참고문헌 (16)

1. Luchetti, Martina, Cutti, Andrea G., Verni, Gennaro, Sacchetti, Rinaldo, Rossi, Nicolino. Impact of Michelangelo prosthetic hand: Findings from a crossover longitudinal study. Journal of rehabilitation research and development, vol.52, no.5, 605-618.

   상세보기

2. Ziegler-Graham, Kathryn, MacKenzie, Ellen J., Ephraim, Patti L., Travison, Thomas G., Brookmeyer, Ron. Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050. Archives of physical medicine and rehabilitation, vol.89, no.3, 422-429.

   상세보기

3. McFarland, Lynne V, Hubbard Winkler, Sandra L, Heinemann, Allen W, Jones, Melissa, Esquenazi, Alberto. Unilateral upper-limb loss: Satisfaction and prosthetic-device use in veterans and servicemembers from Vietnam and OIF/OEF conflicts.. Journal of rehabilitation research and development, vol.47, no.4, 299-316.

   상세보기

4. Luppescu, G., Lowney, M., and Shah, R., “Classification of Hand Gestures using Surface Electromyography Signals for Upper-Limb Amputees,” Stanford University Technical Report, http://cs229.stanford.edu/proj2016spr/report/040.pdf (Accessed 8 JUN 2018) 

5. Clancy, Edward A., Bida, Oljeta, Rancourt, Denis. Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force-varying contractions. Journal of biomechanics, vol.39, no.14, 2690-2698.

   상세보기

6. Micera, S., Vannozzi, G., Sabatini, A.M., Dario, P.. Improving detection of muscle activation intervals. IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society, vol.20, no.6, 38-46.

   상세보기

7. Bonato, P., D'Alessio, T., Knaflitz, M.. A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE transactions on bio-medical engineering, vol.45, no.3, 287-299.

   상세보기

8. Andrews, A., “Finger Movement Classification Using Forearm EMG Signals,” http://qspace.library.queensu.ca/bitstream/handle/1974/1574/Andrews_Alexander_J_200810_MScE.pdf (Accessed 8 JUN 2018) 

9. Keating, J., “Relating Forearm Muscle Electrical Activity to Finger Forces,” Worcester Polytechnic Institute, 2014. 

10. Englehart, K., Hudgin, B., Parker, P.A.. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE transactions on bio-medical engineering, vol.48, no.3, 302-311.

    상세보기

11. Phinyomark, A., Hirunviriya, S., Limsakul, C., and Phukpattaranont, P., “Evaluation of EMG Feature Extraction for Hand Movement Recognition Based on Euclidean Distance and Standard Deviation”, Proc. of International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 856-860, 2010. 

12. 10.1017/CBO9780511801389 

    

13. 10.7551/mitpress/4175.001.0001 Schölkopf, B. and Smola, A. J., “Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond,” MIT Press, 2002. 

    

14. Phinyomark, Angkoon, Phukpattaranont, Pornchai, Limsakul, Chusak. Feature reduction and selection for EMG signal classification. Expert systems with applications, vol.39, no.8, 7420-7431.

    상세보기

15. Marshall, Matthew M., Armstrong, Thomas J.. Observational Assessment of Forceful Exertion and the Perceived Force Demands of Daily Activities. Journal of occupational rehabilitation, vol.14, no.4, 281-294.

    상세보기

16. Coppard, Vanessa L., Tresilian, James R., Mon-Williams, Mark, Carson, Richard G.. The effect of obstacle position on reach-to-grasp movements. Experimental brain research, vol.137, no.3, 497-501.

    상세보기

저자의 다른 논문 :

김재효 (5)