A comparison of tonal signal extraction methods

1998 ◽  
Vol 104 (3) ◽  
pp. 1781-1782
Author(s):  
Michael G. Jones
Keyword(s):  
Extraction Methods ◽  
Signal Extraction ◽  
Tonal Signal

Progress on the Fabrication of Smart Textiles Based on Soft Strain Sensors

2019 ◽  
Vol 6 (6) ◽  
pp. 1-12
Author(s):  
Rafiu King Raji ◽  
Xuhong Miao ◽  
Ailan Wan ◽  
Zhejiang ◽  
Shu Zhang ◽  
...  
Keyword(s):  
Experimental Testing ◽  
Extraction Methods ◽  
Strain Sensors ◽  
Sensor Nodes ◽  
Signal Extraction ◽  
Strain Sensing ◽  
Sensitive Material ◽  
Smart Textiles ◽  
Fabric Structure ◽  
Fabric Deformation

The focus of this study is on strain sensing research and applications in smart textiles. Strain sensing is the measurement of fabric deformation by embedding a strain-sensitive material in it and subjecting it to stress. This paper presents an extensive classification of knitted textile strain sensors. Salient knitted strain sensor production parameters, such as conductive yarn choice, fabric structure, fabric structure deformation, and its relationship to strain signal extraction are discussed. The study concludes that producing yarn-based soft strain sensors for smart textile applications is viable. However, sensitive yarns with the right conductivity, count, and structural configuration are often unavailable. Work remains in the areas of efficient fabric deformation, signal extraction methods, development of sensor nodes, and robust experimental testing systems.

Characterization of surfactant complex mixtures using Raman spectroscopy and signal extraction methods: Application to laundry detergent deformulation

2016 ◽  
Vol 915 ◽  
pp. 36-48 ◽  
Author(s):  
Alexandra Gaubert ◽  
Yohann Clement ◽  
Anne Bonhomme ◽  
Benjamin Burger ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Laundry Detergent ◽  
Extraction Methods ◽  
Complex Mixtures ◽  
Signal Extraction ◽  
Surfactant Complex

scholarly journals Continuous Distant Measurement of the User’s Heart Rate in Human-Computer Interaction Applications

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4205 ◽  
Author(s):  
Przybyło
Keyword(s):  
Heart Rate ◽  
Human Computer Interaction ◽  
Pulse Signal ◽  
Estimation Algorithm ◽  
Time Domain Analysis ◽  
Extraction Methods ◽  
Signal Extraction ◽  
Power Spectral ◽  
The Impact ◽  
Computer Interaction

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e. a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user's heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e. reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

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