Skip to main content
Log in

‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors

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

Abstract

A protocol named Outwalk was developed to easily measure the thorax–pelvis and lower-limb 3D kinematics on children with cerebral palsy (CP) and amputees during gait in free-living conditions, by means of an Inertial and Magnetic Measurement System (IMMS). Outwalk defines the anatomical/functional coordinate systems (CS) for each body segment through three steps: (1) positioning the sensing units (SUs) of the IMMS on the subjects’ thorax, pelvis, thighs, shanks and feet, following simple rules; (2) computing the orientation of the mean flexion–extension axis of the knees; (3) measuring the SUs’ orientation while the subject’s body is oriented in a predefined posture, either upright or supine. If the supine posture is chosen, e.g. when spasticity does not allow to maintain the upright posture, hips and knees static flexion angles must be measured through a standard goniometer and input into the equations that define Outwalk anatomical CSs. In order to test for the inter-rater measurement reliability of these angles, a study was carried out involving nine healthy children (7.9 ± 2 years old) and two physical therapists as raters. Results showed RMS error of 1.4° and 1.8° and a negligible worst-case standard error of measurement of 2.0° and 2.5° for hip and knee angles, respectively. Results were thus smaller than those reported for the same measures when performed through an optoelectronic system with the CAST protocol and support the beginning of clinical trials of Outwalk with children with CP.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

CP:

Cerebral palsy

CS:

Coordinate system

DR:

Right drop-rise

IMMS:

Inertial and magnetic measurement system

PAT:

Posterior–anterior tilting

RMS:

Root mean square

SEMse :

Standard error of measurement considering systematic errors

SU:

Sensing unit of an IMMS

TP:

Joint representing the movements of the Pelvis relative to the Thorax

h :

Hip static flexion angle during Outwalk calibration in supine posture

k :

Knee static flexion angle during Outwalk calibration in supine posture

T 1, T 2 :

Physical therapists involved in the reliability study of h and k

References

  1. Andriacchi TP, Alexander EJ (2000) Studies of human locomotion: past, present and future. J Biomech 33(10):1217–1224

    Article  Google Scholar 

  2. Benedetti MG, Catani F, Leardini A et al (1998) Data management in gait analysis for clinical applications. Clin Biomech 13(3):204–215

    Article  Google Scholar 

  3. Best R, Begg R (2006) Overview of motion analysis and gait feature. In: Begg R, Palaniswami M (eds) Computational intelligence for movement sciences. IGP, Hershey, PA

  4. Biomch-L (July-August 2007) Discussion about: “functional methods for the ankle complex”

  5. Bobath B, Bobath K (1975) Motor development in the different types of cerebral palsy. William Heinemann Medical Books, London

    Google Scholar 

  6. Cappozzo A, Catani F, Della Croce U et al (1995) Position and orientation in space of bones during movement: anatomical frame definition and determination. Clin Biomech 10(4):171–178

    Article  Google Scholar 

  7. Chang FM, Seidl AJ, Muthusamy K et al (2006) Effectiveness of instrumented gait analysis in children with cerebral palsy—comparison of outcomes. J Pediatr Orthop 26(5):612–616

    Google Scholar 

  8. Cutti AG, Giovanardi A, Rocchi L et al (2008) Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors. Med Biol Eng Comput 46(2):169–178

    Article  Google Scholar 

  9. Davis RB III, Ounpuu S, Tyburski D et al (1991) A gait data collection and reduction technique. Hum Mov Sci 10:575–587

    Article  Google Scholar 

  10. De Vet HCW, Terwee CB, Knol DL et al (2006) When to use agreement versus reliability measures. J Clin Epidemiol 59:1033–1039

    Article  Google Scholar 

  11. Della Croce U, Cappozzo A, Kerrigan DC (1999) Pelvis and lower limb anatomical landmark calibration precision and its propagation to bone geometry and joint angles. Med Biol Eng Comput 37:155–161

    Article  Google Scholar 

  12. Ferrari A, Cutti AG, Garofalo P et al (2009) First in-vivo assessment of ‘outwalk’—a novel protocol for clinical gait analysis based on inertial & magnetic sensors. Med Biol Eng Comput. doi:10.1007/s11517-009-0544-y

  13. Ferrari A (1993) The use of epidemiology in disabilities: criteria for classification. In: Bottos M, Scrutton D, Ferrari A, Neville BGR (eds) The restored infant. Fisioray, Firenze, pp 16–20

  14. Ferrari A, Alboresi S, Muzzini S et al (2008) The term diplegia should be enhanced. Part I: a new rehabilitation oriented classification of cerebral palsy. Eur J Phys Rehabil Med 44(2):195–201

    Google Scholar 

  15. Kaufman KR, Levine JA, Brey RH et al (2007) Gait and balance of transfemoral amputees using passive mechanical and microprocessor-controlled prosthetic knees. Gait Post 26:489–493

    Article  Google Scholar 

  16. Kavanagh JJ, Menz HB (2008) Accelerometry: a technique for quantifying movement patterns during walking. Gait Post 28:1–15

    Article  Google Scholar 

  17. Kontaxis A, Cutti AG, Johnson G et al (2009) A framework for the definition of standardized protocols for measuring upper-extremity kinematics. Clin Biomech 24(3):246–253

    Article  Google Scholar 

  18. O’Donovan KJ, Kamnik R, O’Keeffe DT et al (2007) An inertial and magnetic sensor based technique for joint angle measurement. J Biomech 40:2604–2611

    Article  Google Scholar 

  19. Picerno P, Cereatti A, Cappozzo A (2008) Joint kinematics estimate using wearable inertial and magnetic sensing modules. Gait Post 28(4):588–595

    Article  Google Scholar 

  20. Ramsey DK, Wretenberg PF (1999) Biomechanics of the knee: methodological considerations in the in vivo kinematic analysis of the tibiofemoral and patellofemoral joint. J Biomech 14:595–611

    Google Scholar 

  21. Roetenberg D (2006) Inertial and magnetic sensing of human motion. PhD Thesis, Twente University

  22. Sabatini AM (2006) Inertial sensing in biomechanics: a survey of computational techniques bridging motion analysis and personal navigation. In: Begg R, Palaniswami M (eds) Computational intelligence for movement sciences. IGP, Hershey

  23. Schache AG, Baker R, Lamoreux LW (2006) Defining the knee joint flexion–extension axis for purposes of quantitative gait analysis: an evaluation of methods. Gait Post 24:100–109

    Article  Google Scholar 

  24. Schmalz T, Blumentritt S, Jarasch R (2002) Energy expenditure and biomechanical characteristics of lower limb amputee gait: the influence of prosthetic alignment and different prosthetic components. Gait Post 16:255–263

    Article  Google Scholar 

  25. Schwartz MH, Rozumalski A (2005) A new method for estimating joint parameters from motion data. J Biomech 38:107–116

    Google Scholar 

  26. Sciavicco L, Siciliano B (2000) Modelling and control of robot manipulators. Springer, Berlin

  27. Stagni R, Fantozzi S, Cappello A (2006) Propagation of anatomical landmark misplacement to knee kinematics: performance of single and double calibration. Gait Post 24(2):137–141

    Article  Google Scholar 

  28. Sutherland DH, Davids JR (1993) Common gait abnormalities of the knee in cerebral palsy. Clin Orthop Relat Res 288:139–147

    Google Scholar 

  29. Weir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19(1):231–240

    Article  MathSciNet  Google Scholar 

  30. Winters F, Gage JR, Hicks R (1987) Gait patterns in spastic hemiplegia in children and young adults. J Bone Joint Surg Am 69(3):437–441

    Google Scholar 

  31. Woltring HJ (1990) Data processing and error analysis. In: Cappozzo A, Berme N (eds) Biomechanics of human movement. Bertec Corporation, Worthington

  32. Wu G, Siegler S, Allard P et al (2002) ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion. Part I. Ankle, hip and spine. J Biomech 35(4):543–548

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Giovanni Cutti.

Electronic supplementary material

Below is the link to the electronic supplementary material.

11517_2009_545_MOESM1_ESM.jpg

Xsens system. (a) Data-logger (Xbus Master) with two SUs connected; (b) an SU with its local CS, in the global (earth-based) CS (JPG 1554 kb)

Supplementary material 2 (DOC 59 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cutti, A.G., Ferrari, A., Garofalo, P. et al. ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput 48, 17–25 (2010). https://doi.org/10.1007/s11517-009-0545-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-009-0545-x

Keywords

Navigation