CoChat
Sign Up
Cited by 423Non-Invasive Vital Sign MonitoringContext-Aware Activity Recognition SystemsWireless Body Area Networks

The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.

Wearable Sensors and Systems

Open access
Bonato, Paolo·2010·IEEE Engineering in Medicine and Biology Magazine
DOI 10.1109/memb.2010.936554
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/memb.2010.936554

Cites

66 of 66 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Schematic representation of a system for patients ' monitoring in the home and community settings. A subject is shown while exercising at the gym (e.g., undergoing balance therapy). Exercise compliance, exercise performance, and the associated physiological responses (i.e., heart rate and respiratory rate) are monitored via wearable sensors. A cell phone serves as a data logger and gateway for communication with a remote location via a cell phone network and/or the Internet.
Fig. 1 Schematic representation of a system for patients ' monitoring in the home and community settings. A subject is shown while exercising at the gym (e.g., undergoing balance therapy). Exercise compliance, exercise performance, and the associated physiological responses (i.e., heart rate and respiratory rate) are monitored via wearable sensors. A cell phone serves as a data logger and gateway for communication with a remote location via a cell phone network and/or the Internet.CC-BY-NC · From the PDF
Fig. 2 Schematic representation of a wearable system that allows one to collect movement and physiological data (i.e., heart rate and respiratory rate). Movement data are gathered using the four nodes equipped with accelerometers that are strapped around the ankles and wrists. Physiological data are collected via a chest strap.
Fig. 2 Schematic representation of a wearable system that allows one to collect movement and physiological data (i.e., heart rate and respiratory rate). Movement data are gathered using the four nodes equipped with accelerometers that are strapped around the ankles and wrists. Physiological data are collected via a chest strap.CC-BY-NC · From the PDF
Image 3
CC-BY-NC · From the PDF
Fig. 3 Examples of e-textile technologies developed over the past ten years. (a) A system developed by Wade and Asada [22] relying upon special buttons that carry sensor technology to record physiological and movement data. (b) A system developed by De Rossi ' s research team [23] for monitoring the movements of the shoulder and elbow via recordings of the voltage drop on conductive elastomers that are printed on the garment. (Figures used with permission.)
Fig. 3 Examples of e-textile technologies developed over the past ten years. (a) A system developed by Wade and Asada [22] relying upon special buttons that carry sensor technology to record physiological and movement data. (b) A system developed by De Rossi ' s research team [23] for monitoring the movements of the shoulder and elbow via recordings of the voltage drop on conductive elastomers that are printed on the garment. (Figures used with permission.)CC-BY-NC · From the PDF
Image 5
CC-BY-NC · From the PDF
Fig. 4 An important application of wearable technology consists of monitoring individuals while they exercise. The focus here is on wellness/fitness monitoring, with the potential development of methodologies to improve exercise compliance. Wearable sensors allow one to monitor movement, respiratory rate, and heart rate. ECG: electrocardiographic recordings; SpO2: oxygen saturation.
Fig. 4 An important application of wearable technology consists of monitoring individuals while they exercise. The focus here is on wellness/fitness monitoring, with the potential development of methodologies to improve exercise compliance. Wearable sensors allow one to monitor movement, respiratory rate, and heart rate. ECG: electrocardiographic recordings; SpO2: oxygen saturation.CC-BY-NC · From the PDF
Image 7
CC-BY-NC · From the PDF
Fig. 5 Home-monitoring applications would often benefit from technology for remote examination of patients. The screen capture presented shows the graphical interface of an application recently developed by Matt Welsh ' s research team at Harvard University to monitor patients with Parkinson ' s disease in the home. The software application provides clinicians with access to wearable sensor data and measures the severity of parkinsonian symptoms [45].
Fig. 5 Home-monitoring applications would often benefit from technology for remote examination of patients. The screen capture presented shows the graphical interface of an application recently developed by Matt Welsh ' s research team at Harvard University to monitor patients with Parkinson ' s disease in the home. The software application provides clinicians with access to wearable sensor data and measures the severity of parkinsonian symptoms [45].CC-BY-NC · From the PDF
Fig. 6 Rehabilitation robotics is combined with wearable technology for the purpose of enhancing functions that are not provided by robotic systems [51]. Features provided by commercially available robotic systems like the one shown in this figure (Armeo by Hocoma AG) can be augmented via the use of wearable sensors. In the example presented, a sensorized glove provides the system with the ability to implement exercises targeting the recovery of hand function. This capability would not be available if the robot were to be used alone.
Fig. 6 Rehabilitation robotics is combined with wearable technology for the purpose of enhancing functions that are not provided by robotic systems [51]. Features provided by commercially available robotic systems like the one shown in this figure (Armeo by Hocoma AG) can be augmented via the use of wearable sensors. In the example presented, a sensorized glove provides the system with the ability to implement exercises targeting the recovery of hand function. This capability would not be available if the robot were to be used alone.CC-BY-NC · From the PDF
Image 10
CC-BY-NC · From the PDF
Fig. 7 It is envisioned that combining (a) wearable technology and (b) home robots will result in novel home-monitoring applications. Wearable sensors will communicate wirelessly with home robots (image courtesy of iRobot) that will in turn respond to alarm messages using onboard capabilities (i.e., image processing) to interact with the patient and assess the severity of the situation.
Fig. 7 It is envisioned that combining (a) wearable technology and (b) home robots will result in novel home-monitoring applications. Wearable sensors will communicate wirelessly with home robots (image courtesy of iRobot) that will in turn respond to alarm messages using onboard capabilities (i.e., image processing) to interact with the patient and assess the severity of the situation.CC-BY-NC · From the PDF
Fig. 8 Complex systems under development will soon provide enhanced monitoring capability, the ability to facilitate clinical interventions, and features that are suitable for detecting emergency situations, assess needs (e.g., via gathering images and other information using a home robot), and alert a remote clinical center when necessary.
Fig. 8 Complex systems under development will soon provide enhanced monitoring capability, the ability to facilitate clinical interventions, and features that are suitable for detecting emergency situations, assess needs (e.g., via gathering images and other information using a home robot), and alert a remote clinical center when necessary.CC-BY-NC · From the PDF
Image 13
CC-BY-NC · From the PDF

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.

A few free checks a day · papers already verified open instantly · results are public

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.

The self-check needs the paper’s full text.

Citations

Cites

Show all 66 references

Research works printed but not linked: 2

  1. 0, Shimmer
  2. baechlin, 0, Assistive cueing, context awareness, freezing of gait, Parkinson's disease, personal health assistant, IEEE Trans Inf Technol Biomed

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Bonato, P.
Affiliations
Harvard University (US)
Corresponding author
Bonato, Paolo
ORCID
Bonato, Paolo
Venue
IEEE Engineering in Medicine and Biology Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2010-05
Volume
29
Issue
3
Pages
25-36
ISSN
0739-5175 (ISSN-L)
DOI
10.1109/memb.2010.936554
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
Licence
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.

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