Embedded Files
2020
2020
Surface Electromyography (EMG) Signal Processing, Classification, and Practical Considerations
Angkoon Phinyomark, Evan Campbell, Erik Scheme
Biomedical Signal Processing: Advances in Theory, Algorithms and Applications (ISBN 978-981-13-9096-8 )
Chapter 1, pp. 3-29, 2020, Springer, doi no: 10.1007/978-981-13-9097-5_1
2018
2018
Topological Data Analysis of Biomedical Big Data
Angkoon Phinyomark, Esther Ibañez–marcelo, Giovanni Petri
Signal Processing and Machine Learning for Biomedical Big Data (ISBN 978-149-877-346-1)
Chapter 11, pp. 209-233, July 2018, CRC Press, doi no: 10.1201/9781351061223-11.
2014
2014
The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface
Angkoon Phinyomark, Franck Quaine, Yann Laurillau
Applications, Challenges, and Advancements in Electromyography Signal Processing (ISBN 978-146-666-090-8)
Chapter 15, pp. 321-353, May 2014, IGI Grobal, doi no: 10.4018/978-1-4666-6090-8.ch015.
2012
2012
The Usefulness of Mean and Median Frequencies in Electromyography Analysis
Angkoon Phinyomark, Sirinee Thongpanja, Huosheng Hu, Pornchai Phukpattaranont, Chusak Limsakul
Computational Intelligence in Electromyography Analysis: A Perspective on Current Applications and Future Challenges (ISBN 980-953-307-474-5)
Chapter 8, pp. 195-220, October 2012, Intech, doi no: 10.5772/50639.
Full Paper | Chapter Proposal (Most Downloaded Chapters)
The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal
Angkoon Phinyomark, Pornchai Phukpattaranont, Chusak Limsakul
EMG Methods for Evaluating Muscle and Nerve Function (ISBN 978-953-307-793-2)
Chapter 7, pp. 107-132, January 2012, Intech, doi no: 10.5772/25757.
Full Paper | Chapter Proposal (Most Downloaded Chapters)
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