Advertisement

The Effects of Passive Ankle-Foot Orthotic Devices’ Stiffness – Application and Limitation of 2D Inverted Pendulum Gait Model

  • Qianyi Fu
  • Thomas Armstrong
  • Albert Shih
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 824)

Abstract

This paper presents a pilot study for the development of a lumped parameter model that can facilitate the interpretation of gait data and design AFOs. A 2-D kinematic link model was constructed first and then adapted into a lumped parameter model with inverted pendulum approach. A patient with ankle disability was recruited and performed three walks with different ankle stiffness support: no AFO, medium-stiff (3.6 N·m/deg) AFOs, and stiff (4.5 N·m/deg) AFOs. An inertia measurement unit (IMU) system was used to measure the sagittal kinematics of the impaired and unimpaired limbs, and the data collected was used as inputs for the proposed gait model. Good agreement between observed and predicted swing time of the unimpaired side based on given AFO stiffness was achieved.

Keywords

Inverted pendulum AFO stiffness Swing time 

References

  1. Apkarian J, Naumann S, Cairns B (1989) A three-dimensional kinematic and dynamic model of the lower limb. J Biomech 22(2):143–155CrossRefGoogle Scholar
  2. Browning RC, Baker EA, Herron JA, Kram R (2006) Effects of obesity and sex on the energetic cost and preferred speed of walking. J Appl Physiol 100(2):390–398CrossRefGoogle Scholar
  3. Cavagna GA, Thys H, Zamboni A (1976) The sources of external work in level walking and running. J Physiol 262(3):639–657CrossRefGoogle Scholar
  4. Garcia M, Chatterjee A, Ruina A, Coleman M (1998) The simplest walking model: stability, complexity, and scaling. J Biomech Eng 120(2):281–288CrossRefGoogle Scholar
  5. Kadaba MP, Ramakrishnan HK, Wootten ME (1990) Measurement of lower extremity kinematics during level walking. J Orthop Res 8(3):383–392CrossRefGoogle Scholar
  6. Martin AE, Schmiedeler JP (2014) Predicting human walking gaits with a simple planar model. J Biomech 47(6):1416–1421CrossRefGoogle Scholar
  7. McGrath M, Howard D, Baker R (2015) The strengths and weaknesses of inverted pendulum models of human walking. Gait Posture 41(2):389–394CrossRefGoogle Scholar
  8. Mohler BJ, Thompson WB, Creem-Regehr SH, Pick HL Jr, Warren WH Jr (2007) Visual flow influences gait transition speed and preferred walking speed. Exp Brain Res 181(2):221–228CrossRefGoogle Scholar
  9. Rogers JC, Irrgang JJ (2003) Measures of adult lower extremity function: The American Academy of Orthopedic Surgeons Lower Limb Questionnaire, The Activities of Daily Living Scale of the Knee Outcome Survey (ADLS), Foot Function Index (FFI), Functional Assessment System (FAS), Harris Hip Score (HHS), Index of Severity for Hip Osteoarthritis (ISH), Index of Severity for Knee Osteoarthritis (ISK), Knee Injury and Osteoarthritis Outcome Score (KOOS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC™). Arthritis Care Res 49(S5)CrossRefGoogle Scholar
  10. Saunders J, Inman V, Eberhart H (1953) The major determinants in normal and pathological gait. Am J Bone Joint Surg 35:543–558CrossRefGoogle Scholar
  11. Schrank ES, Hitch L, Wallace K, Moore R, Stanhope SJ (2013) Assessment of a virtual functional prototyping process for the rapid manufacture of passive-dynamic ankle-foot orthoses. J Biomech Eng 135(10):101011CrossRefGoogle Scholar
  12. Winter DA (1983) Knee flexion during stance as a determinant of inefficient walking. Phys Ther 63(3):331–333MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA

Personalised recommendations