Skip to main content

Advertisement

SpringerLink
Log in

Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Taking advantage of motion sensing technology, a quantitative assessment method for lower limbs motor function of cerebral palsy (CP) based on the gross motor function measurement (GMFM)-24 scale was explored in this study. According to the motion analysis on GMFM-24 scale, we translated the assessment problem of GMFM-24 scale into a detection problem of different motion modes including static state, fall, step, turning, alternating gait, walking, running, lifting legs, kicking balls, and jumping. The surface electromyography (sEMG) electrodes and inertial sensors were adopted to capture motion data, and a framework integrating a series of detection algorithms was presented for the assessment of lower limbs gross motor function. Two groups of participants including 8 healthy adults and 14 CP children were recruited. A self-developed data acquisition equipment integrating 24 sEMG electrodes and 9 inertial units was adopted for data acquisition. A platform based on two laser beam sensors was used to perform cross-border detection. The parameters/thresholds of motion detection algorithms were determined by the data from healthy adults, and the lower limbs gross motor function evaluation was conducted on 14 CP children. The experimental results verified the feasibility and effectiveness of the proposed quantitative assessment method. Compared to the clinical assessment score based on GMFM-24 scale, 90.1% accuracy was obtained for evaluation of 303 tasks in 14 CP children. The objective motor function assessment method proposed has potential application value for the quantitative assessment of lower limbs motor function of CP children in clinical practice.

The algorithm framework for the assessment of lower limbs gross motor function. Using the GMFM-24 scale as the evaluation standard, a quantitative evaluation program for the lower limbs gross motor function of CP children based on motion sensing technology was proposed.

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

Access this article

Log in via an institution

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Bashir FI, Khokhar AA, Schonfeld D (2007) Real-time motion trajectory-based indexing and retrieval of video sequences. IEEE Trans Multimed 9:58–65

    Article  Google Scholar 

  2. Bojanic DM, Petrovacki-Balj BD, Jorgovanovic ND, Ilic VR (2011) Quantification of dynamic EMG patterns during gait in children with cerebral palsy. J Neurosci Methods 198:325–331. https://doi.org/10.1016/j.jneumeth.2011.04.030

    Article  PubMed  Google Scholar 

  3. Bourgeois AB, Mariani B, Aminian K, Zambelli P, Newman C (2014) Spatio-temporal gait analysis in children with cerebral palsy using, foot-worn inertial sensors. Gait Posture 39:436–442

    Article  Google Scholar 

  4. Bunke H, Roth M, Schukat-Talamazzini EG (1995) Off-line cursive handwriting recognition using hidden Markov models. Pattern Recogn 28:1399–1413

    Article  Google Scholar 

  5. Butler EE (2011) Quantitative analysis of upper limb function among children with cerebral palsy during a reach and grasp cycle. Stanford University, Stanford

    Google Scholar 

  6. Carpenter C, Bass A (2017) The value of gait analysis in decision making about surgical treatment of cerebral palsy. In: Paediatric Orthopaedics. Springer, Berlin, pp 361–367

    Chapter  Google Scholar 

  7. Cheng J, Chen X, Shen M (2013) A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J Biomed Health Informa 17:38–45

    Article  Google Scholar 

  8. Cools W, De Martelaer K, Samaey C, Andries C (2009) Movement skill assessment of typically developing preschool children: a review of seven movement skill assessment tools. J Sports Sci Med 8:154

    PubMed  PubMed Central  Google Scholar 

  9. Dai H, Zhang P, Lueth T (2015) Quantitative assessment of parkinsonian tremor based on an inertial measurement unit. Sensors 15:25055–25071

    Article  Google Scholar 

  10. Diebel J (2006) Representing attitude: Euler angles, unit quaternions, and rotation vectors. Matrix 58:1–35

    Google Scholar 

  11. Euston M, Coote P, Mahony R, Kim J, Hamel T (2008) A complementary filter for attitude estimation of a fixed-wing UAV. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, Piscataway, pp 340–345

    Chapter  Google Scholar 

  12. Fortune E, Lugade V, Morrow M, Kaufman K (2014) Validity of using tri-axial accelerometers to measure human movement—part II: step counts at a wide range of gait velocities. Med Eng Phys 36:659–669

    Article  Google Scholar 

  13. Gibson RM, Amira A, Ramzan N, Casaseca-de-la-Higuera P, Pervez Z (2016) Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl Soft Comput 39:94–103

    Article  Google Scholar 

  14. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G (2000) Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 10:361–374

    Article  CAS  Google Scholar 

  15. Kangas M, Konttila A, Lindgren P, Winblad I, Jämsä T (2008) Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28:285–291

    Article  Google Scholar 

  16. Koshmak G, Loutfi A, Linden M (2016) Challenges and issues in multisensor fusion approach for fall detection. J Sensors 2016

  17. Lim D, Park C, Kim NH, Kim S-H, Yu YS (2014) Fall-detection algorithm using 3-axis acceleration: combination with simple threshold and hidden Markov model. J Appl Math 2014

  18. Mercer C (2006) Acceleration, velocity and displacement spectra omega arithmetic. Prosig signal processing tutorials

    Google Scholar 

  19. Palisano R, Rosenbaum P, Walter S, Russell D, Wood E, Galuppi B (1997) Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev Med Child Neurol 39:214–223

    Article  CAS  Google Scholar 

  20. Park ES, Kim HW, Park CI, D-w R, Park CW (2006) Dynamic foot pressure measurements for assessing foot deformity in persons with spastic cerebral palsy. Arch Phys Med Rehabil 87:703–709

    Article  Google Scholar 

  21. Parnandi A, Wade E, Matarić M (2010) Motor function assessment using wearable inertial sensors. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, Piscataway, pp 86–89

    Chapter  Google Scholar 

  22. Patel S, Hughes R, Hester T, Stein J, Akay M, Dy J, Bonato P (2010) Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, Piscataway, pp 6858–6861

    Chapter  Google Scholar 

  23. Repnik E, Puh U, Goljar N, Munih M, Mihelj M (2018) Using inertial measurement units and electromyography to quantify movement during action research arm test execution. Sensors 18:2767

    Article  Google Scholar 

  24. Russell DJ, Rosenbaum PL, Wright M, Avery LM (2002) Gross motor function measure (GMFM-66 & GMFM-88) user’s manual, vol 159. Mac Keith, London

    Google Scholar 

  25. Saether R, Adde L, Helbostad J, Braendvik S, Lydersen S, Vik T (2014) Gait characteristics in children and adolescents with cerebral palsy assessed with a trunk-worn accelerometer: sp46. Dev Med Child Neurol 56:30

    Article  Google Scholar 

  26. Scholtes VA, Becher JG, Beelen A, Lankhorst GJ (2006) Clinical assessment of spasticity in children with cerebral palsy: a critical review of available instruments. Dev Med Child Neurol 48:64–73

    Article  Google Scholar 

  27. Seel T, Raisch J, Schauer T (2014) IMU-based joint angle measurement for gait analysis. Sensors 14:6891–6909

    Article  Google Scholar 

  28. Spasojević S, Ilić TV, Stojković I, Potkonjak V, Rodić A, Santos-Victor J (2017) Quantitative assessment of the arm/hand movements in Parkinson’s disease using a wireless armband device. Front Neurol 8:388

    Article  Google Scholar 

  29. Tang L, Yang X, Yin Q, Cai K, Wang H, Chaudhury I, Yao C, Zhou Q, Kwon M, Hartman JA (2014) Investigating the optimal size of anticancer nanomedicine. Proc Natl Acad Sci 111:15344–15349

    Article  CAS  Google Scholar 

  30. Tang L, Li F, Cao S, Zhang X, Wu D, Chen X (2015) Muscle synergy analysis in children with cerebral palsy. Journal of Neural Engineering 12:046017

    Article  Google Scholar 

  31. Tang L, Chen X, Cao S, Wu D, Zhao G, Zhang X (2017) Assessment of upper limb motor dysfunction for children with cerebral palsy based on muscle synergy analysis. Front Hum Neurosci 11:130

    PubMed  PubMed Central  Google Scholar 

  32. Wade E, Parnandi AR, Mataric MJ (2010) Automated administration of the wolf motor function test for post-stroke assessment. In: 2010 4th International Conference on Pervasive Computing Technologies for Healthcare. IEEE, Piscataway, pp 1–7

    Google Scholar 

  33. Weng S, Xiang L, Tang W, Yang H, Zheng L, Lu H, Zheng H (2014) A low power and high accuracy MEMS sensor based activity recognition algorithm. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, Piscataway, pp 33–38

    Chapter  Google Scholar 

  34. Wie B, Barba PM (1985) Quaternion feedback for spacecraft large angle maneuvers. J Guid Control Dyn 8:360–365

    Article  Google Scholar 

  35. Wu J, Zhou Z, Chen J, Fourati H, Li R (2016) Fast complementary filter for attitude estimation using low-cost MARG sensors. IEEE Sensors J 16:6997–7007

    Article  Google Scholar 

  36. Zhang X, Chen X, Li Y, Lantz V, Wang K, Yang J (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybernet A Syst Hum 41:1064–1076

    Article  Google Scholar 

  37. Zhu C, Sheng W (2011) Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 41:569–573

    Article  Google Scholar 

  38. Zollo L, Rossini L, Bravi M, Magrone G, Sterzi S, Guglielmelli E (2011) Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation. Med Biol Eng Comput 49:1131

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to all the participants for their participation in this study.

Funding

This work was supported by the National Natural Science Foundation of China under Grants 61671417 and 61871360.

Author information

Authors and Affiliations

  1. Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China

    Xiang Chen, Qi Wu, Lu Tang, Shuai Cao, Xu Zhang & Xun Chen

Authors
  1. Xiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuai Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Chen.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

Each participant signed an informed consent form before commencing the experiment, which was approved by the Ethics Review Committee of Anhui Medical University (No. PJ2014-08-04).

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Wu, Q., Tang, L. et al. Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors. Med Biol Eng Comput 58, 101–116 (2020). https://doi.org/10.1007/s11517-019-02076-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-019-02076-w

Keywords

Search

Navigation