期刊论文详细信息
Journal of Foot and Ankle Research
Validation and reliability testing of a new, fully integrated gait analysis insole
Tim Pohlemann2  Jörg Holstein2  Mika Rollmann2  Michael Roland3  Stefan Döbele1  Rebecca Hell2  Nils Thomas Veith2  Benedikt Johannes Braun2 
[1] BG Trauma Center, Department of Trauma Surgery, Eberhard Karls University Tübingen, Tübingen, Germany;Department of Trauma, Hand and Reconstructive Surgery, Saarland University, Building 57, Kirrbergerstr. 1, Homburg, 66421, Germany;Saarland University, Chair of Applied Mechanics, Saarbruecken, Germany
关键词: Reliability;    Validation;    Integrated insole system;    Gait analysis;   
Others  :  1231221
DOI  :  10.1186/s13047-015-0111-8
 received in 2015-04-04, accepted in 2015-09-14,  发布年份 2015
PDF
【 摘 要 】

Background

A new tool (OpenGo, Moticon GmbH) was introduced to continuously measure kinetic and temporospatial gait parameters independently through an insole over up to 4 weeks. The goal of this study was to investigate the validity and reliability of this new insole system in a group of healthy individuals.

Methods

Gait data were collected from 12 healthy individuals on a treadmill at two different speeds. In total, six trials of three minutes each were performed by every participant. Validation was performed with the FDM-S System (Zebris). Complete sensor data were used for a within test reliability analysis of over 10000 steps. Intraclass correlation was calculated for different gait parameters and analysis of variance performed.

Results

Intraclass correlation for the validation was >0.796 for temporospatial and kinetic gait parameters. No statistical difference was seen between the insole and force plate measurements (difference between means: 36.3 ± 27.19 N; p = 0.19 and 0.027 ± 0.028 s; p = 0.36). Intraclass correlation for the reliability was >0.994 for all parameters measured.

Conclusion

The system is feasible for clinical trials that require step by step as well as grouped analysis of gait over a long period of time. Comparable validity and reliability to a stationary analysis tool has been shown.

【 授权许可】

   
2015 Braun et al.

【 预 览 】
附件列表
Files Size Format View
20151109091239750.pdf 1238KB PDF download
Fig. 4. 54KB Image download
Fig. 3. 54KB Image download
Fig. 2. 64KB Image download
Fig. 1. 22KB Image download
【 图 表 】

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

【 参考文献 】
  • [1]Atallah L, Jones GG, Ali R, Leong JJH, Lo B, Yang GZ. Observing recovery from knee-replacement surgery by using wearable sensors. Proceedings of the 2011 International Conference on Body Sensor Networks. IEEE, Dallas, TX, USA; 2011. (23–25 May 2011)
  • [2]Salarian A, Russmann H, Vingerhoets FJ, Dehollain C, Blanc Y, Burkhard PR et al.. Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 2004; 51(8):1434-43.
  • [3]Simon SR. Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems. J Biomech. 2004; 37(12):1869-80.
  • [4]Wren TA, Gorton GE, Ounpuu S, Tucker CA. Efficacy of clinical gait analysis: A systematic review. Gait Posture. 2011; 34(2):149-53.
  • [5]Godfrey A, Del Din S, Barry G, Mathers JC, Rochester L. Within trial validation and reliability of a single tri-axial accelerometer for gait assessment. Conf Proc IEEE Eng Med Biol Soc. 2014; 2014(1557-170X (Print)):5892-5.
  • [6]Lara J, Godfrey A, Evans E, Heaven B, Brown LJE, Barron E et al.. Towards measurement of the Healthy Ageing Phenotype in lifestyle-based intervention studies. Maturitas. 2013; 76(2):189-99.
  • [7]McCamley J, Donati M, Grimpampi E, Mazza C. An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait and Posture. 2012; 36(2):316-8.
  • [8]Jansen H, Fenwick A, Doht S, Frey S, Meffert R. Clinical outcome and changes in gait pattern after pilon fractures. Int Orthop. 2013; 37(1):51-8.
  • [9]Hurkmans HL, Bussmann JB, Benda E, Verhaar JA, Stam HJ. Effectiveness of audio feedback for partial weight-bearing in and outside the hospital: a randomized controlled trial. Arch Phys Med Rehabil. 2012; 93(4):565-70.
  • [10]Bamberg SJ, Benbasat AY, Scarborough DM, Krebs DE, Paradiso JA. Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans Inf Technol Biomed. 2008; 12(4):413-23.
  • [11]Coutts, Fiona. "Gait analysis in the therapeutic environment." Manual therapy 4.1. 1999; 2–10.
  • [12]Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors. 2012; 12(2):2255-83.
  • [13]Błażkiewicz M, Wiszomirska I, Wit A. Comparison of four methods of calculating the symmetry of spatial-temporal parameters of gait. Acta Bioeng Biomech. 2014; 16(1):29-35.
  • [14]Fan YF, Loan M, Fan YB, Li ZY, Luo DL. Least-action principle in gait. EPL (Europhysics Letters). 2009; 87(5):58003.
  • [15]Gigi R, Mor A, Haim A, Luger E, Melamed E, Beer Y et al.. Deviations in gait metrics in patients with chronic ankle instability. Osteoarthritis Cartilage. 2014; 22:S123.
  • [16]Segal G, Elbaz A, Parsi A, Heller Z, Palmanovich E, Nyska M et al.. Clinical outcomes following ankle fracture: a cross-sectional observational study. J Foot and Ankle Res. 2014; 7(1):50. BioMed Central Full Text
  • [17]Hailey D, Tomie J. An assessment of gait analysis in the rehabilitation of children with walking difficulties. Disabil Rehabil. 2000; 22(6):275-80.
  • [18]Macri F, Marques LF, Backer RC, Santos MJ, Belangero WD. Validation of a standardised gait score to predict the healing of tibial fractures. J Bone Joint Surg. 2012; 94(4):544-8.
  • [19]Braun BJ, Rollmann M, Veith N, Pohlemann T. Fracture healing redefined. Medical Hypotheses. 2015
  • [20]Goodship AE, Kenwright J. The influence of induced micromovement upon the healing of experimental tibial fractures. J Bone Joint Surg. 1985; 67(4):650-5.
  • [21]Marsell R, Einhorn TA. The biology of fracture healing. Injury. 2011; 42(6):551-5.
  • [22]Abdul Razak AH, Zayegh A, Begg RK, Wahab Y. Foot plantar pressure measurement system: a review. Sensors. 2012; 12(7):9884-912.
  • [23]Benocci M, Rocchi L, Farella E, Chiari L, Benini L, editors. A wireless system for gait and posture analysis based on pressure insoles and Inertial Measurement Units. Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference on; 2009: IEEE.
  • [24]Feng Y, Ge Y, Song Q, editors. A human identification method based on dynamic plantar pressure distribution. Information and Automation (ICIA), 2011 IEEE International Conference on; 2011; New York City, NY, USA: IEEE. 329–32.
  • [25]Morris Bamberg SJ, Benbasat AY, Moxley Scarborough D, Krebs DE, Paradiso JA. Gait Analysis Using a Shoe-Integrated Wireless Sensor System. IEEE Trans Inf Technol Biomed. 2008; 12:413-23.
  • [26]Hartmann A, Murer K, de Bie RA, de Bruin ED. Reproducibility of spatio-temporal gait parameters under different conditions in older adults using a trunk tri-axial accelerometer system. Gait Posture. 2009; 30(3):351-5.
  • [27]Menz HB, Latt MD, Tiedemann A, Mun San Kwan M, Lord SR. Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. Gait Posture. 2004; 20(1):20-5.
  • [28]Welk GJ, Schaben JA, Morrow JR. Reliability of accelerometry-based activity monitors: a generalizability study. Med Sci Sports Exerc. 2004; 36(9):1637-45.
  • [29]Lee SJ, Hidler J. Biomechanics of overground vs. treadmill walking in healthy individuals. J Appl Physiol. 2008; 104(3):747-55.
  • [30]Menz HB. Two feet, or one person? Problems associated with statistical analysis of paired data in foot and ankle medicine. The Foot. 2004; 14(1):2-5.
  文献评价指标  
  下载次数:57次 浏览次数:86次