CoChat
Sign Up
Cited by 159Context-Aware Activity Recognition SystemsPhysical Activity and HealthAdvanced Sensor and Energy Harvesting Materials

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

Open access
Banos, Oresti, Toth, Mate, Damas, Miguel, Pomares, Hector, Rojas, Ignacio·2014·Sensors
DOI 10.3390/s140609995PMC PMC4118358
Open this paper in CoChat — free accountOpen PDF

Abstract

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.

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 · 10.3390/s140609995

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Example of sensor displacement introduced during the user self-placement of a sensor (a), and its effect at the feature level (b). In this particular example, the displacement with respect to the predefined deployment in the self-placement case applies to the right calf (RC), while the placement remains approximately similar for the sensor attached to the left calf (LC). In (b), the mean and standard deviation computed from the sensor acceleration signals is represented for various instances of a given activity. Confidence ellipses are plotted to represent the feature distribution for each case.
Figure 1. Example of sensor displacement introduced during the user self-placement of a sensor (a), and its effect at the feature level (b). In this particular example, the displacement with respect to the predefined deployment in the self-placement case applies to the right calf (RC), while the placement remains approximately similar for the sensor attached to the left calf (LC). In (b), the mean and standard deviation computed from the sensor acceleration signals is represented for various instances of a given activity. Confidence ellipses are plotted to represent the feature distribution for each case.CC BY · PubMed Central
Figure 2. Example of possible sensor placements according to the (a) ideal, (b) self-placement and (c) induced-displacement deployments. In (b), the sensor is arbitrarily rotated 180° (approximately) by the user with respect to the ideal positioning (a). In (c), the expert explicitly displaces the sensor from the middle upper arm to the elbow.
Figure 2. Example of possible sensor placements according to the (a) ideal, (b) self-placement and (c) induced-displacement deployments. In (b), the sensor is arbitrarily rotated 180° (approximately) by the user with respect to the ideal positioning (a). In (c), the expert explicitly displaces the sensor from the middle upper arm to the elbow.CC BY · PubMed Central
Figure 3. Experimental setup (cardio-fitness room). Eight Xsens units are placed on each body limb and an additional one on the back. A laptop is used to store the recorded data and for labeling tasks. A camera records each session for offline post-processing. Sensor legend: left calf (LC), left thigh (LT), right calf (RC), right thigh (RT), back (BACK), left lower arm (LLA), left upper arm (LUA), right lower arm (RLA) and right upper arm (RUA).
Figure 3. Experimental setup (cardio-fitness room). Eight Xsens units are placed on each body limb and an additional one on the back. A laptop is used to store the recorded data and for labeling tasks. A camera records each session for offline post-processing. Sensor legend: left calf (LC), left thigh (LT), right calf (RC), right thigh (RT), back (BACK), left lower arm (LLA), left upper arm (LUA), right lower arm (RLA) and right upper arm (RUA).CC BY · PubMed Central
Figure 4. Missing activity data for each particular subject (shading spots). (a) For ideal and self-placement conditions: the legend identifies the corresponding sensor deployment (both ≡ self-placed and ideally-placed). (b) For the induced-displaced condition: only Participants 2, 5 and 15 were considered.
Figure 4. Missing activity data for each particular subject (shading spots). (a) For ideal and self-placement conditions: the legend identifies the corresponding sensor deployment (both ≡ self-placed and ideally-placed). (b) For the induced-displaced condition: only Participants 2, 5 and 15 were considered.CC BY · PubMed Central
Figure 5. Shading spots identify the displaced sensors for the (a) self-placement and (b) induced-displacement deployments. Only Participants 2, 5 and 15 were considered in (b).
Figure 5. Shading spots identify the displaced sensors for the (a) self-placement and (b) induced-displacement deployments. Only Participants 2, 5 and 15 were considered in (b).CC BY · PubMed Central
Figure 6. The accuracy (average bar and standard deviation whiskers) results from the evaluation of the single sensor approach across all subjects and sensors for the (a) ideal-placement; (b) self-placement and (c) induced-displacement settings. The top legend identifies the classification paradigm. The horizontal axis labels identify the feature set used for each experiment. The activity recognition dataset (i.e., the number of activities) used is respectively underlined.
Figure 6. The accuracy (average bar and standard deviation whiskers) results from the evaluation of the single sensor approach across all subjects and sensors for the (a) ideal-placement; (b) self-placement and (c) induced-displacement settings. The top legend identifies the classification paradigm. The horizontal axis labels identify the feature set used for each experiment. The activity recognition dataset (i.e., the number of activities) used is respectively underlined.CC BY · PubMed Central
Figure 7. Accuracy (mean and standard deviation) results from the evaluation of the feature fusion model for the (a) ideal-placement, (b) self-placement and (c–f) induced-displacement (number of sensors) settings. The top legend identifies the classification paradigm. The horizontal axis labels identify the feature set used for each experiment. The activity recognition dataset (i.e., the number of activities) used is respectively underlined.
Figure 7. Accuracy (mean and standard deviation) results from the evaluation of the feature fusion model for the (a) ideal-placement, (b) self-placement and (c–f) induced-displacement (number of sensors) settings. The top legend identifies the classification paradigm. The horizontal axis labels identify the feature set used for each experiment. The activity recognition dataset (i.e., the number of activities) used is respectively underlined.CC BY · PubMed Central
Figure 8. Accuracy (mean and standard deviation) results from the evaluation of the decision fusion model for the (a) ideal-placement; (b) self-placement and (c–f) induced-displacement (number of sensors) settings. The top legend identifies the base classifiers paradigm. The horizontal axis labels identify the feature set used for each experiment. The activity recognition dataset (i.e., the number of activities) used is respectively underlined.
Figure 8. Accuracy (mean and standard deviation) results from the evaluation of the decision fusion model for the (a) ideal-placement; (b) self-placement and (c–f) induced-displacement (number of sensors) settings. The top legend identifies the base classifiers paradigm. The horizontal axis labels identify the feature set used for each experiment. The activity recognition dataset (i.e., the number of activities) used is respectively underlined.CC BY · PubMed Central

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 52 references

Research works printed but not linked: 2

  1. Duda, R.O., Hart, P.E., and Stork, D.G. (2000). Pattern Classification, Wiley-Interscience. [2nd ed].
  2. Theodoridis, S., and Koutroumbas, K. (2008). Pattern Recognition, Academic Press. [4th ed].

Cited by

Sorted by citations

See all 159

Related

Metadata

Authors
Banos, O., Toth, M., Damas, M., Pomares, H., & Rojas, I.
Affiliations
  • Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies-University of Granada (CITIC-UGR), C/Calle Periodista RafaelGomez Montero 2, Granada E18071, Spain
  • University of the Basque Country (ES)
  • Universidad de Granada (ES)
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2014-06-06
Volume
14
Issue
6
Pages
9995-10023
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s140609995
PMC
PMC4118358
OpenAlex
W2157091296
Cited by
159
References
52
Citations by year
2026: 9 · 2025: 10 · 2024: 10 · 2023: 12 · 2022: 11 · 2021: 17 · 2020: 13 · 2019: 8 · 2018: 20 · 2017: 18 · 2016: 15 · 2015: 13 · 2014: 3
FWCI
14.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
  • Communities in ADDI (Universidad del Pais Vasco) (submittedVersion, other-oa)
  • Communities in ADDI (University of the Basque Country) (submittedVersion, other-oa)
  • Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by-nc-nd)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Hispana (submittedVersion, other-oa)
Funders
European Commission (264738, 228398)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
  • Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
Keywords
wearable activity recognition, sensor displacement, self-placement, sensor placement, wearable sensors
MeSH terms
Activities of Daily Living, Adult, Clothing, Equipment Design, Female, Humans, Male, Exercise, Monitoring, Ambulatory, Young Adult

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