CoChat
Sign Up
Cited by 121Non-Invasive Vital Sign MonitoringECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Open access
Moshawrab, Mohammad, Adda, Mehdi, Bouzouane, Abdenour, Ibrahim, Hussein, Raad, Ali·2023·Sensors
DOI 10.3390/s23020828PMC PMC9865666
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Verified 2026-10-09 · 1 reference not checked yet

AI snapshot

Written from the paper’s full text.

TL;DR

This PRISMA-guided systematic review synthesizes 87 studies (2010–2022) on smart wearables for detecting and predicting cardiovascular diseases, reporting high feasibility and accuracy but major gaps in explainability, privacy, and multimodal data use. Interest has grown sharply since 2019, yet the authors conclude further research is needed before broad clinical adoption.

Key findings

  • The search identified 4002 records; after removing 1021 duplicates, 2981 remained, 599 were full-text screened, and 87 studies were included. 78% of included publications appeared in 2019 or later: 9 in 2019, 18 in 2020, 21 in 2021, and 21 in 2022, versus 19 in 2010–2018.
  • ECG was used in 69 of the systems; atrial fibrillation was the most studied disease, addressed in 39 of 87 studies. The PhysioNet MIT-BIH dataset was the most popular, used by 36 of 87 studies, while 25 studies used researchers’ private data.
  • 55 studies built custom devices; others used commercially available wearables or did not specify the device. One unspecified WBAN study achieved 96% accuracy, and the discussion notes several implementations reported accuracy over 99%.
  • None of the reviewed studies implemented explainable AI, federated learning, or multimodal machine learning; the most commonly used models were convolutional neural networks, support vector machines, long short-term memories, and decision trees.

Methods

  • Systematic literature review following PRISMA guidelines, searching IEEE, PubMed, and Scopus for peer-reviewed journal and conference papers in English, published between 2010 and 2022, addressing AI, smart wearables, healthcare, and cardiovascular diseases.
  • Three database-specific queries combined four concept blocks: AI, health, wearables, and CVDs; records were screened first by title/abstract, then by full text, leaving 87 eligible studies.
  • Data extracted from each study included publication year, diseases studied, vital signs recorded, wearable hardware, embedded intelligent models, datasets used, and performance metrics; results were analyzed descriptively by device type, year, vital signs, diseases, models, and datasets.

Limitations

  • Stated: The authors conclude that further research is needed to improve smart wearables for CVD management, citing challenges in data privacy and confidentiality, noise and artifacts, data diversity and heterogeneity, and user technology adoption and engagement.
  • Stated: None of the 87 reviewed studies used explainable AI, federated learning, or multimodal machine learning, which the authors argue limits trust, privacy protection, and the ability to analyze heterogeneous data.
  • Evident in the text: The search was performed in October 2021, yet the review claims coverage through 2022 and includes 2022 publications; the Scopus query also limits publication years to 2010–2021, creating an inconsistency. No formal risk-of-bias or study-quality appraisal is described.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/s23020828

Cites

160 of 161 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 24 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    55 studies built their own devices using various vital sign sensors, power resources, storage resources, communications, and other technical components.

    “Throughout the analysis of the eligible documents, it was shown that 55 studies built their own devices using various vital sign sensors, power resources, storage resources, communications, and other technical components.”
    Quote in the paperNot a substantial claim
  • Findingown data

    87 documents were deemed suitable for the review.

    “87 papers were deemed relevant for further review.”
    • Also stated in: § 3.1. Study Selection · plain
      “87 documents were deemed suitable for this review.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Among custom-built devices, some studies proposed wearables with unspecified components.

    “Thus, in [39,41,42,43,46,48,50,51,54,56,58,59,60,61,62,66,86,88,92,116,118,123], the authors proposed custom-built wearables with unspecified components.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Custom-built wearables with unspecified components were able to detect various cardiovascular diseases such as atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, cardiac asystole, cardiovascular risk, fusion beats, heart attack, heart failure, hypertension, myocardial infarction, myocardial ischemia, premature atrial contractions, and premature ventricular

    “These studies were able to detect various cardiovascular diseases such as atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, cardiac asystole, cardiovascular risk, fusion beats, heart attack, heart failure, hypertension, myocardial infarction, myocardial ischemia, premature atrial contractions, and premature ventricular contractions.”
    Quote in the paperNot a substantial claim
  • Findingcitation

    Several studies used commercially available sensors to develop their wearable devices.

    “On the other hand, several studies used commercially available sensors to develop their wearable devices.”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 36
  • May combine two claims. 25

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    Commercially available devices were categorized into three types: wristband devices, belts, and others.

    “Depending on the type of device used, three categories can be distinguished, namely, wristband devices, belts, and others.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The initial search identified 4002 documents from the three libraries.

    “Initially, 4002 documents were identified from the three libraries based on the above searches.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    That study achieved 96% accuracy in the classification algorithm, demonstrating high feasibility in detecting CVDs.

    “Their study achieved 96% accuracy in the classification algorithm, demonstrating high feasibility in detecting CVDs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    Smartwatches and smart wristbands were used in some studies to record vital signs.

    “For example, in [45,47,55,68,70,80,85,98,103,107,108,112], the authors used smartwatches and smart wristbands to record vital signs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    2382 documents were excluded on the basis of irrelevance, leaving 599 for the next stage.

    “Then, the aforementioned selection procedure was applied, excluding 2382 documents on the basis of irrelevance and advancing 599 to the next stage.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The search yielded 1013 documents on IEEE, 1020 on PubMed, and 1969 on Scopus.

    “The search on IEEE yielded 1013 documents, on PubMed 1020, and on Scopus 1969”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    Various wearable ECG devices such as smart vests and patches were used in some studies.

    “In addition, the authors in [40,44,49,63,64,76,78,83,91,93,96,102,110,120,121] used various wearable ECG devices such as smart vests and patches.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    512 documents were excluded during full-text screening.

    “In this phase, 512 documents were excluded for various reasons”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    The AD8232 ECG sensor was used in many studies to collect vital signs data.

    “In addition, the authors in [52,57,65,67,69,71,72,74,75,77,79,81,89,94,100,101,113,122] used the AD8232 ECG sensor to collect vital signs data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    Smart belts were used in some studies to collect ECG data.

    “In addition, the authors in [90,99,105,106] used smart belts to collect ECG data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In a single study, the device used was not specified.

    “Finally, in a single study, the device used was not specified.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Vital signs obtained by custom-built wearables with unspecified components included radial artery audio signal, blood oxygen level, blood pressure, blood sugar level, body temperature, diastolic pressure, electrocardiogram, electroencephalogram, heart rate, motion data, oxygen saturation level, photoplethysmogram, respiratory rate, and skin temperature.

    “Vital signs obtained for this purpose were radial artery audio signal, blood oxygen level, blood pressure, blood sugar level, body temperature, diastolic pressure, electrocardiogram, electroencephalogram, heart rate, motion data, oxygen saturation level, photoplethysmogram, respiratory rate, and skin temperature.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    The studies can be divided into three categories according to whether the measuring devices used are commercially available or not: custom-built, commercially available, and unspecified.

    “Contextually, the studies can be divided into three categories, for example, according to whether the measuring devices used are commercially available or not. Systems in the first group use components that are not commonly available; these components were custom-made by the researchers for the study. Systems that use readily available technology and commercially available devices comprise the”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The other group of studies used commercially available devices capable of recording various vital signs such as activity parameters, blood oxygen level, blood pressure, blood glucose level, cholesterol level, electrocardiogram, electroencephalogram, electromyogram, heart rate, oxygen saturation level, photoplethysmogram, pulse plethysmogram, and respiratory rate.

    “The other group of studies consists of studies that used commercially available devices. These devices were capable of recording various vital signs such as activity parameters, blood oxygen level, blood pressure, blood glucose level, cholesterol level, electrocardiogram, electroencephalogram, electromyogram, heart rate, oxygen saturation level, photoplethysmogram, pulse plethysmogram, and”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    After excluding 1021 duplicate entries, 2981 documents remained.

    “after which duplicate entries were excluded, removing 1021 and leaving 2981 documents.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    ECG, BP, HR, and temperature were all reliable predictors of cardiovascular disease.

    “ECG, BP, HR, and temperature were all reliable predictors of cardiovascular disease.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Atrial fibrillation (AFib) is the most commonly studied disease, with 39 of 87 studies addressing it.

    “Atrial fibrillation (AFib) is the most commonly studied disease, with 39 of 87 studies addressing it.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Findingnone given

    Smart watches such as the Samsung Active and Apple Watch have significant efficiency in capturing ECG signals, complementing the accuracy of ECGs performed in a doctor's office, clinic, or hospital room.

    “Studies have shown that smart watches such as the Samsung Active and Apple Watch have significant efficiency in capturing ECG signals, complementing the accuracy of ECGs performed in a doctor’s office, clinic, or hospital room.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The number of publications per year was nine in 2019, eighteen in 2020, twenty-one in 2021, and twenty-one in 2022.

    “There are nine in 2019, eighteen in 2020, twenty-one in 2021, and twenty-one in 2022.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    A total of 68 studies were published in 2019-2022, compared to only 19 in 2010-2018.

    “During those years, a total of 68 studies were published (compared to only 19 in 2010–2018).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    Other conditions studied include premature ventricular contractions (PVCs), ventricular ectopic beats, bradycardia, paced beat (PACE), and many others.

    “Other conditions are also being studied, including premature ventricular contractions (PVCs), ventricular ectopic beats, bradycardia, paced beat (PACE), and many others.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The search strategy used three queries combining four focus areas: AI, health, wearables, and CVDs.

    “In order to conduct a thorough search of the above materials, three queries were formulated. While these queries all follow the same logical structure, they use different syntaxes to comply with the different rules imposed by each data source. Targeted articles are found at the intersection of four query blocks, each defining a different topic of interest. AI, health, wearables, and CVDs (or”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The selection process involved three steps: title/abstract review, full-text review, and data extraction.

    “The data extracted from the records were selected in three steps to determine which files were relevant to this analysis. The first step was to review the titles and abstracts of all documents to determine if they were relevant to the topic of this study. The documents that passed this step were then downloaded. In a second step, we reviewed the downloaded files to quickly verify their content and”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The review searched IEEE, PubMed, and Scopus.

    “This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022.”
    • Also stated in: § 2.2. Information Sources · plain
      “Several academic abstract and citation databases for peer-reviewed literature were used, including IEEE, PubMed, and Scopus Elsevier, to ensure superior results and to cover the largest number of documents possible.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Eligible papers had to address AI or related fields, present a smart wearable solution, address healthcare, focus on cardiovascular diseases, be published in a peer-reviewed journal or conference proceedings, be published between January 2010 and October 2022, and be in English.

    “To be considered reputable, a paper must address artificial intelligence or related fields, present a smart wearable solution, address healthcare, and focus on cardiovascular diseases. In addition, the material should have been published in a peer-reviewed journal or as a conference proceedings. In addition, only documents published between January 2010 and October 2022 were considered. As a final”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    The systematic review was conducted following PRISMA guidelines and the PRISMA 2020 checklist.

    “This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022.”
    • Also stated in: § 2.1. Eligibility Criteria · plain
      “In conducting this review, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses ) [37] was used as a guide for preparing a systematic literature review. The structure of this review was based on the latest PRISMA checklist (PRISMA Checklist 2020) [38].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [37] Page, 2021, PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews, BMJ source

      Not checked in the free run.

    • cites [38] (2022, November 17). PRISMA. Available online: https://www.prisma-statement.org//PRISMAStatement/Checklist.aspx.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Combining multiple vital signs in the analysis process could allow for more accurate prediction of cardiovascular disease.

    “Therefore, combining multiple vital signs in the analysis process could allow for more accurate prediction of cardiovascular disease.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Research in medicine has shown that the use of multiple vital signs may be more helpful in detecting a disease than the use of a single vital sign.

  • May combine two claims.

    Interpretationown data

    The number of publications addressing smart wearables for CVD management has jumped, reflecting growing interest and widespread acceptance of such devices.

    “The number of publications addressing the use of smart wearables for cardiovascular disease management has jumped, reflecting both the growing interest in this area and the widespread acceptance of such devices.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Publication counts reflect growing interest and widespread acceptance of smart wearables for CVD.

  • May combine two claims.

    Interpretationown data

    Smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease.

    “Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    The review showed high practicality and effectiveness of smart wearables for treating fatal heart diseases.

    “The review of the research area showed the high practicality and effectiveness of such methods, reflecting the growing interest that has surged in recent years.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Smart wearables have proven to be viable in cardiovascular disease detection, providing both continuous and real-time monitoring without interfering with daily life routines.

    “Consequently, smart wearables have proven to be viable in this area, providing both continuous and real-time monitoring without interfering with daily life routines.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Smart watches and wristbands are quite effective at capturing ECG signals.

    “Smart watches and wristbands, on the other hand, are quite effective at capturing ECG signals and are also convenient for a number of other reasons.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AFib is the leading cause of death and morbidity due to stroke, heart failure, thromboembolism, and reduced quality of life, and accounts for the majority of these cases.

    “AFib is the leading cause of death and morbidity due to stroke, heart failure, thromboembolism, and reduced quality of life, and accounts for the majority of these cases [127].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [127] Lip, 2007, Management of atrial fibrillation, Lancet source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Federated learning is still in its infancy and is still vulnerable to various challenges.

    “FL is still in its infancy and is still vulnerable to various challenges [148,149].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [148] Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., and Seth, K. (November, January 30). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA. source

      Not checked in the free run.

    • cites [149] Zhang, 2022, Challenges and future directions of secure federated learning: A survey, Front. Comput. Sci. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Cardiovascular disease detection through smart wearables is now a reality.

    “Cardiovascular disease detection through smart wearables is now a reality.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    ECG is considered the most effective indicator of cardiovascular disease due to its high accuracy in recording the presence of such disease and its practicality and reliability in detecting it.

    “the electrocardiogram (ECG) is considered the most effective indicator of cardiovascular disease due to its high accuracy in recording the presence of such disease and its practicality and reliability in detecting it.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Premisecitation

    Machine learning and deep learning models are attracting much attention and are proving to be very practical in the healthcare industry.

    “The intelligent models of machine learning and deep learning are attracting much attention and are proving to be very practical [130,131] in the healthcare industry.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [130] Miotto, 2018, Deep learning for healthcare: Review, opportunities and challenges, Brief. Bioinform. source

      Not checked in the free run.

    • cites [131] Bhatt, 2021, The state of the art of deep learning models in medical science and their challenges, Multimed. Syst. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    Smart wearables that combine AI and ICT are expected to be very useful in cardiovascular disease detection and prediction.

    “Therefore, smart wearables that combine AI and ICT are expected to be very useful in cardiovascular disease detection and prediction.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    If AI results are not explained, the medical community and patients will not have confidence in them, or at least be wary of adopting them.

    “if the results are not explained, the medical community and patients will not have confidence in them, or at least be wary of adopting them.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    Greater trust and wider use of smart wearables as tools for predicting CVDs will result from improved accuracy, reliability, feasibility, privacy, and explainability of such devices.

    “In other words, greater trust and wider use of smart wearables as tools for predicting CVDs will result from improved accuracy, reliability, feasibility, privacy, and explainability of such devices.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: When it comes to health, users are only willing to use tools that are highly accurate, understandable, private, and reliable.

  • May combine two claims.

    Interpretationreasoning

    Regulations protecting personal data make it more difficult to collect data to train models, which in turn makes it more difficult to improve the accuracy of model performance.

    “Although these regulations help protect private information, they pose new challenges to the traditional AI data processing model to varying degrees by making it more difficult to collect data to train models, which in turn makes it more difficult to improve the accuracy of model performance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    By using multimodal ML, researchers can evaluate many datasets simultaneously, which greatly improves the accuracy of their results.

    “However, by using this technology, researchers can evaluate many datasets simultaneously, which greatly improves the accuracy of their results.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    Smart watches and wristbands are an excellent, if not ideal, option for ECG capture devices and thus for predicting CVD parameters.

    “their ability to record a wide range of biometric data makes them an excellent, if not ideal, option for ECG capture devices and thus for predicting CVD parameters.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    Using federated learning to identify CVDs is expected to make it easier to collect more data, which in turn will improve detection accuracy.

    “It is expected that using FL to identify CVDs will make it easier to collect more data, which in turn will improve detection accuracy.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: More data can lead to more accurate models.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Federated machine learning has shown promising results in the healthcare industry.

    “Federated machine learning has shown promising results in the healthcare industry, as indicated in [146,147].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [146] Xu, 2021, Federated learning for healthcare informatics, J. Healthc. Informatics Res. source

      Not checked in the free run.

    • cites [147] Nguyen, 2022, Federated learning for smart healthcare: A survey, ACM Comput. Surv. (CSUR) source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisereasoning

    Explainable AI is proposed as a viable solution to address the challenges posed by the black-box nature of AI, ML, and DL models.

    “In this context, and to address the challenges posed by the black-box nature of AI, ML, and DL models, explainable AI (XAI) is proposed as a viable solution.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [137] Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., and Zhu, J. (2019). Explainable AI: A brief survey on history, research areas, approaches and challenges. CCF International Conference on Natural Language Processing and Chinese Computing, Springer. source

      Not checked in the free run.

    • cites [138] Doran, D., Schulz, S., and Besold, T.R. (2017). Besold. What does explainable AI really mean? A new conceptualization of perspectives. arXiv.

      Not checked in the free run.

    • cites [139] Gunning, 2019, XAI—Explainable artificial intelligence, Sci. Robot. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Using multimodal ML to analyze different data such as ECG, EHR recordings, and MRI images can help increase the accuracy of CVD detection and prediction.

    “Therefore, using the concept of multimodal ML to analyze different data such as ECG, EHR recordings, and MRI images can help increase the accuracy of CVD detection and prediction.”
    Quote in the paperNot a substantial claim
47 of 47

  • The paper relies on this without support.

    Publication counts reflect growing interest and widespread acceptance of smart wearables for CVD.

    “The number of publications addressing the use of smart wearables for cardiovascular disease management has jumped, reflecting both the growing interest in this area and the widespread acceptance of such devices.”

  • The paper relies on this without support.

    The data extracted from the included papers accurately represent the studies' details.

    “From each paper, the year of publication, disease(s) treated, vital signs recorded, hardware of the wearable device(s), embedded intelligent models, dataset(s) used, and outcome metrics were extracted.”

  • The paper relies on this without support.

    The performance metrics reported in the included studies are valid and comparable across studies.

    “In addition, the databases used for training and the performance metrics are detailed in Table 2.”

  • The paper relies on this without support.

    More data can lead to more accurate models.

    “it is generally accepted that more data can lead to more accurate models.”

  • The paper relies on this without support.

    Research in medicine has shown that the use of multiple vital signs may be more helpful in detecting a disease than the use of a single vital sign.

    “Research in the field of medicine has shown that the use of multiple vital signs may be more helpful in detecting a disease than the use of a single vital sign.”

  • The paper relies on this without support.

    One of the major barriers to the use of smart wearables to detect and predict cardiovascular disease is user acceptance, adoption, and participation.

    “One of the major barriers to the use of smart wearables to detect and predict cardiovascular disease is user acceptance, adoption, and participation.”

  • The paper relies on this without support.

    When it comes to health, users are only willing to use tools that are highly accurate, understandable, private, and reliable.

    “when it comes to health, users are only willing to use tools that are highly accurate, understandable, private, and reliable.”
7 of 7

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Reported search date predates included 2022 studies

    Major

    A search performed in October 2021 could not have retrieved the studies the review says were published in 2022.

  • Figure 3 is identified as two different diagrams

    Minor

    The paper describes Figure 3 both as the PRISMA study-selection diagram and as an ECG signal diagram.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 5

  1. Poslad, S. (2011). Ubiquitous Computing: Smart Devices, Environments and Interactions, John Wiley Sons.
  2. Thorp, E.O. (1998, January 19–20). The invention of the first wearable computer. Proceedings of the Digest of Papers. Second International Symposium on Wearable Computers (Cat. No. 98EX215), Pittsburgh, PA, USA.
  3. Myles, 2004, Brown. An introduction to decision tree modeling, J. Chemom. A J. Chemom. Soc., 18, 275
  4. Doran, D., Schulz, S., and Besold, T.R. (2017). Besold. What does explainable AI really mean? A new conceptualization of perspectives. arXiv.
  5. Mammen, P.M. (2021). Federated learning: Opportunities and challenges. arXiv.

Cited by

Sorted by citations

Related

Authors
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A.
Affiliations
  • Université du Québec à Rimouski (CA)
  • Université du Québec à Chicoutimi (CA)
  • Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
  • Islamic University of Lebanon (LB)
Corresponding author
Moshawrab, Mohammad
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2023-01-11
Volume
23
Issue
2
Pages
828
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s23020828
PMC
PMC9865666
OpenAlex
W4315572261
Cited by
120
References
161
Citations by year
2026: 24 · 2025: 36 · 2024: 41 · 2023: 19
FWCI
2.71
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Natural Sciences and Engineering Research Council of Canada (NSERC) (06351)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
wearable devices, cardiovascular disease detection, cardiovascular disease prediction, vital signs monitoring, wearable sensors, artificial intelligence in healthcare
MeSH terms
Wearable Electronic Devices, Artificial Intelligence, Cardiovascular Diseases, Humans

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review