Man Down Situation Detection Using an in-Ear Inertial Platform
Man down situations (MDS) are a health or life threatening situations occurring largely in high-risk industrial workplaces. MDS automatic detection is crucial for workers safety especially in isolated working conditions where workers could be unable to call for help on their own, either due to loss...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/5/1730 |
| _version_ | 1797415606937452544 |
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| author | Alex Guilbeault-Sauvé Bruno De Kelper Jérémie Voix |
| author_facet | Alex Guilbeault-Sauvé Bruno De Kelper Jérémie Voix |
| author_sort | Alex Guilbeault-Sauvé |
| collection | DOAJ |
| description | Man down situations (MDS) are a health or life threatening situations occurring largely in high-risk industrial workplaces. MDS automatic detection is crucial for workers safety especially in isolated working conditions where workers could be unable to call for help on their own, either due to loss of consciousness or an incapacitating injury. These solution must be reliable, robust, easy to use, but also have a low false-alarm rate, short response time and good ergonomics. This project aims to improve this technology by providing a global MDS definition according to a combination of three observable critical states based on characterization of body movement and orientation data from inertial measurements (accelerometer and gyroscope): the worker falls (F), worker immobility (I), the worker is down on the ground (D). The MDS detection strategy was established based on the detection of at least two distinct states, such as F-I, F-D or I-D, over a certain period of time. This strategy was tested using a large public database, revealing a significant reduction of the false alarms rate to 1.1%, reaching up to 99% accuracy. The proposed detection strategy was also incorporated into a digital earpiece, designed to address hearing protection issues, and validated according to an <i>in vivo</i> test procedure based on simulations of industrial workers normal activities and critical states. |
| first_indexed | 2024-03-09T05:52:03Z |
| format | Article |
| id | doaj.art-33021483011d4467abac513aa3ef1647 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:52:03Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-33021483011d4467abac513aa3ef16472023-12-03T12:16:44ZengMDPI AGSensors1424-82202021-03-01215173010.3390/s21051730Man Down Situation Detection Using an in-Ear Inertial PlatformAlex Guilbeault-Sauvé0Bruno De Kelper1Jérémie Voix2Université du Québec, École de technologie supérieure (ÉTS), Montréal, QC H3C 1K3, CanadaUniversité du Québec, École de technologie supérieure (ÉTS), Montréal, QC H3C 1K3, CanadaUniversité du Québec, École de technologie supérieure (ÉTS), Montréal, QC H3C 1K3, CanadaMan down situations (MDS) are a health or life threatening situations occurring largely in high-risk industrial workplaces. MDS automatic detection is crucial for workers safety especially in isolated working conditions where workers could be unable to call for help on their own, either due to loss of consciousness or an incapacitating injury. These solution must be reliable, robust, easy to use, but also have a low false-alarm rate, short response time and good ergonomics. This project aims to improve this technology by providing a global MDS definition according to a combination of three observable critical states based on characterization of body movement and orientation data from inertial measurements (accelerometer and gyroscope): the worker falls (F), worker immobility (I), the worker is down on the ground (D). The MDS detection strategy was established based on the detection of at least two distinct states, such as F-I, F-D or I-D, over a certain period of time. This strategy was tested using a large public database, revealing a significant reduction of the false alarms rate to 1.1%, reaching up to 99% accuracy. The proposed detection strategy was also incorporated into a digital earpiece, designed to address hearing protection issues, and validated according to an <i>in vivo</i> test procedure based on simulations of industrial workers normal activities and critical states.https://www.mdpi.com/1424-8220/21/5/1730man downfall detectionworker safetymonitoringinertial platformwearable sensors |
| spellingShingle | Alex Guilbeault-Sauvé Bruno De Kelper Jérémie Voix Man Down Situation Detection Using an in-Ear Inertial Platform Sensors man down fall detection worker safety monitoring inertial platform wearable sensors |
| title | Man Down Situation Detection Using an in-Ear Inertial Platform |
| title_full | Man Down Situation Detection Using an in-Ear Inertial Platform |
| title_fullStr | Man Down Situation Detection Using an in-Ear Inertial Platform |
| title_full_unstemmed | Man Down Situation Detection Using an in-Ear Inertial Platform |
| title_short | Man Down Situation Detection Using an in-Ear Inertial Platform |
| title_sort | man down situation detection using an in ear inertial platform |
| topic | man down fall detection worker safety monitoring inertial platform wearable sensors |
| url | https://www.mdpi.com/1424-8220/21/5/1730 |
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