Intelligent Service Robotics

, Volume 10, Issue 4, pp 323–332 | Cite as

Symmetric lifting posture recognition of skilled experts with linear discriminant analysis by center-of-pressure velocity

  • Hieyong JeongEmail author
  • Yuko Ohno
Original Research Paper


Although it has been well known that novices should train a good lifting posture, there was little way to recognize whether the current posture was good or not based on measured data. The purpose of this paper was to classify the difference between skilled experts working at a freight transport company and unskilled novices without any experience during symmetric lifting by using center-of-pressure (CoP) velocities. All the human subjects performed symmetric lifting experiments with closed eyes; the experiments involved lifting loads (6 and 18 kg) to the upside. Time series data of the CoP position were measured, using a Wii Balance Board, and then, the CoP velocities were calculated. The linear discriminant analysis (LDA) was designed by seven indices which were derived from CoP velocities that reflected the center-of-mass acceleration. The result indicated that the designed LDA discriminated the difference in posture between the two groups with the low error rate (0.100 and 0.017) for classification under 6 and 18 kg. Based on measurement results of CoP trajectories, we inferred that the difference in the CoP velocities between the two groups could be attributed to the difference in the balance ability which means that most skilled experts place their body weight on their rearfeet during symmetric lifting. The LDA classifier designed by CoP velocities was helpful for recognition of the difference between skilled experts and unskilled novices during symmetric lifting. Because the skillful characteristics of experts may be responsible for the lightening of the burden on the waist during lifting, it is considered for the regular check of posture to be helpful for reducing the ratio of occupational low back pain at the workplace.


Center-of-pressure velocity Linear discriminant analysis Recognition of lifting posture Symmetric lifting 

Supplementary material

11370_2017_227_MOESM1_ESM.pdf (27.9 mb)
Supplementary material 1 (pdf 28550 KB)
11370_2017_227_MOESM2_ESM.avi (25.5 mb)
Supplementary material 2 (avi 26089 KB)

Supplementary material 3 (avi 43378 KB)

Supplementary material 4 (avi 43437 KB)


  1. 1.
    John Schubbe DC (2004) Good Posture Helps Reduce Back Pain. A Peer Reviewed Article.
  2. 2.
    Jeong H, Yamada K, Kido M, Okada S, Nomura T, Ohno Y (2016) Analysis of difference in center-of-pressure positions between experts and novices during asymmetric lifting. IEEE J Transl Eng Health Med. doi: 10.1109/JTEHM.2016.2599185 Google Scholar
  3. 3.
    Nachemson A (1975) Towards a better understanding of low back pain: a review of the mechanics of the lumbar disc. Rheumatol Rehabil 14:129–143CrossRefGoogle Scholar
  4. 4.
    Lee J, Park S, Yoo W (2011) Change in craniocervical and trunk flexion angles and gluteal pressure during VDT work with continuous cross-legged sitting. J Occup Health 53:350–355CrossRefGoogle Scholar
  5. 5.
    Krag MH, Seroussi RE, Wilder DG et al (1987) Internal displacement distribution from in vitro loading of human thoracic and lumbar spinal motion segments: experimental results and theoretical predictions. Spine 12:1001–1007CrossRefGoogle Scholar
  6. 6.
    Schmidt H, Kettler A, Heuer F, Simon U, Claes L, Wilke H (2007) Intradiscal pressure, shear strain, and fiber strain in the intervertebral disc under combined loading. Spine 32(7):748–755CrossRefGoogle Scholar
  7. 7.
    Waters TR, Putz-Anderson V, Garg A, Fine L (1993) Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics 36(7):749–776CrossRefGoogle Scholar
  8. 8.
    Benda BJ, Riley PO, Krebs DE (1994) Biomechanical relationship between center of gravity and center of pressure during standing. IEEE Trans Rehabil Eng 2(1):3–10CrossRefGoogle Scholar
  9. 9.
    Shimba T (1984) An estimation of center of gravity from force platform data. J Biomech 17:53–60CrossRefGoogle Scholar
  10. 10.
    Masani K, Vette A, Abe M, Nakazawa K (2014) Center of pressure velocity reflects body acceleration rather than body velocity during quiet standing. Gait Posture 39:946–952CrossRefGoogle Scholar
  11. 11.
    Masani K, Vette AH, Kouzaki M, Kanehisa H, Fukunaga T, Popovic MR (2007) Larger center of pressure minus center of gravity in the elderly induces larger body acceleration during quiet standing. Neurosci Lett 422(3):202–206CrossRefGoogle Scholar
  12. 12.
    Konoike Transport Company. Accessed 5 Sept 2016
  13. 13.
    Ouchi Y, Okada H, Yoshikawa E, Nobezawa S, Futatsubashi M (1999) Brain activation during maintenance of standing postures in humans. Brain 122(2):329–338CrossRefGoogle Scholar
  14. 14.
    Bartlett HL, Ting LH, Bingham JT (2014) Accuracy of force and center of pressure measures of the Wii Balance Board. Gait Posture 39(1):224–228. doi: 10.1016/j.gaitpost.2013.07.010 Please check and confirm inserted the page numbers are correctly identified for reference [14]CrossRefGoogle Scholar
  15. 15.
    Clark RA, Bryant AL, Pua Y, McCrory P, Bennell K, Hunt M (2010) Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance. Gait Posture 31(3):307–310CrossRefGoogle Scholar
  16. 16.
    Park D, Lee G (2014) Validity and reliability of balance assessment software using the Nintendo Wii balance board: usability and validation. J Neuroeng Rehabil 11:99. doi: 10.1186/1743-0003-11-99 CrossRefGoogle Scholar
  17. 17.
    Huurnink A, Fransz DP, Kingma I, van Dieen JH (2013) Comparison of a laboratory grade force platform with a Nintendo Wii Balance Board on measurement of postural control in single-leg stance balance tasks. J Biomech 46(7):1392–1395CrossRefGoogle Scholar
  18. 18.
    Vukobratovic M, Borovac B (2004) Zero-moment point: thirty five years of its life. Int J HR 1(1):157–173 (World Scientific Publishing Company)Google Scholar
  19. 19.
    Jeong H, Kido M, Ohno Y (2016) Linear discriminant analysis for symmetric lifting recognition of skilled logistic experts by center of pressure trajectory. In: The 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC16), August 16–20, pp 4573–4576Google Scholar
  20. 20.
    Scholkopf B, Smola AJ (2002) Learning with kernels, support vector machines, regularization, optimization, and beyond. Massachusetts Institute of Technology Press, ISBN-10: 0262194759, ISBN-13: 978-0262194754Google Scholar
  21. 21.
    Murphy KP (2012) Machine learning, a probabilistic perspective. Massachusetts Institute of Technology Press, ISBN-10: 0262018020, ISBN-13: 978-0262018029Google Scholar
  22. 22.
    Powers DMW (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation (PDF). J Mach Learn Technol 2(1):37–63MathSciNetGoogle Scholar
  23. 23.
    Martínez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233CrossRefGoogle Scholar
  24. 24.
    Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Hum Genet 7(2):179–188Google Scholar
  25. 25.
    Rao CR (1948) The utilization of multiple measurements in problems of biological classification. J R Stat Soc Series B Methodol 10(2):159–203MathSciNetzbMATHGoogle Scholar
  26. 26.
    Raschka S (2016) Python machine learning. Packt Publishing Ltd. ISBN-10: 1783555130, ISBN-13: 978-1783555130Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Robotics and Design for Innovative Healthcare, Graduate School of MedicineOsaka UniveristySuitaJapan
  2. 2.Division of Health Sciences, Graduate School of MedicineOsaka UniversityOsakaJapan

Personalised recommendations