CoChat
Sign Up
Cited by 14Context-Aware Activity Recognition SystemsPhysical Activity and HealthNon-Invasive Vital Sign Monitoring

Methodologies for continuous activity classification of user through wearable devices: Feasibility and preliminary investigation

Pietroni, Filippo, Casaccia, Sara, Revel, Gian Marco, Scalise, Lorenzo·2016·2016 IEEE Sensors Applications Symposium (SAS)
DOI 10.1109/sas.2016.7479867
Open this paper in CoChat — free account

Abstract

Nowadays, the possibility to use wearable devices at low-cost and with high measuring capabilities is widely spread. They are able to collect multiple physiological and physical quantities, but the information provided is generally of "low level" (series of raw data) for the end-user, caregiver or the medical staff. A smart processing and an intelligent combination of these data may lead to the identification of more refined indicators (e.g. the kind of activity performed by the user) and so easier to understand information. In this work, the authors have performed a preliminary investigation on how to discriminate different levels of physical activity (resting, walking, running) conducted by a user. The analysis has been carried out with different trials and the use of a commercial wearable sensor (BioHarness 3.0), which measures five physiological and physical quantities. Different approaches, based on classification and clustering techniques, have been tested to prove their ability in discriminating the tasks performed. Results show that a threshold-based approach, applied to the physical quantities measured, is able to identify the activities conducted by the user, with an average accuracy of 98%. Moreover, a novel indicator based on the combination of both physiological and physical quantities is proposed and discussed in the paper. The integration of physiological data (e.g. Heart Rate) could lead to more "high level" information, which should be better investigated in future works.

Integrity signals

Registry

Crossref · 10.1109/sas.2016.7479867

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Supervised learning methods are used to train the machine in HAR applications that analyse eating patterns and exercise in patients suffering from diabetes, obesity and heart disease, as well as applications that measure the number of calories expended during an exercise regimen.

    “For example, supervised learning methods are used to train the machine in HAR applications that analyse eating patterns and exercise in patients suffering from diabetes, obesity and heart disease, as well as applications that measure the number of calories expended during an exercise regimen [144].”

    From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [144]

Cites

Flagged first, then by citations

3 of the 37 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Pietroni, F., Casaccia, S., Revel, G. M., & Scalise, L.
Affiliations
Marche Polytechnic University (IT)
ORCID
Venue
2016 IEEE Sensors Applications Symposium (SAS)
Publisher
IEEE
Published
2016-04
Pages
1-6
DOI
10.1109/sas.2016.7479867
OpenAlex
W2416752317
Cited by
14
References
37
Citations by year
2025: 1 · 2022: 1 · 2021: 6 · 2019: 3 · 2018: 1 · 2017: 1 · 2016: 1
FWCI
0.69
Type
conference paper
Language
en
Open access
closed
Locations
  • IEEE Sensors Applications Symposium (SAS) (publishedVersion)
  • Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords
activity classification, wearable sensors, physical activity monitoring, walking detection, resting state, threshold-based classification, physiological signals, heart rate, clustering

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