The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring
Open accessAbstract
Many devices and solutions for remote electrocardiogram (ECG) monitoring have been proposed in the literature. These solutions typically have a large marginal cost per added sensor and are not seamlessly integrated with other smart home solutions. Here, we propose an ECG remote monitoring system that is dedicated to non-technical users in need of long-term health monitoring in residential environments and is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype consists of a complete vertical solution with a series of advantages with respect to the state of the art, considering both the prototypes with integrated front end and prototypes realized with off-the-shelf components: 1) ECG prototype sensors with record-low energy per effective number of quantized levels; 2) an architecture providing low marginal cost per added sensor/user; and 3) the possibility of seamless integration with other smart home systems through a single IoT infrastructure.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
A wireless wearable ECG sensor for long-term applications
Nemati, E., Deen, M., & Mondal, T. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6122530 · Cited by 418
Bayesian probabilistic population projections for all countries
Raftery, A. E., Li, N., Ševčíková, H., Gerland, P., & Heilig, G. K. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211452109 · Cited by 298
Chuo, Y., Marzencki, M., Hung, B., Jaggernauth, C., Tavakolian, K., Lin, P., & Kaminska, B. (2010) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2010.2052616 · Cited by 102
Altini, M., Polito, S., Penders, J., Kim, H., Van Helleputte, N., Kim, S., & Yazicioglu, F. (2011) · Proceedings of the 2nd Conference on Wireless Health · doi:10.1145/2077546.2077564 · Cited by 36
Cited by
Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F., & Lindén, M. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0760-1 · Cited by 351
Large-Scale Distributed Dedicated- and Non-Dedicated Smart City Sensing Systems
Habibzadeh, H., Qin, Z., Soyata, T., & Kantarci, B. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2725638 · Cited by 114
Satija, U., Ramkumar, B., & Manikandan, M. S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2877055 · Cited by 102
A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health
Sahoo, P., Thakkar, H., & Lee, M.-Y. (2017) · Sensors · doi:10.3390/s17040711 · Cited by 81
Metadata
- Authors
- Spano, E., Di Pascoli, S., & Iannaccone, G.
- Venue
- IEEE Sensors Journal
- Published
- 2016-07
- Volume
- 16
- Issue
- 13
- Pages
- 5452-5462
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2346753755
- Cited by
- 256
- References
- 5
- FWCI
- 20.42
- Type
- journal article
- Language
- en
- Open access
- green
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring. https://app.cochat.ai/papers/10.1109/jsen.2016.2564995
- The paper
- Spano, E., Di Pascoli, S., & Iannaccone, G. (2016). Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2016.2564995
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
