TY - GEN
T1 - Gait Data Compression using Linear Prediction Modeling and data decomposition based on discrete wavelet transform
AU - Nahdliyah, Khoirun
AU - Arifin, Achmad
AU - Fatoni, Muhammad Hilman
AU - Arrofiqi, Fauzan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/17
Y1 - 2020/11/17
N2 - Gait in the human body measured and expressed in a biomedical signal stored in the patient's medical record. Gait is a time-series signal, depends on time changes and require a large-enough storage capacity. In this study, an optimization method based on data reduction used to compress gait data using linear prediction modeling. The estimation signal from the method decomposed using discrete wavelet transform (DWT). The estimation signal used to maintain the authenticity of the information in the gait signal. Linear modeling with order value from 8 to 11 generated similar signal with error value up to 1, 67 x10^{-5}. Daubechies wavelet used to decompose the signal with compression level up to 25.5259%. The results of the research show that the compressed signal has a simpler data size while maintaining the value of the original data. With a smaller capacity, the designed gait database will have more efficient storage space requirements.
AB - Gait in the human body measured and expressed in a biomedical signal stored in the patient's medical record. Gait is a time-series signal, depends on time changes and require a large-enough storage capacity. In this study, an optimization method based on data reduction used to compress gait data using linear prediction modeling. The estimation signal from the method decomposed using discrete wavelet transform (DWT). The estimation signal used to maintain the authenticity of the information in the gait signal. Linear modeling with order value from 8 to 11 generated similar signal with error value up to 1, 67 x10^{-5}. Daubechies wavelet used to decompose the signal with compression level up to 25.5259%. The results of the research show that the compressed signal has a simpler data size while maintaining the value of the original data. With a smaller capacity, the designed gait database will have more efficient storage space requirements.
KW - Discrete Wavelet Transform
KW - Gait data
KW - Linear Prediction
UR - http://www.scopus.com/inward/record.url?scp=85099657748&partnerID=8YFLogxK
U2 - 10.1109/CENIM51130.2020.9297935
DO - 10.1109/CENIM51130.2020.9297935
M3 - Conference contribution
AN - SCOPUS:85099657748
T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
SP - 82
EP - 85
BT - CENIM 2020 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Y2 - 17 November 2020 through 18 November 2020
ER -