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

  • Andrea Giovanni Cutti
  • Alberto Ferrari
  • Pietro Garofalo
  • Michele Raggi
  • Angelo Cappello
  • Adriano Ferrari
Original Article


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.


Gait analysis Kinematics Protocol Ambulatory Inertial sensors Magnetic sensors Cerebral palsy Amputee 



Cerebral palsy


Coordinate system


Right drop-rise


Inertial and magnetic measurement system


Posterior–anterior tilting


Root mean square


Standard error of measurement considering systematic errors


Sensing unit of an IMMS


Joint representing the movements of the Pelvis relative to the Thorax

List of symbols


Hip static flexion angle during Outwalk calibration in supine posture


Knee static flexion angle during Outwalk calibration in supine posture

T1, T2

Physical therapists involved in the reliability study of h and k

Supplementary material

11517_2009_545_MOESM1_ESM.jpg (1.5 mb)
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)
11517_2009_545_MOESM2_ESM.doc (58 kb)
Supplementary material 2 (DOC 59 kb)


  1. 1.
    Andriacchi TP, Alexander EJ (2000) Studies of human locomotion: past, present and future. J Biomech 33(10):1217–1224CrossRefGoogle Scholar
  2. 2.
    Benedetti MG, Catani F, Leardini A et al (1998) Data management in gait analysis for clinical applications. Clin Biomech 13(3):204–215CrossRefGoogle Scholar
  3. 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, PAGoogle Scholar
  4. 4.
    Biomch-L (July-August 2007) Discussion about: “functional methods for the ankle complex”Google Scholar
  5. 5.
    Bobath B, Bobath K (1975) Motor development in the different types of cerebral palsy. William Heinemann Medical Books, LondonGoogle Scholar
  6. 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):171–178CrossRefGoogle Scholar
  7. 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):612–616Google Scholar
  8. 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):169–178CrossRefGoogle Scholar
  9. 9.
    Davis RB III, Ounpuu S, Tyburski D et al (1991) A gait data collection and reduction technique. Hum Mov Sci 10:575–587CrossRefGoogle Scholar
  10. 10.
    De Vet HCW, Terwee CB, Knol DL et al (2006) When to use agreement versus reliability measures. J Clin Epidemiol 59:1033–1039CrossRefGoogle Scholar
  11. 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 37:155–161CrossRefGoogle Scholar
  12. 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. 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 16–20Google Scholar
  14. 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):195–201Google Scholar
  15. 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 26:489–493CrossRefGoogle Scholar
  16. 16.
    Kavanagh JJ, Menz HB (2008) Accelerometry: a technique for quantifying movement patterns during walking. Gait Post 28:1–15CrossRefGoogle Scholar
  17. 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):246–253CrossRefGoogle Scholar
  18. 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 40:2604–2611CrossRefGoogle Scholar
  19. 19.
    Picerno P, Cereatti A, Cappozzo A (2008) Joint kinematics estimate using wearable inertial and magnetic sensing modules. Gait Post 28(4):588–595CrossRefGoogle Scholar
  20. 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 14:595–611Google Scholar
  21. 21.
    Roetenberg D (2006) Inertial and magnetic sensing of human motion. PhD Thesis, Twente UniversityGoogle Scholar
  22. 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, HersheyGoogle Scholar
  23. 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 24:100–109CrossRefGoogle Scholar
  24. 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 16:255–263CrossRefGoogle Scholar
  25. 25.
    Schwartz MH, Rozumalski A (2005) A new method for estimating joint parameters from motion data. J Biomech 38:107–116Google Scholar
  26. 26.
    Sciavicco L, Siciliano B (2000) Modelling and control of robot manipulators. Springer, BerlinGoogle Scholar
  27. 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):137–141CrossRefGoogle Scholar
  28. 28.
    Sutherland DH, Davids JR (1993) Common gait abnormalities of the knee in cerebral palsy. Clin Orthop Relat Res 288:139–147Google Scholar
  29. 29.
    Weir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19(1):231–240CrossRefMathSciNetGoogle Scholar
  30. 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):437–441Google Scholar
  31. 31.
    Woltring HJ (1990) Data processing and error analysis. In: Cappozzo A, Berme N (eds) Biomechanics of human movement. Bertec Corporation, WorthingtonGoogle Scholar
  32. 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):543–548CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2009

Authors and Affiliations

  • Andrea Giovanni Cutti
    • 1
  • Alberto Ferrari
    • 1
    • 2
  • Pietro Garofalo
    • 1
    • 2
  • Michele Raggi
    • 1
  • Angelo Cappello
    • 2
  • Adriano Ferrari
    • 3
  1. 1.INAIL Prostheses CentreVigorso di BudrioItaly
  2. 2.Department of Electronics, Computer Science and SystemsUniversity of BolognaBolognaItaly
  3. 3.Department of NeuroscienceUniversity of Modena and Reggio EmiliaReggio EmiliaItaly

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