Öz
In th is study, we introduce a simple and cost-effective method based on image analysis that can be used in the objective assessment/measurement of tremor. Tremor can be defined as the
involuntary rhythmic movement of body parts (usually hands). Types of tremor include, but not limited to essential tremor, athetose, chorea, and flapping tremor. Objective assessment of tremor is crucial in the diagnosis of many neuromuscular d iseases. Velocity and acceleration sensors utilizing semiconductor technology a re widely used for measurement of tremor. However, one major weakness of this type of sensors is that they cannot sa tisfactorily track slow or constant speed motion, because of their poor response in the low-frequency region, where some of tremors may have some significant spectral components. Therefore, accurate measurement of tremor for the diagnosis or prognosis of movement disorders is an important issue. The method that we devise consists of an in fra-red LED that is attached to the body part whose motion to be measured and a simple CCD camera that is a ttached to a personal computer. The frames acquired by th e system first saved into a movie file, and then analyzed using a software tool that we have developed. We present the use and advantages of the devised system on a sample artificial data se t.
Anahtar Kelimeler
Kaynakça
- Elbe R. J ., Koller W. C. Tremor, Baltimore: The Johns Hopkins University Press, 1990.
- Hallett M. “Classification and treatment of tremor.” JAMA, Vol. 8, No. 266, pp. 1115-1117,
- McAuley J. H., Marsden C. D. Physiologica l and pathological tremors and rhythmic central motor control Brain 123: 1545-1567, 2000.
- Jobbagy A. et al. “Analysis of movement patterns aids the early detection of Parkinson’s disease.” Proc. of th In t. Con f. IEEE/EMBS. pp. 1760 -1763, 1997.
- Padgaonkar A. J. et al. “Measurement of angular acceleration of a rig id body using linear accelerometers.” Journal of Applied Mechanics. pp. 552-557, 1975.
- Oppenheim A. V., Willsky A. S., Hamid S. Signals and Systems. Prentice-Hall, second edition , 1997.
- Janesick J. R. “Scien tific Charge-Coup led Devices.” SPIE Press Monograph Vol. PM83, Shiavi R. Introduction to Applied Statistical Signal Analysis, Second Edition. Academic Press, San Diego, Rangayyan R. M. Biomedical Signal Analysis : A Case-Study Approach, Wiley-IEEE Press, Clarkson P. M. Optimal and adaptive signa l processing, Boca Raton: CRC Press, 1993.
- Kay S. M. and Marple S. L. “Spectrum Estimation – A Modern Perspective,” Proc. IEEE, Vol. , No. 11, pp. 1419, 1981.
- Marple S. L. Digital Spectral Analysis, Englewood Cliffs, NJ , Prentice-Hall, pp. 373-378, Akaike H. “Power spectrum estimation through autoregressive model fitting,” Ann. Inst. Stat. Meth., Vol. 21, pp. 407-419, 1969.
- Akaike H. “A new look at statistical model identifica tion,” IEEE Trans. Automat. Contr., Vo l. AC-19, pp . 716-723, 1974.
- Barron A., Rissanen J., Yu B. “The minimum description leng th principle in coding and modeling,” IEEE Trans. Informa tion Theory, Vol. 44, No. 6, pp. 2743-2760, 1998.
- Rissanen J. “Universal coding, information, prediction, and estimation,” IEEE Trans. Information Theory, Vo l. , pp. 629-636, 1984.
Öz
In th is study, we introduce a simple and cost-effective method based on image analysis that can be used in the objective assessment/measurement of tremor. Tremor can be defined as the
involuntary rhythmic movement of body parts (usually hands). Types of tremor include, but not limited to essential tremor, athetose, chorea, and flapping tremor. Objective assessment of tremor is crucial in the diagnosis of many neuromuscular d iseases. Velocity and acceleration sensors utilizing semiconductor technology a re widely used for measurement of tremor. However, one major weakness of this type of sensors is that they cannot sa tisfactorily track slow or constant speed motion, because of their poor response in the low-frequency region, where some of tremors may have some significant spectral components. Therefore, accurate measurement of tremor for the diagnosis or prognosis of movement disorders is an important issue. The method that we devise consists of an in fra-red LED that is attached to the body part whose motion to be measured and a simple CCD camera that is a ttached to a personal computer. The frames acquired by th e system first saved into a movie file, and then analyzed using a software tool that we have developed. We present the use and advantages of the devised system on a sample artificial data se t.
Kaynakça
- Elbe R. J ., Koller W. C. Tremor, Baltimore: The Johns Hopkins University Press, 1990.
- Hallett M. “Classification and treatment of tremor.” JAMA, Vol. 8, No. 266, pp. 1115-1117,
- McAuley J. H., Marsden C. D. Physiologica l and pathological tremors and rhythmic central motor control Brain 123: 1545-1567, 2000.
- Jobbagy A. et al. “Analysis of movement patterns aids the early detection of Parkinson’s disease.” Proc. of th In t. Con f. IEEE/EMBS. pp. 1760 -1763, 1997.
- Padgaonkar A. J. et al. “Measurement of angular acceleration of a rig id body using linear accelerometers.” Journal of Applied Mechanics. pp. 552-557, 1975.
- Oppenheim A. V., Willsky A. S., Hamid S. Signals and Systems. Prentice-Hall, second edition , 1997.
- Janesick J. R. “Scien tific Charge-Coup led Devices.” SPIE Press Monograph Vol. PM83, Shiavi R. Introduction to Applied Statistical Signal Analysis, Second Edition. Academic Press, San Diego, Rangayyan R. M. Biomedical Signal Analysis : A Case-Study Approach, Wiley-IEEE Press, Clarkson P. M. Optimal and adaptive signa l processing, Boca Raton: CRC Press, 1993.
- Kay S. M. and Marple S. L. “Spectrum Estimation – A Modern Perspective,” Proc. IEEE, Vol. , No. 11, pp. 1419, 1981.
- Marple S. L. Digital Spectral Analysis, Englewood Cliffs, NJ , Prentice-Hall, pp. 373-378, Akaike H. “Power spectrum estimation through autoregressive model fitting,” Ann. Inst. Stat. Meth., Vol. 21, pp. 407-419, 1969.
- Akaike H. “A new look at statistical model identifica tion,” IEEE Trans. Automat. Contr., Vo l. AC-19, pp . 716-723, 1974.
- Barron A., Rissanen J., Yu B. “The minimum description leng th principle in coding and modeling,” IEEE Trans. Informa tion Theory, Vol. 44, No. 6, pp. 2743-2760, 1998.
- Rissanen J. “Universal coding, information, prediction, and estimation,” IEEE Trans. Information Theory, Vo l. , pp. 629-636, 1984.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Haziran 2007 |
| Yayımlandığı Sayı | Yıl 2007 Cilt: 2 Sayı: 7 |
