Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety
Appropriate use of helmets as industrial personal protective gear is a long-standing challenge. The dilemma for any user wearing a helmet is thermal discomfort versus the chances of head injuries while not wearing it. Applying helmet microclimate psychrometry, we propose a logistic regression- (LR)...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/151769 |
| _version_ | 1811689779171950592 |
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| author | Tan, Yan Hao Hitesh, Agarwal Li, Holden King Ho |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Tan, Yan Hao Hitesh, Agarwal Li, Holden King Ho |
| author_sort | Tan, Yan Hao |
| collection | NTU |
| description | Appropriate use of helmets as industrial personal protective gear is a long-standing challenge. The dilemma for any user wearing a helmet is thermal discomfort versus the chances of head injuries while not wearing it. Applying helmet microclimate psychrometry, we propose a logistic regression- (LR) based machine learning (ML) algorithm coupled with low-cost and readily available MEMS sensors to determine if a helmet was worn (W) or not worn (NW) by a human user. Experiment runs involving human subject (S) and mannequin experiment control (C) groups were conducted across no mask (NM) and mask (M) conditions. Only ambient-microclimate humidity difference (AMHD) was a feasible parameter for helmet wearing determination with 71 to 85% goodness of fit, 72 to 76% efficacy, and distinction from control group. Ambient-microclimate humidity difference’s rate of change (AMHDROC) had high correlation to helmet wearing and removal initiations and was quantitatively better in all measures. However, its feasibility was doubtful for continuous use beyond 1 min due to plateauing AMHD response. Experiments with control groups and temperature measurement showed invariant response to helmet worn or not worn with goodness of fit and efficacy consolidation to 50%. Results showed the algorithm can make helmet-wearing determinations with combination of analysis and use of data that was individually authentic and non-identifiable. This is an improvement as compared to state of the art skin-contact mechanisms and image analytics methods in enabling safety enhancements through data-driven worker safety ownership. |
| first_indexed | 2024-10-01T05:53:31Z |
| format | Journal Article |
| id | ntu-10356/151769 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:53:31Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1517692023-03-04T17:22:32Z Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety Tan, Yan Hao Hitesh, Agarwal Li, Holden King Ho School of Mechanical and Aerospace Engineering School of Computer Science and Engineering Engineering::Mechanical engineering Helmet Appropriate Use Appropriate use of helmets as industrial personal protective gear is a long-standing challenge. The dilemma for any user wearing a helmet is thermal discomfort versus the chances of head injuries while not wearing it. Applying helmet microclimate psychrometry, we propose a logistic regression- (LR) based machine learning (ML) algorithm coupled with low-cost and readily available MEMS sensors to determine if a helmet was worn (W) or not worn (NW) by a human user. Experiment runs involving human subject (S) and mannequin experiment control (C) groups were conducted across no mask (NM) and mask (M) conditions. Only ambient-microclimate humidity difference (AMHD) was a feasible parameter for helmet wearing determination with 71 to 85% goodness of fit, 72 to 76% efficacy, and distinction from control group. Ambient-microclimate humidity difference’s rate of change (AMHDROC) had high correlation to helmet wearing and removal initiations and was quantitatively better in all measures. However, its feasibility was doubtful for continuous use beyond 1 min due to plateauing AMHD response. Experiments with control groups and temperature measurement showed invariant response to helmet worn or not worn with goodness of fit and efficacy consolidation to 50%. Results showed the algorithm can make helmet-wearing determinations with combination of analysis and use of data that was individually authentic and non-identifiable. This is an improvement as compared to state of the art skin-contact mechanisms and image analytics methods in enabling safety enhancements through data-driven worker safety ownership. Ministry of Education (MOE) Published version This research was funded by Ministry of Education (MOE) Tier 1 Award 020212-00001. 2021-07-15T11:10:26Z 2021-07-15T11:10:26Z 2021 Journal Article Tan, Y. H., Hitesh, A. & Li, H. K. H. (2021). Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety. Micromachines, 12(4), 449-. https://dx.doi.org/10.3390/mi12040449 2072-666X https://hdl.handle.net/10356/151769 10.3390/mi12040449 33923785 2-s2.0-85105171702 4 12 449 en 020212-00001 Micromachines © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Mechanical engineering Helmet Appropriate Use Tan, Yan Hao Hitesh, Agarwal Li, Holden King Ho Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety |
| title | Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety |
| title_full | Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety |
| title_fullStr | Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety |
| title_full_unstemmed | Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety |
| title_short | Application of machine learning algorithm on MEMS-based sensors for determination of helmet wearing for workplace safety |
| title_sort | application of machine learning algorithm on mems based sensors for determination of helmet wearing for workplace safety |
| topic | Engineering::Mechanical engineering Helmet Appropriate Use |
| url | https://hdl.handle.net/10356/151769 |
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