Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique
Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using ki...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1468 |
| _version_ | 1826531335526678528 |
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| author | Moritz Schneider Kevin Seeser-Reich Armin Fiedler Udo Frese |
| author_facet | Moritz Schneider Kevin Seeser-Reich Armin Fiedler Udo Frese |
| author_sort | Moritz Schneider |
| collection | DOAJ |
| description | Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. High-validation F1 scores were achieved by the best-performing models, particularly CNNs and InceptionTime, indicating their effectiveness in near-fall classification. The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. Nevertheless, our findings suggest the applicability of machine-learning-based STF prevention systems for workplace safety monitoring and, more generally, applications in fall mitigation. To further improve the accuracy and generalizability of the system, future research should investigate multimodal data integration and improved classification techniques. |
| first_indexed | 2025-03-14T01:33:42Z |
| format | Article |
| id | doaj.art-461a36465a8e497ab0de6719302363ec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2025-03-14T01:33:42Z |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-461a36465a8e497ab0de6719302363ec2025-03-12T13:59:54ZengMDPI AGSensors1424-82202025-02-01255146810.3390/s25051468Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning TechniqueMoritz Schneider0Kevin Seeser-Reich1Armin Fiedler2Udo Frese3Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA), 53757 Sankt Augustin, GermanyInstitute for Occupational Safety and Health of the German Social Accident Insurance (IFA), 53757 Sankt Augustin, GermanyRheinAhrCampus, Koblenz University of Applied Sciences, 53424 Remagen, GermanyGerman Research Center for Artificial Intelligence (DFKI), 28359 Bremen, GermanySlips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. High-validation F1 scores were achieved by the best-performing models, particularly CNNs and InceptionTime, indicating their effectiveness in near-fall classification. The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. Nevertheless, our findings suggest the applicability of machine-learning-based STF prevention systems for workplace safety monitoring and, more generally, applications in fall mitigation. To further improve the accuracy and generalizability of the system, future research should investigate multimodal data integration and improved classification techniques.https://www.mdpi.com/1424-8220/25/5/1468sliptripfallnear fallmachine learningprevention |
| spellingShingle | Moritz Schneider Kevin Seeser-Reich Armin Fiedler Udo Frese Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique Sensors slip trip fall near fall machine learning prevention |
| title | Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique |
| title_full | Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique |
| title_fullStr | Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique |
| title_full_unstemmed | Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique |
| title_short | Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique |
| title_sort | enhancing slip trip and fall prevention real world near fall detection with advanced machine learning technique |
| topic | slip trip fall near fall machine learning prevention |
| url | https://www.mdpi.com/1424-8220/25/5/1468 |
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