Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

RehabFAB: design investigation and needs assessment of displacement-orientated fabric wearable sensors for rehabilitation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Patients with motor impairments (e.g., stroke, bone fracture, Parkinson’s) are sensitive to the wearing experience of rehabilitation devices, and they often have difficulty accurately positioning them at an accurate position. While solutions involving optical systems or IMUs could potentially help alleviate the issue, they often introduce other challenges such as privacy concerns or discomforting experiences. With the emergence of wearable soft sensors during the last few decades, researchers widely apply soft sensors in rehabilitation to improve the wearing experience. However, these approaches have primarily focused on analyzing the sensor readings to improve accuracy rather than addressing the needs of patients and healthcare providers, and there is a lack of comprehensive design investigation and need assessment based on soft sensor-based rehabilitation systems for motor-impaired patients and their doctors. In this study, we developed an application, RehabFAB, utilizing fabric sensors for rehabilitation purposes. Besides, we evaluated our application and device and investigated the needs of patients and doctors for potential home rehabilitation applications. The investigation was conducted through thematic analysis, correlation analysis and System Usability Scale. The experimental results validated the efficacy, reliability and usability of our approach, with a SUS score of 81.75. In addition, the RehabFAB meets the expectations of motor-impaired patients and medical professionals as a home rehabilitation tool. Our core contributions lie in a thorough evaluation of the needs of motor-impaired patients in order to design a stable and reliable motion-tracking device based on soft sensors for their recovery.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

  1. https://item.taobao.com/item.htm?id=644248735672

  2. Elbow arthrolysis is a surgical procedure to treat conditions that are causing significant stiffness and reduced range of motion in the elbow joint [51].

  3. MMT is a method carried out by examiners without any equipment to evaluate patients’ muscle strength.

  4. A spasm is a sudden involuntary contraction of a muscle [62], a group of muscles, or a hollow organ such as the bladder [63].

  5. Tertiary-level rehabilitation represents a stage (>4-6 months) after the occurrence of diseases when patients can move independently and have the ability rehabilitating at home [42].

References

  1. Adans-Dester C, Hankov N, O’Brien A et al (2020) Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit Med 3(1):1–10

    Google Scholar 

  2. Al-Qazzaz NK, Alyasseri ZAA, Abdulkareem KH et al (2021) Eeg feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation. Comput Biol Med 137(104):799

    Google Scholar 

  3. Alvarez JT, Gerez LF, Araromi OA et al (2022) Towards soft wearable strain sensors for muscle activity monitoring. IEEE Trans Neural Syst Rehabil Eng 30:2198–2206

    Google Scholar 

  4. Bader P, Voit A, Le HV et al (2019) Windowwall: Towards adaptive buildings with interactive windows as ubiquitous displays. ACM Trans Comput Hum Interact (TOCHI) 26(2):1–42

    Google Scholar 

  5. Bangor A, Kortum P, Miller J (2009) Determining what individual sus scores mean: Adding an adjective rating scale. J Usability Stud 4(3):114–123

    Google Scholar 

  6. Bartlett NW, Lyau V, Raiford WA et al (2015) A soft robotic orthosis for wrist rehabilitation. J Med Devices 9(3)

  7. Braun V, Clarke V (2006) Using thematic analysis in psychology. Qual Res Psychol 3(2):77–101

    Google Scholar 

  8. Brooke J (1996) Sus-a quick and dirty usability scale. usability evaluation in industry 189, 194 (1996), 4–7

  9. Capela NA, Lemaire ED, Baddour N (2015) Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PloS One 10(4):e0124-414

    Google Scholar 

  10. Chen X, Gong L, Wei L et al (2020) A wearable hand rehabilitation system with soft gloves. IEEE Trans Ind Inform 17(2):943–952

    Google Scholar 

  11. Chen X, Jiang X, Fang J et al (2023) Dispad: Flexible on-body displacement of fabric sensors for robust joint-motion tracking. Proc ACM Interact Mob Wearable Ubiquitous Technol 7(1). https://doi.org/10.1145/3580832

  12. Chen Y, Tan X, Yan D et al (2020) A composite fabric-based soft rehabilitation glove with soft joint for dementia in parkinson’s disease. IEEE J Transl Eng Health Med 8:1–10

    Google Scholar 

  13. Chossat JB, Tao Y, Duchaine V et al (2015) Wearable soft artificial skin for hand motion detection with embedded microfluidic strain sensing. In: 2015 IEEE international conference on robotics and automation (ICRA), IEEE, pp 2568–2573

  14. Chu CY, Patterson RM (2018) Soft robotic devices for hand rehabilitation and assistance: a narrative review. J Neuroeng Rehabilitation 15(1):1–14

    Google Scholar 

  15. Ciesla N, Dinglas V, Fan E et al (2011) Manual muscle testing: a method of measuring extremity muscle strength applied to critically ill patients. J Vis Exp 50:e2632

    Google Scholar 

  16. Dai Y, Wu J, Fan Y et al (2022) Mseva: A musculoskeletal rehabilitation evaluation system based on emg signals. ACM Trans Sens Netw 19(1):1–23

    Google Scholar 

  17. Dash A, Yadav A, Chauhan A et al (2019) Kinect-assisted performance-sensitive upper limb exercise platform for post-stroke survivors. Front Neurosci 13:228

    Google Scholar 

  18. De Vito L, Postolache O, Rapuano S (2014) Measurements and sensors for motion tracking in motor rehabilitation. IEEE Instrum Meas Mag 17(3):30–38

    Google Scholar 

  19. Dovat L, Lambercy O, Gassert R et al (2008) Handcare: a cable-actuated rehabilitation system to train hand function after stroke. IEEE Trans Neural Syst Rehabil Eng 16(6):582–591

    Google Scholar 

  20. Fang C, He B, Wang Y et al (2020) Emg-centered multisensory based technologies for pattern recognition in rehabilitation: state of the art and challenges. Biosensors 10(8):85

    Google Scholar 

  21. Fang J, Yuan J, Wang M et al (2020) Novel accordion-inspired foldable pneumatic actuators for knee assistive devices. Soft Robot 7(1):95–108

    Google Scholar 

  22. Ferraris C, Nerino R, Chimienti A et al (2014) Remote monitoring and rehabilitation for patients with neurological diseases. In: Proceedings of the 9th international conference on body area networks, pp 76–82

  23. Foundation P (2023) Understanding parkinson’s. https://www.parkinson.org/understanding-parkinsons. Accessed 28 Nov 2022

  24. Gao J, Li B, Huang X et al (2019) Electrically conductive and fluorine free superhydrophobic strain sensors based on sio2/graphene-decorated electrospun nanofibers for human motion monitoring. Chem Eng J 373:298–306

    Google Scholar 

  25. Gash MC, Kandle PF, Murray IV et al (2022) Physiology, muscle contraction. In: StatPearls [Internet]. StatPearls Publishing

  26. Gino C (2021) Using imus for the assessment of knee flex-extension angle in presence of soft tissue artefacts. PhD thesis, Politecnico di Torino

  27. Glauser O, Wu S, Panozzo D et al (2019) Interactive hand pose estimation using a stretch-sensing soft glove. ACM Trans Graph (TOG) 38(4):41

    Google Scholar 

  28. Healthline (2022) Periarthritis. https://www.healthline.com/health/arthritis/periarthritis. Accessed 28 Nov 2022

  29. Kaku A, Parnandi A, Venkatesan A et al (2020) Towards data-driven stroke rehabilitation via wearable sensors and deep learning. In: Machine learning for healthcare conference, PMLR, pp 143–171

  30. Kantak SS, Johnson T, Zarzycki R (2022) Linking pain and motor control: conceptualization of movement deficits in patients with painful conditions. Physical Therapy 102(4):pzab289

    Google Scholar 

  31. Khan MA, Saibene M, Das R et al (2021) Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng 18(6):061003

    Google Scholar 

  32. Khokhlova L, Belcastro M, Torchia P et al (2021) Wearable textile-based device for human lower-limbs kinematics and muscle activity sensing. In: International conference on wearables in healthcare, Springer, pp 70–81

  33. Kumar V, Abbas AK, Fausto N et al (2014) Robbins and Cotran pathologic basis of disease, professional edition e-book. Elsevier health sciences

  34. Kytö M, Maye L, McGookin D (2019) Using both hands: tangibles for stroke rehabilitation in the home. In: Proceedings of the 2019 CHI conference on human factors in computing systems, pp 1–14

  35. Lang CE, Waddell KJ, Klaesner JW et al (2017) A method for quantifying upper limb performance in daily life using accelerometers. JoVE (J Vis Exp) 122:e55673

    Google Scholar 

  36. Lee SI, Adans-Dester CP, Grimaldi M et al (2018) Enabling stroke rehabilitation in home and community settings: a wearable sensor-based approach for upper-limb motor training. IEEE J Transl Eng Health Med 6:1–11

    Google Scholar 

  37. Leuenberger K, Gonzenbach R, Wachter S et al (2017) A method to qualitatively assess arm use in stroke survivors in the home environment. Med Biol Eng Comput 55(1):141–150

    Google Scholar 

  38. Li F, Chen J, Ye G et al (2023) Soft robotic glove with sensing and force feedback for rehabilitation in virtual reality. Biomimetics 8(1):83

    Google Scholar 

  39. Liu R, Shao Q, Wang S et al (2019) Reconstructing human joint motion with computational fabrics. Proc ACM Interact Mob Wearable Ubiquitous Technol 3(1):19

    Google Scholar 

  40. de Lucena DS, Stoller O, Rowe JB et al (2017) Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery. In: 2017 International conference on rehabilitation robotics (ICORR), IEEE, pp 1603–1608

  41. Maceira-Elvira P, Popa T, Schmid AC et al (2019) Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabilitation 16(1):1–18

    Google Scholar 

  42. Mayer T, Polatin P, Smith B et al (2003) Spine rehabilitation: secondary and tertiary nonoperative care: North american spine society committee: Contemporary concepts review committee. Spine J 3(3):28–36

    Google Scholar 

  43. Meng Q, Zhang J, Yang X (2019) Virtual rehabilitation training system based on surface emg feature extraction and analysis. J Med Syst 43:1–11

    Google Scholar 

  44. Michielsen ME, Selles RW, Stam HJ et al (2012) Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. Arch Phys Med Rehabil 93(11):1975–1981

  45. Mohebbi A (2020) Human-robot interaction in rehabilitation and assistance: a review. Curr Robot Rep 1(3):131–144

    Google Scholar 

  46. Monoli C, Fuentez-Pérez JF, Cau N et al (2021) Land and underwater gait analysis using wearable imu. IEEE Sens J 21(9):11192–11202

    Google Scholar 

  47. Oguntosin V, Harwin WS, Kawamura S et al (2015) Development of a wearable assistive soft robotic device for elbow rehabilitation. In: 2015 IEEE international conference on rehabilitation robotics (ICORR), IEEE, pp 747–752

  48. Park YL, Br Chen, Pérez-Arancibia NO et al (2014) Design and control of a bio-inspired soft wearable robotic device for ankle-foot rehabilitation. Bioinspiration Biomim 9(1):016007

    Google Scholar 

  49. Pastor I, Hayes HA, Bamberg SJ (2012) A feasibility study of an upper limb rehabilitation system using kinect and computer games. In: 2012 Annual international conference of the IEEE engineering in medicine and biology society, IEEE, pp 1286–1289

  50. Patel S, Park H, Bonato P et al (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroengineering Rehabilitation 9(1):1–17

    Google Scholar 

  51. physio.co.uk (2023) Arthrolysis. https://www.physio.co.uk/what-we-treat/surgery/elbow/arthrolysis.php. Accessed 11 Jan 2023

  52. Qassim HM, Wan Hasan W (2020) A review on upper limb rehabilitation robots. Appl Sci 10(19):6976

    Google Scholar 

  53. Reinkensmeyer DJ, Dewald J, Rymer WZ (1996) Robotic devices for physical rehabilitation of stroke patients: Fundamental requirements, target therapeutic techniques, and preliminary designs. Technol Disabil 5(2):205–215

    Google Scholar 

  54. Roberts JC, Headleand C, Ritsos PD (2015) Sketching designs using the five design-sheet methodology. IEEE Trans Vis Comput Graph 22(1):419–428

    Google Scholar 

  55. Ruiz A, Forner-Cordero A, Rocon E et al (2006) Exoskeletons for rehabilitation and motor control. In: The first IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, 2006. BioRob 2006, IEEE, pp 601–606

  56. Tinazzi M, Geroin C, Erro R et al (2021) Functional motor disorders associated with other neurological diseases: beyond the boundaries of “organic” neurology. Eur J Neurol 28(5):1752–1758

  57. Tognetti A, Lorussi F, Dalle Mura G et al (2014) New generation of wearable goniometers for motion capture systems. J Neuroeng Rehabilitation 11(1):56

    Google Scholar 

  58. Van Meulen FB, Klaassen B, Held J et al (2016) Objective evaluation of the quality of movement in daily life after stroke. Front Bioeng Biotechnol 3:210

    Google Scholar 

  59. Vu CC, Kim J (2020) Highly elastic capacitive pressure sensor based on smart textiles for full-range human motion monitoring. Sens Actuator A Phys 314(112):029

    Google Scholar 

  60. Waller SM, Whitall J (2008) Bilateral arm training: why and who benefits? NeuroRehabilitation 23(1):29–41

    Google Scholar 

  61. WIKIPEDIA (2022a) Cerebral infarction. https://en.wikipedia.org/wiki/Cerebral_infarction. Accessed 28 Nov 2022

  62. WIKIPEDIA (2022b) Dorland’s medical reference works. https://en.wikipedia.org/wiki/Dorland%27s_medical_reference_works. Accessed 27 Nov 2022

  63. WIKIPEDIA (2022c) Spasm. https://en.wikipedia.org/wiki/Spasm. Accessed 27 Nov 2022

  64. WIKIPEDIA (2022d) Thrombosis. https://en.wikipedia.org/wiki/Thrombosis. Accessed 25 Nov 2022

  65. WIKIPEDIA (2023a) Hemiparesis. https://en.wikipedia.org/wiki/Hemiparesis#cite_note-FactsInfo-1. Accessed 10 Jan 2023

  66. WIKIPEDIA (2023b) Wikipedia. https://en.wikipedia.org/wiki/Parkinson%27s_disease. Accessed 28 Nov 2022

  67. Xsens (2023) Xsens, the leading innovator in 3d motion tracking technology. http://www.xsens.com. Accessed 10 Jan 2023

  68. Yu M, Jin J, Wang X et al (2021) Development and design of flexible sensors used in pressure-monitoring sports pants for human knee joints. IEEE Sensors Journal 21(22):25400–25408

    Google Scholar 

  69. Zhang T, Li Z, Li K et al (2019) Flexible pressure sensors with wide linearity range and high sensitivity based on selective laser sintering 3d printing. Adv Mater Technol 4(12):1900679

    Google Scholar 

  70. Zhao Y, Wang J, Zhang Y et al (2021) Flexible and wearable emg and psd sensors enabled locomotion mode recognition for ioht-based in-home rehabilitation. IEEE Sens J 21(23):26311–26319

    Google Scholar 

  71. Zheng M, Crouch MS, Eggleston MS (2022) Surface electromyography as a natural human-machine interface: a review. IEEE Sens J 22(10):9198–9214

    Google Scholar 

  72. Zhu Z, Guo S, Qin Y et al (2021) Robust elbow angle prediction with aging soft sensors via output-level domain adaptation. IEEE Sens J 21(20):22976–22984

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (62072383, 61702433, 62077039, 61962021), the Fundamental Research Funds for the Central Universities (20720210044, 20720190006), Research and Development Program of Jiangxi Province (20223BBE51039, 20232BBE50020),Science Fund for Distinguished Young Scholars of Jiangxi Province (20232ACB212007) and Leading Project of Fujian Provincial Science and Technology Department (project name: Application of Flexible Sensor Based Motion Capture Clothing in Stroke Rehabilitation; grant number: 2022D022).

Author information

Authors and Affiliations

  1. School of Informatics, Xiamen University, No. 422, Siming South Road, Xiamen, 361005, Fujian, China

    Xiaowei Chen, Xiao Jiang, Shihui Guo, Juncong Lin & Minghong Liao

  2. Department of Rehabilitation Medicine, Xiamen Haicang Hospital, No. 89 Haiyu Road, Xiamen, 361026, Fujian, China

    Hongli Fan

  3. Department of Neurology, The Second Affiliated Hospital of Xiamen Medical College, No.566 Shengguang Road, Xiamen, 361021, Fujian, China

    Yiwen Zhang

  4. Virtual Reality & Interactive Technology Institute, East China Jiao Tong University, No.808 Shuanggang Road, Nanchang, 330013, Jiangxi, China

    Guoliang Luo

Authors
  1. Xiaowei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shihui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juncong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Minghong Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongli Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yiwen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guoliang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juncong Lin.

Ethics declarations

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (mp4 24477 KB)

Appendix A: summary tables

Appendix A: summary tables

Table 7 Findings from patient interview
Full size table
Table 8 Findings from doctor interview
Full size table
Table 9 The detailed profiles of patients
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Jiang, X., Guo, S. et al. RehabFAB: design investigation and needs assessment of displacement-orientated fabric wearable sensors for rehabilitation. Multimed Tools Appl 83, 57579–57612 (2024). https://doi.org/10.1007/s11042-023-17726-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17726-3

Keywords

Search

Navigation