Internet of Things and occupational well-being in industry 4.0: A systematic mapping study and taxonomy

Bavaresco, Rodrigo Simon; Arruda, Helder; Rocha, Eduarda; Barbosa, Jorge Luís Victória; Li, G.P.

doi:10.1016/j.cie.2021.107670

Cited by 34 publications

(18 citation statements)

References 55 publications

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“…The main tool used to pick up signals was the digital stethoscope, mentioning in 44 articles (IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,44,45,46,48,49,50,51,52,53,54,56). The connectivity of digital stethoscopes occurs, in most equipment, by Bluetooth, not over the Internet, and for this reason these equipments are not considered IoT devices.…”

Section: Resultsmentioning

confidence: 99%

“…Among the 58 articles selected, 34 works (58.62%,IDs = 1, 2, 5, 6, 10, 19,20,21,22,25,31,32,33,34,35,36,37,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,58) mention care using Machine Learning. Leng et al [30] (36) showed the possible interactions between the electronic stethoscope, the sensor-captured signal decomposition algorithm, machine learning techniques and cardiac sound segmentation.…”

Section: Fq1 -Is There Mention Of Care Using Machine Learning?mentioning

confidence: 99%

“…Communication is performed through low-power wireless technology (Bluetooth) between the stethoscope and a personal computer. Among the selected articles, 16 works mention the use of Bluetooth (IDs = 1, 3, 4, 6, 9, 14, 21,22,31,35,36,38,41,44,46,51).…”

Section: Fq1 -Is There Mention Of Care Using Machine Learning?mentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites¹,

Silva²,

Barbosa³

et al. 2021

Preprint

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This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

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Section: Resultsmentioning

confidence: 99%

Section: Fq1 -Is There Mention Of Care Using Machine Learning?mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites¹,

Silva²,

Barbosa³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Among the 58 articles selected, 45 works (77.59%, IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,41,44,45,46,48,49,50,51,52,53,54,56) cite heart rate observation methods. There are two widely used methods for observing the heartbeat.…”

Section: Gq3-what Methods Are Currently Being Used For Heart Rate Observation?mentioning

confidence: 99%

“…Among the 58 articles selected, 34 works (58.62%, IDs = 1, 2, 5, 6, 10, 19,20,21,22,25,31,32,33,34,35,36,37,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,58) mention care using Machine Learning. Leng et al [30] (36) showed the possible interactions between the electronic stethoscope, the sensor-captured signal decomposition algorithm, machine learning techniques and cardiac sound segmentation.…”

Section: Fq1-is There Mention Of Care Using Machine Learning?mentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

et al. 2021

View full text Add to dashboard Cite

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

show abstract

Machine Learning and IoT Applied to Cardiovascular Diseases Identification Through Heart Sounds: A Literature Review

Brites

Silva

Barbosa

et al. 2022

Lecture Notes in Networks and Systems

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show abstract

Internet of Things and occupational well-being in industry 4.0: A systematic mapping study and taxonomy

Cited by 34 publications

References 55 publications

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification Through Heart Sounds: A Literature Review

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