Monitoring hand hygiene with commercial gas sensors: A pattern recognition approach

2022 ◽  
Vol 352 ◽  
pp. 131027
Author(s):  
S. Braun ◽  
A. Kobald ◽  
A. Oprea ◽  
I. Boehme ◽  
P. Bonanati ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Hand Hygiene ◽  
Gas Sensors ◽  
Pattern Recognition Approach

Pattern Recognition Approach to Fault Diagnosis in the DAMADICS Benchmark Flow Control Valve

2003 ◽  
Vol 36 (5) ◽  
pp. 861-866 ◽  
Author(s):  
A. Marciniak ◽  
C.D. Bocăială ◽  
R. Louro ◽  
J. Sa da Costa ◽  
J. Korbicz
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Flow Control ◽  
Control Valve ◽  
Flow Control Valve ◽  
Pattern Recognition Approach

scholarly journals Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Aixiang He ◽  
Jun Yu ◽  
Guangfen Wei ◽  
Yi Chen ◽  
Hao Wu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Decision Tree ◽  
Gas Sensors ◽  
Operating Temperature ◽  
Ambient Air ◽  
Sensor Response ◽  
Short Time Fourier Transform ◽  
Short Time ◽  
Different Gases

Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.

Sign in / Sign up

Export Citation Format

Share Document