A systematic mapping study of robotics in human care

Santos, Nicolás Bajo; Bavaresco, Rodrigo Simon; Tavares, João; Ramos, Grasieli de Oliveira; Barbosa, Jorge Luís Victória

doi:10.1016/j.robot.2021.103833

Cited by 26 publications

(21 citation statements)

References 63 publications

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“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9, 11, 16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…In this way, IoT can improve the effectiveness of treatments, prevent risk situations and assist in health promotion. Twelve studies approached the context of remote monitoring (IDs = 5, 9, 10, 17,27,36,38,40,43,44,51,55). In addition, IoT increases resource management efficiency 9 https://physionet.org/ through flexibility and mobility using intelligent solutions.…”

Section: Resultsmentioning

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

Self Cite

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9,11,16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

Section: Gq1-what Iot Features Are Being Used To Capture Human Chest Sound Signals?mentioning

confidence: 99%

“…In this way, IoT can improve the effectiveness of treatments, prevent risk situations and assist in health promotion. Twelve studies approached the context of remote monitoring (IDs = 5, 9, 10, 17,27,36,38,40,43,44,51,55). In addition, IoT increases resource management efficiency through flexibility and mobility using intelligent solutions.…”

Section: Gq2-what Are the Benefits For The Patient By Using Iot For Cardiac Care?mentioning

confidence: 99%

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

et al. 2021

Self Cite

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.

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Soft Upper‐Limb Wearable Robotic Devices: Technology and Applications

Sharma,

Phan,

Davies

et al. 2024

Advanced Intelligent Systems

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One of the practical applications in the field of soft robotics involves the development of soft robotic wearable devices. These devices make use of their intrinsically compliant structures to interact safely and harmoniously with the human body. While soft wearable robots demonstrate their utility in lower‐limb applications for locomotion, the upper‐limb domain offers significant prospects in a wide range of applications that soft robotic technology can address. In this review, the current state of technology in the field of soft wearable upper limbs is systematically analyzed and categorized. Categorizations are made based on their applications in rehabilitation, activities of daily living support, and human augmentation. Furthermore, in this study, also contemporary technological aspects, encompassing sensing technology and control systems, are explored. Despite exciting potential in this domain, several limitations from existing devices inherently impede widespread adoption and thus hinder further progress in the field. In this study, also an overview of the different facets of the domain is provided and key considerations for the advancement of soft wearable robotic devices intended for upper‐limb applications are prescribed.

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A systematic mapping study of robotics in human care

Cited by 26 publications

References 63 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

Soft Upper‐Limb Wearable Robotic Devices: Technology and Applications

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