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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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  4. 10.5370/KIEE.2023.72.11.1427
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References

1 
S. Katz, A. B. Ford, R. W. Moskowitz, B. A. Jackson, M. W. Jaffe, 1963, Studies of Illness in the Aged. The Index of ADL: A Standardized Measure of Biological and Psychosocial Function, J. Amer. Med. Assoc., Vol. 185, pp. 914-919DOI
2 
S. Katz, 1983, Assessing Self-maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living, J. Amer. Geriatr. Soc., Vol. 31, No. 12, pp. 721-727DOI
3 
S. Patel, H. Park, P. Bonato, L. Chan, M. Rodgers, 2012, A Review of Wearable Sensors and Systems with Application in Rehabilitation, J. Neuroeng. Rehabil., Vol. 9, No. 1, pp. 1-17DOI
4 
R. Messing, C. Pal, H. Kautz, 2009, Activity Recognition Using the Velocity Histories of Tracked Keypoints, Proc. ICCVDOI
5 
M. Yang, H. Zheng, 2012, Inertial Sensor-based Methods in Walking Speed Estimation: A Systematic ReviewDOI
6 
B. Fida, I. Bernabucci, D. Bibbo, S. Conforto, M. Schmid, 2015, Varying Behavior of Different Window Sizes on the Classification of Static and Dynamic Physical Activities from a Single Accelerometer, Medical Engineering & Physics, Vol. 37, No. 7, pp. 705-711DOI
7 
M. R. Carlos, L. C. González, J. Wahlström, G. Ramírez, F. Martínez, G. Runger, 2019, How Smartphone Accelerometers Reveal Aggressive Driving Behavior?—The Key Is the Representation, IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 8, pp. 3377-3387DOI
8 
B. Bruno, F. Mastrogiovanni, A. Sgorbissa, 2014, Dataset for ADL Recognition with Wrist-worn Accelerometer, UCI Machine Learning RepositoryGoogle Search
9 
M. Fahim, I. Fatima, S. Y. Lee, Y. T. Park, 2013, EFM: E-volutionary Fuzzy Model for Dynamic Activities Recognition using a Smartphone Accelerometer, Int. J. Applied Intelligence, Vol. 39, pp. 475-488DOI
10 
A. V. Oppenheim, A. S. Willsky, S. H. Nawab, 1997, Signals and Systems, 2nd ed., Prentice-HallDOI
11 
L. Marple, 1999, Computing the Discrete-time 'Analytic' Signal via FFT, vol. 47, no. 9, pp. 2600-2603DOI
12 
J. S. Bendat, A. G. Piersol, 2010, Random Data: Analysis and Measurement Procedures, vol. 729, John Wiley & SonsDOI
13 
J. G. Proakis, D. G. Manolakis, 1996, Digital Signal Processing: Principles, Algorithms, and Applications, Prentice-HallDOI
14 
J. L. Rodgers, W. A. Nicewander, 1988, Thirteen Ways to Look at the Correlation Coefficient, The Amer. Statistician, Vol. 42, No. 1, pp. 59-66DOI
15 
R. K. Blashfield, 1980, The Growth of Cluster Analysis: Tryon, Ward, and Johnson, Multivariate Behavioral Research, Vol. 15, No. 4, pp. 439-458DOI
16 
L. Kaufman, P. J. Rousseeuw, 2009, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & SonsDOI