Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment
The domain of data processing is essential to accelerate the delivery of information based on electronic performance monitoring (EPM). The classification of the activities conducted by craft workers can enhance the mechanisation and productivity of activities. However, research in this field is main...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/12/8/1174 |
| _version_ | 1797446723161817088 |
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| author | Diego Calvetti Luís Sanhudo Pedro Mêda João Poças Martins Miguel Chichorro Gonçalves Hipólito Sousa |
| author_facet | Diego Calvetti Luís Sanhudo Pedro Mêda João Poças Martins Miguel Chichorro Gonçalves Hipólito Sousa |
| author_sort | Diego Calvetti |
| collection | DOAJ |
| description | The domain of data processing is essential to accelerate the delivery of information based on electronic performance monitoring (EPM). The classification of the activities conducted by craft workers can enhance the mechanisation and productivity of activities. However, research in this field is mainly based on simulations of binary activities (i.e., performing or not performing an action). To enhance EPM research in this field, a dynamic laboratory circuit-based simulation of ten common constructions activities was performed. A circuit feasibility case study of EPM using wearable devices was conducted, where two different data processing approaches were tested: machine learning and multivariate statistical analysis (MSA). Using the acceleration data of both wrists and the dominant leg, the machine-learning approach achieved an accuracy between 92 and 96%, while MSA achieved 47–76%. Additionally, the MSA approach achieved 32–76% accuracy by monitoring only the dominant wrist. Results highlighted that the processes conducted with manual tools (e.g., hammering and sawing) have prominent dominant-hand motion characteristics that are accurately detected with one wearable. However, free-hand performing (masonry), walking and not operating value (e.g., sitting) require more motion analysis data points, such as wrists and legs. |
| first_indexed | 2024-03-09T13:44:41Z |
| format | Article |
| id | doaj.art-e140c78a458c4710bbcd419aaabd7c7c |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T13:44:41Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-e140c78a458c4710bbcd419aaabd7c7c2023-11-30T21:02:26ZengMDPI AGBuildings2075-53092022-08-01128117410.3390/buildings12081174Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation DeploymentDiego Calvetti0Luís Sanhudo1Pedro Mêda2João Poças Martins3Miguel Chichorro Gonçalves4Hipólito Sousa5CONSTRUCT/GEQUALTEC, Construction Institute, Faculty of Engineering, Porto University, 4200-465 Porto, PortugalBUILT CoLAB—Digital Built Environment, 4150-171 Porto, PortugalCONSTRUCT/GEQUALTEC, Construction Institute, Faculty of Engineering, Porto University, 4200-465 Porto, PortugalCONSTRUCT/GEQUALTEC, Faculty of Engineering, Porto University, 4200-465 Porto, PortugalCONSTRUCT/GEQUALTEC, Faculty of Engineering, Porto University, 4200-465 Porto, PortugalCONSTRUCT/GEQUALTEC, Faculty of Engineering, Porto University, 4200-465 Porto, PortugalThe domain of data processing is essential to accelerate the delivery of information based on electronic performance monitoring (EPM). The classification of the activities conducted by craft workers can enhance the mechanisation and productivity of activities. However, research in this field is mainly based on simulations of binary activities (i.e., performing or not performing an action). To enhance EPM research in this field, a dynamic laboratory circuit-based simulation of ten common constructions activities was performed. A circuit feasibility case study of EPM using wearable devices was conducted, where two different data processing approaches were tested: machine learning and multivariate statistical analysis (MSA). Using the acceleration data of both wrists and the dominant leg, the machine-learning approach achieved an accuracy between 92 and 96%, while MSA achieved 47–76%. Additionally, the MSA approach achieved 32–76% accuracy by monitoring only the dominant wrist. Results highlighted that the processes conducted with manual tools (e.g., hammering and sawing) have prominent dominant-hand motion characteristics that are accurately detected with one wearable. However, free-hand performing (masonry), walking and not operating value (e.g., sitting) require more motion analysis data points, such as wrists and legs.https://www.mdpi.com/2075-5309/12/8/1174electronic performance monitoringwearable devicesprocess modellingmachine learningmultivariate statistical analysis |
| spellingShingle | Diego Calvetti Luís Sanhudo Pedro Mêda João Poças Martins Miguel Chichorro Gonçalves Hipólito Sousa Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment Buildings electronic performance monitoring wearable devices process modelling machine learning multivariate statistical analysis |
| title | Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment |
| title_full | Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment |
| title_fullStr | Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment |
| title_full_unstemmed | Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment |
| title_short | Construction Tasks Electronic Process Monitoring: Laboratory Circuit-Based Simulation Deployment |
| title_sort | construction tasks electronic process monitoring laboratory circuit based simulation deployment |
| topic | electronic performance monitoring wearable devices process modelling machine learning multivariate statistical analysis |
| url | https://www.mdpi.com/2075-5309/12/8/1174 |
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