2017
DOI: 10.1007/978-3-319-53480-0_52
|View full text |Cite
|
Sign up to set email alerts
|

A New Approach to Human Activity Recognition Using Machine Learning Techniques

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
15
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 23 publications
(15 citation statements)
references
References 25 publications
0
15
0
Order By: Relevance
“…1) Time domain features: e.g. the coefficients of an autoregressive (AR) model for each of the x, y, and z axes [11,18,[26][27][28][29], signal magnitude area (SMA) [11,18,[26][27][28]30], tilt angle [11,31], Histogram [17], mean [17,26,31], standard deviation [25,26], Jerk [32,33], roll angle [11,24] skewness, kurtosis and total integral of modulus of accelerations (IMA) [12], and.…”
Section: Related Workmentioning
confidence: 99%
See 4 more Smart Citations
“…1) Time domain features: e.g. the coefficients of an autoregressive (AR) model for each of the x, y, and z axes [11,18,[26][27][28][29], signal magnitude area (SMA) [11,18,[26][27][28]30], tilt angle [11,31], Histogram [17], mean [17,26,31], standard deviation [25,26], Jerk [32,33], roll angle [11,24] skewness, kurtosis and total integral of modulus of accelerations (IMA) [12], and.…”
Section: Related Workmentioning
confidence: 99%
“…2) Frequency domain features: e.g. power spectral density (PSD) [12,25], signal entropy and spectral energy [12,31], largest frequency component, average frequency signal skewness, and frequency signal kurtosis [26].…”
Section: Related Workmentioning
confidence: 99%
See 3 more Smart Citations