Statistical kinematic modelling: concepts and model validity

References

• Al-Obaidi S, Wall JC, Al-Yaqoub A, Al-Ghanim M. 2003. Basic gait parameters: a comparison of reference data for normal subjects 20 to 29 years of age from Kuwait and Scandinavia. J Rehabil Res Dev. 40(4):361–366.
   PubMed Web of Science ®Google Scholar
• Boudarham J, Roche N, Pradon D, Bonnyaud C, Bensmail D, Zory R. 2013. Variations in kinematics during clinical gait analysis in stroke patients. PLoS One. 8(6):e66421.
   PubMed Web of Science ®Google Scholar
• Chau T, Young S, Redekop S. 2005. Managing variability in the summary and comparison of gait data. J Neuroeng Rehabil. 2:22.
   PubMedGoogle Scholar
• Crane EA, Cassidy RB, Rothman ED, Gerstner GE. 2010. Effect of registration on cyclical kinematic data. J Biomech. 43(12):2444–2447.
   PubMedGoogle Scholar
• De Roeck J, Van Houcke J, Almeida D, Galibarov P, De Roeck L, Audenaert EA. 2020. Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge. Front. Bioeng. Biotechnol. 8:233.
   PubMedGoogle Scholar
• Federolf P, Tecante K, Nigg B. 2012. A holistic approach to study the temporal variability in gait. J Biomech. 45(7):1127–1132.
   PubMed Web of Science ®Google Scholar
• Forner-Cordero A, Koopman HJFM, Van Der Helm FCT. 2006. Describing gait as a sequence of states. J Biomech. 39(5):948–957.
   PubMed Web of Science ®Google Scholar
• Happ C, Greven S. 2018. Multivariate functional principal component analysis for data observed on different (dimensional) domains. J Am Stat Assoc. 113(522):649–659.
   Web of Science ®Google Scholar
• Helwig NE, Hong S, Hsiao-Wecksler ET, Polk JD. 2011. Methods to temporally align gait cycle data. J Biomech. 44(3):561–566.
   PubMed Web of Science ®Google Scholar
• Jacobsen LS, Rasmussen J. 2019. Development of a parametric running model in the AnyBody Modeling System based on a pipeline of kinematic running trials [master’s thesis]. Aalborg: Aalborg University.
   Google Scholar
• Laparra V, Jiménez S, Tuia D, Camps-Valls G, Malo J. 2014. Principal polynomial analysis. Int J Neural Syst. 24(7):1440007.
   PubMedGoogle Scholar
• Lukšys, Donatas, Jatužis, Dalius, Kaladytė-Lokominienė, Rūta, Bunevičiūtė, Ramunė, Sawicki, Aleksander, Griškevičius, Julius. 2018. International Conference BIOMDLORE, Differentiation of gait using principal component analysis and application for Parkinson's disease monitoring, Griškevičius, Julius,  Gediminas, Gaidulis, 2018, 28-30 June 2018,
   Google Scholar
• Martínez M, Villagra F, Castellote JM, Pastor MA. 2018. Kinematic and kinetic patterns related to free-walking in Parkinson’s disease. Sensors. 18(12):4224.
   PubMedGoogle Scholar
• Maurer C, von Tscharner V, Samsom M, Baltich J, Nigg BM. 2013. Extraction of basic movement from whole-body movement, based on gait variability. Physiol Rep. 1(3):e00049.
   PubMedGoogle Scholar
• Milovanović I, Popović DB. 2012. Principal component analysis of gait kinematics data in acute and chronic stroke patients. Comput Math Methods Med. 2012:649743 doi:10.1155/2012/649743. PMC: 22400054
   PubMedGoogle Scholar
• Morgan KD, Noehren B. 2018. Identification of knee gait waveform pattern alterations in individuals with patellofemoral pain using fast Fourier transform. PLoS One. 13(12):e0209015.
   PubMedGoogle Scholar
• Phinyomark A, Petri G, Ibáñez-Marcelo E, Osis ST, Ferber R. 2018. Analysis of big data in gait biomechanics: current trends and future directions. J Med Biol Eng. 38(2):244–260.
   PubMed Web of Science ®Google Scholar
• Pulido-Valdeolivas I, Gómez-Andrés D, Martín-Gonzalo JA, López-López J, Gómez-Barrena E, Sánchez Hernández JJ, Rausell E. 2013. Gait parameters in a reference sample of healthy Spanish schoolchildren: Multivariate descriptive statistics and asymmetries observed in left and right cycles. Neurologia. 28(3):145–152.
   PubMedGoogle Scholar
• Ramsay JO, Hooker G, Graves S. 2009a. Exploring variation: functional principal and canonical components analysis. In: Gentleman R, Hornik K, Parmigiani G, editors. Functional data analysis with R Matlab. 1st ed. New York (NY): Springer; pp. 99–115.
   Google Scholar
• Ramsay JO, Hooker G, Graves S. 2009b. Registration: aligning features for samples of curves. In: Gentleman R, Hornik K, Parmigiani G, editors. Functional data analysis with R Matlab. 1st ed. New York (NY): Springer; pp. 117–130.
   Google Scholar
• Sadeghi H, Mathieu PA, Sadeghi S, Labelle H. 2003. Continuous curve registration as an intertrial gait variability reduction technique. IEEE Trans Neural Syst Rehabil Eng. 11(1):24–30.
   PubMed Web of Science ®Google Scholar
• Schreiber C, Moissenet F. 2019. A multimodal dataset of human gait at different walking speeds established on injury-free adult participants. Sci Data. 6(1):111.
   PubMedGoogle Scholar
• Shang HL. 2014. A survey of functional principal component analysis. Melbourne: Monash University.
   Google Scholar
• Sutherland DH. 2002. The evolution of clinical gait analysis: part II kinematics. Gait Posture. 16(2):159–179.
   PubMed Web of Science ®Google Scholar
• Ullah S, Finch CF. 2013. Applications of functional data analysis: a systematic review. BMC Med Res Methodol. 13:43.
   PubMed Web of Science ®Google Scholar
• Van Der Maaten LJP, Postma EO, Van Den Herik HJ. 2009. Dimensionality Reduction: A Comparative Review. Journal of Machine Learning Research. 10:1–41.
   Google Scholar
• Vieira VM. 2012. Permutation tests to estimate significances on principal components analysis. Comput Ecol Softw. 2(2):103–123.
   Google Scholar
• Warmenhoven J, Cobley S, Draper C, Harrison A, Bargary N, Smith R. 2017. Considerations for the use of functional principal components analysis in sports biomechanics: examples from on-water rowing. Sport Biomech. 18(3):317–341.
   PubMedGoogle Scholar
• Warmenhoven J, Bargary N, Liebl D, Harrison A, Robinson MA, Gunning E, Hooker G. 2021. PCA of waveforms and functional PCA: a primer for biomechanics. J Biomech. 116:110106.
   PubMed Web of Science ®Google Scholar
• Zernicke RF, Caldwell G, Roberts EM. 1976. Fitting biomechanical data with cubic spline functions. Res Q Am Alliance Heal Phys Educ Recreat. 47(1):9–19.
   Google Scholar
• Zhang X, Kano M, Li Y. 2018. Principal polynomial analysis for fault detection and diagnosis of industrial processes. IEEE Access. 6:52298–52307.
   Google Scholar