Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach
Electrical line workers (ELWs) experience harsh environments, characterized by long shifts, remote operations, and potentially risky tasks. Wearables present an opportunity for unobtrusive monitoring of productivity and safety. A prerequisite to monitoring is the automated identification of the task...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733853/?tool=EBI |
| _version_ | 1811178093630455808 |
|---|---|
| author | Saeb Ragani Lamooki Sahand Hajifar Jacqueline Hannan Hongyue Sun Fadel Megahed Lora Cavuoto |
| author_facet | Saeb Ragani Lamooki Sahand Hajifar Jacqueline Hannan Hongyue Sun Fadel Megahed Lora Cavuoto |
| author_sort | Saeb Ragani Lamooki |
| collection | DOAJ |
| description | Electrical line workers (ELWs) experience harsh environments, characterized by long shifts, remote operations, and potentially risky tasks. Wearables present an opportunity for unobtrusive monitoring of productivity and safety. A prerequisite to monitoring is the automated identification of the tasks being performed. Human activity recognition has been widely used for classification for activities of daily living. However, the literature is limited for electrical line maintenance/repair tasks due to task variety and complexity. We investigated how features can be engineered from a single wrist-worn accelerometer for the purpose of classifying ELW tasks. Specifically, three classifiers were investigated across three feature sets (time, frequency, and time-frequency) and two window lengths (4 and 10 seconds) to identify ten common ELW tasks. Based on data from 37 participants in a lab environment, two application scenarios were evaluated: (a) intra-subject, where individualized models were trained and deployed for each worker; and (b) inter-subject, where data was pooled to train a general model that can be deployed for new workers. Accuracies ≥ 93% were achieved for both scenarios, and increased to ≥96% with 10-second windows. Overall and class-specific feature importance were computed, and the impact of those features on the obtained predictions were explained. This work will contribute to the future risk mitigation of ELWs using wearables. |
| first_indexed | 2024-04-11T06:13:40Z |
| format | Article |
| id | doaj.art-f3d03b40e6424907870a828d3b31c623 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T06:13:40Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f3d03b40e6424907870a828d3b31c6232022-12-22T04:41:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approachSaeb Ragani LamookiSahand HajifarJacqueline HannanHongyue SunFadel MegahedLora CavuotoElectrical line workers (ELWs) experience harsh environments, characterized by long shifts, remote operations, and potentially risky tasks. Wearables present an opportunity for unobtrusive monitoring of productivity and safety. A prerequisite to monitoring is the automated identification of the tasks being performed. Human activity recognition has been widely used for classification for activities of daily living. However, the literature is limited for electrical line maintenance/repair tasks due to task variety and complexity. We investigated how features can be engineered from a single wrist-worn accelerometer for the purpose of classifying ELW tasks. Specifically, three classifiers were investigated across three feature sets (time, frequency, and time-frequency) and two window lengths (4 and 10 seconds) to identify ten common ELW tasks. Based on data from 37 participants in a lab environment, two application scenarios were evaluated: (a) intra-subject, where individualized models were trained and deployed for each worker; and (b) inter-subject, where data was pooled to train a general model that can be deployed for new workers. Accuracies ≥ 93% were achieved for both scenarios, and increased to ≥96% with 10-second windows. Overall and class-specific feature importance were computed, and the impact of those features on the obtained predictions were explained. This work will contribute to the future risk mitigation of ELWs using wearables.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733853/?tool=EBI |
| spellingShingle | Saeb Ragani Lamooki Sahand Hajifar Jacqueline Hannan Hongyue Sun Fadel Megahed Lora Cavuoto Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach PLoS ONE |
| title | Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach |
| title_full | Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach |
| title_fullStr | Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach |
| title_full_unstemmed | Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach |
| title_short | Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach |
| title_sort | classifying tasks performed by electrical line workers using a wrist worn sensor a data analytic approach |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733853/?tool=EBI |
| work_keys_str_mv | AT saebraganilamooki classifyingtasksperformedbyelectricallineworkersusingawristwornsensoradataanalyticapproach AT sahandhajifar classifyingtasksperformedbyelectricallineworkersusingawristwornsensoradataanalyticapproach AT jacquelinehannan classifyingtasksperformedbyelectricallineworkersusingawristwornsensoradataanalyticapproach AT hongyuesun classifyingtasksperformedbyelectricallineworkersusingawristwornsensoradataanalyticapproach AT fadelmegahed classifyingtasksperformedbyelectricallineworkersusingawristwornsensoradataanalyticapproach AT loracavuoto classifyingtasksperformedbyelectricallineworkersusingawristwornsensoradataanalyticapproach |