “Design of pressure detection layout for cardiopulmonary resuscitation chest compressions at the base of the palm,” Automation Application, Vol.64, No.11, pp. 179-180+183, 2023 (in Chinese).
  17. [17] X. Xian, “Design and application research of wearable foot information intelligent analysis system,” Master’s thesis, South China University of Technology, 2022 (in Chinese). https://doi.org/10.27151/d.cnki.ghnlu.2022.003772
  18. [18] W. Wang, “Development of intelligent design system for adolescent idiopathic scoliosis orthotics,” Master’s thesis, Dalian Jiaotong University, 2023 (in Chinese). https://doi.org/10.26990/d.cnki.gsltc.2023.000696
  19. [19] T. Guan, L. Peng, Y. Zhu, and X. Chen, “Analysis of influencing factors of respiratory force in patients with scoliosis wearing orthosis in different postures,” Smart Healthcare, Vol.6, No.5, pp. 41-45, 2020 (in Chinese). https://doi.org/10.19335/j.cnki.2096-1219.2020.05.019
  20. [20] Y. Liu, G. Liu, and B. Liu, “Research status and prospect on optimal placement of sensor,” Transducer and Microsystem Technologies, Vol.29, No.11, pp. 4-6+13, 2010 (in Chinese). https://doi.org/10.13873/j.1000-97872010.11.020
  21. [21] N. Xu, G. Wang, and Y. Tao, “Flexible wearable piezoresistive pressure sensors,” Chemical Industry and Engineering Progress, Vol.42, No.10, pp. 5259-5271, 2023 (in Chinese). https://doi.org/10.16085/j.issn.1000-6613.2022-2228
  22. [22] J. Li et al., “Study on temperature and synthetic compensation of piezo-resistive differential pressure sensors by coupled simulated annealing and simplex optimized kernel extreme learning machine,” Sensors, Vol.17, No.4, Article No.894, 2017. https://doi.org/10.3390/s17040894
  23. [23] W. Su et al., “Thermal compensation system for silicon piezoresistive pressure sensors based on surface fitting and wild horse algorithm,” IEEE Sensors J., Vol.24, No.7, pp. 10347-10354, 2024. https://doi.org/10.1109/JSEN.2024.3365469
  24. [24] T. Wu, S. Chen, P. Wu, and S. Nie, “A high precision software compensation algorithm for silicon piezoresistive pressure sensor,” Chinese J. of Electronics, Vol.28, No.4, pp. 748-753, 2019.
  25. [25] W. Wang, L. Ma, Y. Wang, and Y. Yang, “Research on temperature compensation of pressure sensors based on cubic spline curve interpolation,” Mechanical and Electrical Information, Vol.2020, No.23, pp. 33-35, 2020 (in Chinese). https://doi.org/10.19514/j.cnki.cn32-1628/tm.2020.23.016
  26. [26] S. Nie, “High accurate compensation algorithm of silicon piezoresistive pressure sensor and its implementation,” Process Automation Instrumentation, Vol.39, No.6, pp. 49-53, 2018 (in Chinese). https://doi.org/10.16086/j.cnki.issn1000-0380.2018010059
  27. [27] M. Aryafar, M. Hamedi, and M. M. Ganjeh, “A novel temperature compensated piezoresistive pressure sensor,” Measurement, Vol.63, pp. 25-29, 2015. https://doi.org/10.1016/j.measurement.2014.11.032
  28. [28] H. Qiu et al., “Temperature compensation of light addressable potentiometric sensor based on support vector machine,” J. of Optoelectronics Laser, Vol.26, No.12, pp. 2272-2277, 2015 (in Chinese). https://doi.org/10.16136/j.joel.2015.12.0642
  29. [29] J. Li et al., “A temperature compensation method for piezo-resistive pressure sensor utilizing chaotic ions motion algorithm optimized hybrid kernel LSSVM,” Sensors, Vol.16, No.10, Article No.1707, 2016. https://doi.org/10.3390/s16101707
  30. [30] G. Zhou, Y. Zhao, F. Guo, and W. Xu, “A smart high accuracy silicon piezoresistive pressure sensor temperature compensation system,” Sensors, Vol.14, No.7, pp. 12174-12190, 2014. https://doi.org/10.3390/s140712174
  31. [31] Y. Ruan et al., “Temperature compensation and pressure bias estimation for piezoresistive pressure sensor based on machine learning approach,” IEEE Trans. on Instrumentation and Measurement, Vol.70, Article No.1008610, 2021. https://doi.org/10.1109/TIM.2021.3089236
  32. [32] J. Li et al., “Temperature compensation of piezo-resistive pressure sensor utilizing ensemble AMPSO-SVR based on improved AdaBoost.RT,” IEEE Access, Vol.8, pp. 12413-12425, 2020. https://doi.org/10.1109/ACCESS.2020.2965150
  33. [33] L. Chen, J. Shen, B. Zhou, and M. Zhang, “Flexible array pressure sensing system for local temperature variation of irregular objects,” Chinese J. of Electron Devices, Vol.47, No.1, pp. 104-110, 2024 (in Chinese).
  34. [34] Y. Liang, “Research on temperature compensation and implementation of pressure sensors,” Standard & Quality of Light Industry, Vol.2022, No.5, pp. 72-74, 2022 (in Chinese). https://doi.org/10.19541/j.cnki.issn1004-4108.2022.05.014
  35. [35] H. He, J. Xu, Z. Zhou, D. Hu, and J. Li, “Research on interpolation compensation method for temperature error of piezo-resistive pressure sensor,” J. of Electronic Measurement and Instrumentation, Vol.35, No.12, pp. 1-7, 2021 (in Chinese). https://doi.org/10.13382/j.jemi.B2104283
  36. [36] A. M. M. Almassri et al., “Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network,” Sensors, Vol.18, No.8, Article No.2561, 2018. https://doi.org/10.3390/s18082561
  37. [37] M. O. Kayed, A. A. Balbola, E. Lou, and W. A. Moussa, “Hybrid smart temperature compensation system for piezoresistive 3D stress sensors,” IEEE Sensors J., Vol.20, No.22, pp. 13310-13317, 2020. https://doi.org/10.1109/JSEN.2020.3005091
  38. [38] Z. Zhu and H. Zhang, “Research on improved PSO optimized wavelet neural network in temperature compensation of pressure sensor,” Instrument Technique and Sensor, Vol.2022, No.8, pp. 122-126, 2022 (in Chinese).
  39. [39] B. Li, W. Lu, and F. Zuo, “Temperature compensation of optical fiber pressure sensor based on GWO-LSSVM,” J. of Electronic Measurement and Instrumentation, Vol.37, No.5, pp. 143-150, 2023 (in Chinese). https://doi.org/10.13382/j.jemi.B2306255
  40. [40] R. d. S. Pereira and C. A. Cima, “Thermal compensation method for piezoresistive pressure transducer,” IEEE Trans. on Instrumentation and Measurement, Vol.70, Article No.9510807, 2021. https://doi.org/10.1109/TIM.2021.3092789
  41. [41] M. Sarmad, M. Fatima, and J. Tayyub, “Reducing energy consumption of pressure sensor calibration using polynomial HyperNetworks with Fourier features,” Proc. of the AAAI Conf. on Artificial Intelligence, Vol.36, No.11, pp. 12145-12153, 2022. https://doi.org/10.1609/aaai.v36i11.21474
  42. [42] M. Zou, Y. Xu, J. Jin, M. Chu, and W. Huang, “Accurate nonlinearity and temperature compensation method for piezoresistive pressure sensors based on data generation,” Sensors, Vol.23, No.13, Article No.6167, 2023. https://doi.org/10.3390/s23136167

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Last updated on Jun. 20, 2025