2019 IEEE International Conference on Healthcare Informatics (ICHI) 2019
DOI: 10.1109/ichi.2019.8904563
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Cited by 47 publications
(17 citation statements)
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“…have investigated cough and snore detection in the presence of ambient room noise using smartphones. The k-NN classifier resulted in the highest accuracy (97%) when performing the binary classification between cough and snore events [32]. Voluntary cough detection against speech in a population of volunteers has also been investigated [33].…”
Section: Cough Detectionmentioning
confidence: 99%
“…have investigated cough and snore detection in the presence of ambient room noise using smartphones. The k-NN classifier resulted in the highest accuracy (97%) when performing the binary classification between cough and snore events [32]. Voluntary cough detection against speech in a population of volunteers has also been investigated [33].…”
Section: Cough Detectionmentioning
confidence: 99%
“…For instance, videos and images obtained from smartphone camera or data screened through installed inertial sensors were studied for detecting human fatigue levels (Karvekar 2019 ; Roldán Jiménez et al 2019 ). Likewise, condition of cough can be detected from the audio recorded through smartphone microphone (Nemati et al 2019 ; Vhaduri et al 2019 ). Even, the video obtained through smartphone can assist in the prediction of nausea (Story et al 2019 ).…”
Section: Artificial Intelligencementioning
confidence: 99%
“…For example, Hristo et al [ 41 ] develop a mobile device fingerprinting system based on the k -NN algorithm. Additionally, Sudip et al [ 76 ] develop a smartphone-based health monitoring system using the k -NN model. A smartphone-based data mining system is presented in [ 60 ], for fall detection using a k -NN algorithm.…”
Section: Embedded Machine Learning Techniquesmentioning
confidence: 99%