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Wearable Sensors and Systems
Open accessAbstract
It is now more than 50 years since the time when clinical monitoring of individuals in the home and community settings was first envisioned. Until recently, technologies to enable such vision were lacking. However, wearable sensors and systems developed over the past decade have provided the tools to finally implement and deploy technology with the capabilities required by researchers in the field of patients' home monitoring. As discussed, potential applications of these technologies include the early diagnosis of diseases such as congestive heart failure, the prevention of chronic conditions such as diabetes, improved clinical management of neurodegenerative conditions such as Parkinson's disease, and the ability to promptly respond to emergency situations such as seizures in patients with epilepsy and cardiac arrest in subjects undergoing cardiovascular monitoring. Current research efforts are now focused on the development of more complex systems for home monitoring of individuals with a variety of preclinical and clinical conditions. Recent research on the clinical assessment of wearable technology promises to deliver methodologies that are expected to lead to clinical adoption within the next five to ten years. In particular, combining home robots and wearable technology is likely to be a key step toward achieving the goal of effectively monitoring patients in the home. These efforts to merge home robots and wearable technology are expected to enable a new generation of complex systems with the ability to monitor subjects' status, facilitate the administration of interventions, and provide an invaluable tool to respond to emergency situations.
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“Their current capabilities include physiological and biochemical sensing, as well as motion sensing [6,7].”
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Metadata
- Authors
- Bonato, P.
- Affiliations
- Harvard University (US)
- Corresponding author
- Bonato, Paolo
- ORCID
- Bonato, Paolo
- Venue
- IEEE Engineering in Medicine and Biology Magazine
- Published
- 2010-05
- Volume
- 29
- Issue
- 3
- Pages
- 25-36
- ISSN
- 0739-5175 (ISSN-L)
- OpenAlex
- W2032802664
- Cited by
- 423
- References
- 66
- Citations by year
- 2026: 22 · 2025: 30 · 2024: 26 · 2023: 15 · 2022: 29 · 2021: 25 · 2020: 29 · 2019: 13 · 2018: 31 · 2017: 29 · 2016: 40 · 2015: 36 · 2014: 38 · 2013: 22 · 2012: 23
- FWCI
- 11.35
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Engineering in Medicine and Biology Magazine (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- wearable sensors, home monitoring, remote patient monitoring, home robots, congestive heart failure, diabetes prevention, Parkinson's disease, epilepsy, cardiac arrest, cardiovascular monitoring, emergency response
- MeSH terms
- Clothing, Diagnosis, Computer-Assisted, Equipment Design, Signal Processing, Computer-Assisted, Telemedicine, Monitoring, Ambulatory
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.
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