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ÇOK BOYUTLU KİNEMATİK VERİLERİN ANALİZİNDE TEMEL BİLEŞENLER ANALİZİ YÖNTEMİNİN KULLANILMASI.

Yıl 2007, Cilt: 18 Sayı: 4, 156 - 166, 01.08.2007

Öz

Kaynakça

  • Ariel BG. (1968). ARIEL Performance Analysis System (APAS) [Bilgisayar yazılımı]. Ariel Dynamics, San Diego: USA.
  • Aggarwal JK, Cai, Q. (1999). Human motion analysis: a review. Computer Vision and Image Understanding, 73, 428–440,
  • Alexander RMc. (2003). Modelling approach- es in biomechanics. Phil. Trans. R. Soc. Lond, 358, 1429-1435.
  • Bokman L, Syungkwon R, Park FC. (2005). 2005 IEEE International Conference on Robotics and Automation: Movement primitives, principal component analysis, and the efficient generation of natural motions. Barcelona, Spain.
  • Boyle RD. (1998). Scaling additional contri- butions to principal component analysis. Pattern Recogn Lett, 31, 2047–2053.
  • Chen CY, Lee RCT. (1995). A near pattern- matching scheme based upon principal components analysis. Pattern Recogn Lett, 16, 339–345. Medicine Congress: Using PCA for data reduction in different movement pattern. Bulgaristan: Albena Resort.
  • Çilli M, Arıtan, S. (2005). 10th Annual Con- gress European College of Sport Sci- ence: PCA application for modeling and simulation of running patterns. Sirbistan: Belgrade.
  • Daffertshofer A, Lamoth CJC, Meijer DG, Beek PJ. (2004) . PCA in studying coor- dination and variability: a tutorial. Clinical Biomechanics, 19, 415-428.
  • Deluzio KJ, Wyss UP, Costigan PA, Zee B. (2000). Canadian Society for Biomechan- ics. Biomechanical factors in gait of knee osteoarthritis patients, Montreal: QC
  • Gavrila DM. (1999). The visual analysis of hu- man movement: A survey. Computer Vi- sion and Understanding, 73, 82-98.
  • Hatze H. (1980). A mathematical model for the computational determination of param- eter values of anthropomorphic segments.
  • Journal of Biomechanics, 13, 833-843.
  • Hollands K, Wing A, Daffertshofer A. (2004). 3rd IEEE EMBSS UK &RI PostGraduate Conference in Biomedical Engineering & Medical Physics: Principal components analysis of contemporary dance kinemat- ics. Southampton: University of South- ampton.
  • Jackson JE. (1991). A User Guide to Principle Component. New York: John Wily & Sons Inc.
  • Johansson G. (1973). Visual perception of bio- logical motion and a model for its analy- sis. Perceptionand Psychophysics, 14, 201-211.
  • Johansson G. (1975). Visual motion perception. Scientific American, 232, 76–88.
  • Karhunen J, Joutsensalo J. (1995). General- izations of principal component analysis, optimization problems, and neural net- works. Neural Networks, 8, 549–562.
  • Khalaf KA, Parnianpour M, Sparto PJ, Barin K. (1999). Feature extraction and quan- tification of the variability of dynamic performance profiles due to the different sagittal lift characteristics. IEEE Trans- actions on Rehabilitation Engineering, 7, 278–288.
  • Kudoh S. (2004). Balance maintenance for human-like models with whole body mo- tion. (Doctral Thesis, 2005) . The Depart- ment of Computer Science the Graduate School of Information Science and Tech- nology the University of Tokyo.
  • Manly BFJ. (1992). Multivariate Statistical Methods A Premier. London: Chapman & Hall.
  • MATLAB [Bilgisayar Yazılımı] , Mathworks Inc., Natick: Ma, USA
  • Murase H, Nayar SK. (1993). In: Fall Sym- posium: Machine Learning In Computer
  • Vision: Learning and recognition of 3-D
  • objects from brightness images. Raleigh:
  • North Carolina, Fall.
  • Nayar SK, Poggio T. (1996). Early Visual Learn- ing. New York: Oxford University Press,
  • Neagoe VE, Iatan IF., Grunwald S. (2003). AMIA
  • Annu Symp Proc: A neuro-fuzzy approach
  • to classification of ECG signals for isch
  • emic heart disease diagnosis, 494–498.
  • Perez MA, Nussbaum MA. (2003) . Principal component analysis as an evaluation and classification tool for lower torso sEMG data. Journal of Biomechanics, (36), 1225 –1229.
  • Pinkowski B. (1997). Principal component analysis of speech spectrogram images. Pattern Recogn, 30, 777–787.
  • Ramsay JO, Munhall KG, Gracco VL, Ostry DJ. (1996). Functional data analyses of lip mo- tion. J Acoust Soc Am., 99, 3718-3727.
  • Rodtook S, Rangsanseri Y. (2001). International Conference of information technology cod- ing and computing: Adaptive thresholding of document images based on Laplacian sign. Las Vegas: Nevada.
  • Rosales R, Scarloff S. (2000). IEEE Computer Society Workshop on Human Motion: Specialized mappings and the estimation of human body pose from a single image, Austin: TX.
  • Sanger TD. (2000). Human arm movements described by a low-dimensional superpo- sition of principal components. The Jour- nal of Neuroscience, 20, 1066–1072
  • Sanjeev D, Nandedkar D, Sanders B. (1989). Principal component analysis of the fea- tures of concentric needle EMG motor unit action potentials. Muscle&Nerve, 288-293.
  • Santello M, Flanders M and Soechting JF. (1998) . Postural hand synergies for tool use. The Journal of Neuroscience, 18, 10105–10115.
  • Sidenbladh H, Black MJ, Sigal L. (2002). Eu- ropean Conference On Computer Vision: Implict probabalistic models of human motion for synthesis and tracking. Co- penhagen.
  • Troje FN. (2002a). Decomposing biological motion: A frame work for analysis and synthesis of human gait pattern. Journal of Vision, 2, 371-387.
  • Troje FN. (2002b). In: R. P. Würtz, M. Lappe (Eds.), Dynamic Perception: The little dif- ference: Fourier based synthesis of gen- derspecific biological motion. Berlin: AKA Press, 115-120.
  • Yamato J, Ohya J, Ishii K. (1992) . Proc. IEEE Conf. CVPR: Recognizing human action in time sequential images using Hidden Markov Model. Champaign: IL, 379–385.
  • Yeadon MR. (1990). The simulation of aerial movement-II. A mathematical inertia model of the human body. Journal of Bio- mechanics, 23, 67-74.

ÇOK BOYUTLU KİNEMATİK VERİLERİN ANALİZİNDE TEMEL BİLEŞENLER ANALİZİ YÖNTEMİNİN KULLANILMASI.

Yıl 2007, Cilt: 18 Sayı: 4, 156 - 166, 01.08.2007

Öz

İnsan hareketlerinin analizi ve tanımlanmasındaki zorluk insan eklemlerindeki yüksek serbestlik derecesinden ve büyük boyutlardaki kinematik veri setlerinden kaynaklanmaktadır. Bu nedenle hareket yapılarının sayısal olarak ifade edilmesi ve anlam kazandırılmasında yeni yaklaşımlar denenmektedir. Birçok farklı disiplinlerce sıklıkla kullanılmasına rağmen, hareket yapılarına ait kinematik verilerin incelenmesinde henüz Temel Bileşenler Analizi (TBA) kullanımı çok yenidir. Bu çalışmada farklı hareket yapılarında konum bilgilerine TBA tekniği uygulanarak, kinematik veri setlerinin boyutlarının indirgenmesi ve belirlenen temel bileşenlerin incelenmesi amaçlanmıştır. Koşu, atma, atlama, ayakla topa vurma, ağırlık kaldırma ve cimnastik gibi temel sportif hareket tiplerinde konum bilgileri ile temsil edilen anlık duruşlardan oluşan kinematik veri setlerine TBA uygulanmıştır. Hareketin yapısı karmaşıklaştıkça ilk temel bileşenin (tb) toplam varyansta temsil ettiği oranın azaldığı, bunun yanında incelenen tüm hareket tiplerinde ilk 8 tb’ nin hareket yapılarının yaklaşık %98’ini temsil edebildiği belirlenmiştir. İnsan hareketlerinin analizinde bu türden yeni yaklaşımların önemli katkılar sağlayacağı öngörülmektedir. Koşu hareketinde olduğu gibi daha fazla sayıdaki bireye ve farklı durumlara ait hareket yapılarına ait verilerde belirlenen tb’lerin, cinsiyet, koşu hızı, yorgunluk, fiziksel yapı, sakatlık, tekniğin düzgünlüğü vb gibi farklı durumlara bağlı olarak sınıflama, analiz, teşhis, karşılaştırma yada hareket durumları arasında harmanlama yapılabilmesine olanak sağlayacağı düşünülmektedir

Kaynakça

  • Ariel BG. (1968). ARIEL Performance Analysis System (APAS) [Bilgisayar yazılımı]. Ariel Dynamics, San Diego: USA.
  • Aggarwal JK, Cai, Q. (1999). Human motion analysis: a review. Computer Vision and Image Understanding, 73, 428–440,
  • Alexander RMc. (2003). Modelling approach- es in biomechanics. Phil. Trans. R. Soc. Lond, 358, 1429-1435.
  • Bokman L, Syungkwon R, Park FC. (2005). 2005 IEEE International Conference on Robotics and Automation: Movement primitives, principal component analysis, and the efficient generation of natural motions. Barcelona, Spain.
  • Boyle RD. (1998). Scaling additional contri- butions to principal component analysis. Pattern Recogn Lett, 31, 2047–2053.
  • Chen CY, Lee RCT. (1995). A near pattern- matching scheme based upon principal components analysis. Pattern Recogn Lett, 16, 339–345. Medicine Congress: Using PCA for data reduction in different movement pattern. Bulgaristan: Albena Resort.
  • Çilli M, Arıtan, S. (2005). 10th Annual Con- gress European College of Sport Sci- ence: PCA application for modeling and simulation of running patterns. Sirbistan: Belgrade.
  • Daffertshofer A, Lamoth CJC, Meijer DG, Beek PJ. (2004) . PCA in studying coor- dination and variability: a tutorial. Clinical Biomechanics, 19, 415-428.
  • Deluzio KJ, Wyss UP, Costigan PA, Zee B. (2000). Canadian Society for Biomechan- ics. Biomechanical factors in gait of knee osteoarthritis patients, Montreal: QC
  • Gavrila DM. (1999). The visual analysis of hu- man movement: A survey. Computer Vi- sion and Understanding, 73, 82-98.
  • Hatze H. (1980). A mathematical model for the computational determination of param- eter values of anthropomorphic segments.
  • Journal of Biomechanics, 13, 833-843.
  • Hollands K, Wing A, Daffertshofer A. (2004). 3rd IEEE EMBSS UK &RI PostGraduate Conference in Biomedical Engineering & Medical Physics: Principal components analysis of contemporary dance kinemat- ics. Southampton: University of South- ampton.
  • Jackson JE. (1991). A User Guide to Principle Component. New York: John Wily & Sons Inc.
  • Johansson G. (1973). Visual perception of bio- logical motion and a model for its analy- sis. Perceptionand Psychophysics, 14, 201-211.
  • Johansson G. (1975). Visual motion perception. Scientific American, 232, 76–88.
  • Karhunen J, Joutsensalo J. (1995). General- izations of principal component analysis, optimization problems, and neural net- works. Neural Networks, 8, 549–562.
  • Khalaf KA, Parnianpour M, Sparto PJ, Barin K. (1999). Feature extraction and quan- tification of the variability of dynamic performance profiles due to the different sagittal lift characteristics. IEEE Trans- actions on Rehabilitation Engineering, 7, 278–288.
  • Kudoh S. (2004). Balance maintenance for human-like models with whole body mo- tion. (Doctral Thesis, 2005) . The Depart- ment of Computer Science the Graduate School of Information Science and Tech- nology the University of Tokyo.
  • Manly BFJ. (1992). Multivariate Statistical Methods A Premier. London: Chapman & Hall.
  • MATLAB [Bilgisayar Yazılımı] , Mathworks Inc., Natick: Ma, USA
  • Murase H, Nayar SK. (1993). In: Fall Sym- posium: Machine Learning In Computer
  • Vision: Learning and recognition of 3-D
  • objects from brightness images. Raleigh:
  • North Carolina, Fall.
  • Nayar SK, Poggio T. (1996). Early Visual Learn- ing. New York: Oxford University Press,
  • Neagoe VE, Iatan IF., Grunwald S. (2003). AMIA
  • Annu Symp Proc: A neuro-fuzzy approach
  • to classification of ECG signals for isch
  • emic heart disease diagnosis, 494–498.
  • Perez MA, Nussbaum MA. (2003) . Principal component analysis as an evaluation and classification tool for lower torso sEMG data. Journal of Biomechanics, (36), 1225 –1229.
  • Pinkowski B. (1997). Principal component analysis of speech spectrogram images. Pattern Recogn, 30, 777–787.
  • Ramsay JO, Munhall KG, Gracco VL, Ostry DJ. (1996). Functional data analyses of lip mo- tion. J Acoust Soc Am., 99, 3718-3727.
  • Rodtook S, Rangsanseri Y. (2001). International Conference of information technology cod- ing and computing: Adaptive thresholding of document images based on Laplacian sign. Las Vegas: Nevada.
  • Rosales R, Scarloff S. (2000). IEEE Computer Society Workshop on Human Motion: Specialized mappings and the estimation of human body pose from a single image, Austin: TX.
  • Sanger TD. (2000). Human arm movements described by a low-dimensional superpo- sition of principal components. The Jour- nal of Neuroscience, 20, 1066–1072
  • Sanjeev D, Nandedkar D, Sanders B. (1989). Principal component analysis of the fea- tures of concentric needle EMG motor unit action potentials. Muscle&Nerve, 288-293.
  • Santello M, Flanders M and Soechting JF. (1998) . Postural hand synergies for tool use. The Journal of Neuroscience, 18, 10105–10115.
  • Sidenbladh H, Black MJ, Sigal L. (2002). Eu- ropean Conference On Computer Vision: Implict probabalistic models of human motion for synthesis and tracking. Co- penhagen.
  • Troje FN. (2002a). Decomposing biological motion: A frame work for analysis and synthesis of human gait pattern. Journal of Vision, 2, 371-387.
  • Troje FN. (2002b). In: R. P. Würtz, M. Lappe (Eds.), Dynamic Perception: The little dif- ference: Fourier based synthesis of gen- derspecific biological motion. Berlin: AKA Press, 115-120.
  • Yamato J, Ohya J, Ishii K. (1992) . Proc. IEEE Conf. CVPR: Recognizing human action in time sequential images using Hidden Markov Model. Champaign: IL, 379–385.
  • Yeadon MR. (1990). The simulation of aerial movement-II. A mathematical inertia model of the human body. Journal of Bio- mechanics, 23, 67-74.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Murat Çilli Bu kişi benim

Serdar Arıtan Bu kişi benim

Yayımlanma Tarihi 1 Ağustos 2007
Gönderilme Tarihi 31 Ocak 2015
Yayımlandığı Sayı Yıl 2007 Cilt: 18 Sayı: 4

Kaynak Göster

APA Çilli, M., & Arıtan, S. (2007). ÇOK BOYUTLU KİNEMATİK VERİLERİN ANALİZİNDE TEMEL BİLEŞENLER ANALİZİ YÖNTEMİNİN KULLANILMASI. Spor Bilimleri Dergisi, 18(4), 156-166.

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