AI-based task classification with pressure insoles for occupational safety

Simple item page
dc.contributor.authorO’Sullivan, Patriciaen
dc.contributor.authorMenolotto, Matteoen
dc.contributor.authorVisentin, Andreaen
dc.contributor.authorO’Flynn, Brendanen
dc.contributor.authorKomaris, Dimitrios-Sokratisen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-02-08T16:00:20Z
dc.date.available2024-02-08T16:00:20Z
dc.date.issued2024en
dc.description.abstractPressure insoles allow for the collection of real time pressure data inside and outside a laboratory setting as they are non-intrusive and can be simply integrated into industrial environments for occupational health and safety monitoring purposes. Activity detection is important for the safety and wellbeing of workers, and the present study aims to employ pressure insoles to detect the type of industry-related task an individual is performing by using random forest, an artificial intelligence-based classification technique. Twenty subjects wore loadsol® pressure insoles and performed five specific tasks associated with a typical workflow: standing, walking, pick and place, assembly, and manual handling. For each activity, statistical and morphological features were extracted to create a training dataset. The classifier performed with an accuracy over 82%, using ten-fold cross-validation, for a time window of 5 seconds, showing the potential for task classification in edge-AI applications in smart manufacturing environments. A re-analysis focused on the five most influential features obtained 83% accuracy. The combination of random forest and in-depth feature analysis (SHAP) provided insights into the importance of features and the impact of their value on each task class. Such an understanding can aid in reducing misclassifications for health and safety purposes and can aid in the design of pressure insoles that are optimized for impactful features. The accuracy achieved is comparable to similar task classification studies but with the benefit of added explainability, which increases transparency and, thereby, trust in the classifier decisions.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2289-P2-INSIGHT)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO’Sullivan, P., Menolotto, M., Visentin, A., O’Flynn, B. and Komaris, D.-S. (2024) 'AI-based task classification with pressure insoles for occupational safety', IEEE Access, doi: https://doi.org/10.1109/ACCESS.2024.3361754en
dc.identifier.doi10.1109/access.2024.3361754en
dc.identifier.issn2169-3536en
dc.identifier.journaltitleIEEE Accessen
dc.identifier.urihttps://hdl.handle.net/10468/15536
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.rights© 2024, the Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectHuman Activity Recognitionen
dc.subjectMachine learningen
dc.subjectWearable sensorsen
dc.titleAI-based task classification with pressure insoles for occupational safetyen
dc.typeArticle (peer-reviewed)en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AI-based_Task_Classification_with_Pressure_Insoles_for_Occupational_Safety.pdf
Size:
1010.93 KB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tyndall National Institute - Journal Articles