SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition

2015 ◽  
Author(s):  
Isao Hayashi ◽  
Yinlai Jiang ◽  
Shuoyu Wang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Motion Data

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

Time series forecasting based on wavelet decomposition and feature extraction

2016 ◽  
Vol 28 (S1) ◽  
pp. 183-195 ◽  
Author(s):  
Tianhong Liu ◽  
Haikun Wei ◽  
Chi Zhang ◽  
Kanjian Zhang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Time Series Forecasting

Multi-Dimensional Encoding Image Based Time Series Feature Extraction for Improved Colorimetric Gas Concentration Estimation

2021 ◽  
Vol MA2021-02 (57) ◽  
pp. 1939-1939
Author(s):  
Changhyun KIM ◽  
Junyeop Lee ◽  
Junkyu Park ◽  
Daewoong Jung ◽  
Chang-Woo Nam ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Gas Concentration ◽  
Concentration Estimation

Seismic Response of Embankment Dams Based on Recorded Strong-Motion Data in Japan

2019 ◽  
Vol 35 (2) ◽  
pp. 955-976 ◽  
Author(s):  
DongSoon Park ◽  
Tadahiro Kishida
Keyword(s):  
Time Series ◽  
Seismic Response ◽  
Prediction Models ◽  
Strong Motion ◽  
Dynamic Responses ◽  
Design Stage ◽  
Ground Acceleration ◽  
Motion Data ◽  
Embankment Dams ◽  
Strong Motion Database

It is important to investigate strong-motion time series recorded at dams to understand their complex seismic responses. This paper develops a strong-motion database recorded at existing embankment dams and analyzes correlations between dam dynamic responses and ground-motion parameters. The Japan Commission on Large Dams database used here includes 190 recordings at the crests and foundations of 60 dams during 54 earthquakes from 1978 to 2012. Seismic amplifications and fundamental periods from recorded time series were computed and examined by correlation with shaking intensities and dam geometries. The peak ground acceleration (PGA) at the dam crest increases as the PGA at the foundation bedrock increases, but their ratio gradually decreases. The fundamental period broadly increases with the dam height and PGA at the foundation bedrock. The nonlinear dam response becomes more apparent as the PGA at the foundation bedrock becomes >0.2 g. The prediction models of these correlations are proposed for estimating the seismic response of embankment dams, which can inform the preliminary design stage.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

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