Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution
Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/10/3346 |
| _version_ | 1827692809850716160 |
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| author | Mingming Zhao Georges Beurier Hongyan Wang Xuguang Wang |
| author_facet | Mingming Zhao Georges Beurier Hongyan Wang Xuguang Wang |
| author_sort | Mingming Zhao |
| collection | DOAJ |
| description | Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures, and three typical right foot postures. The features from lower-resolution mapping were compared with those from high-resolution Xsensor pressure mats on the backrest and seat pan. We applied five different supervised machine-learning techniques to recognize the postures of each body part and used leave-one-out cross-validation to evaluate their performance. A uniform sampling method was used to reduce number of pressure sensors, and five new layouts were tested by using the best classifier. Results showed that the random forest classifier outperformed the other classifiers with an average classification accuracy of 86% using the original pressure mats and 85% when only 8% of the pressure sensors were available. This study demonstrates the feasibility of using fewer pressure sensors for driver posture monitoring and suggests research directions for better sensor designs. |
| first_indexed | 2024-03-10T11:31:08Z |
| format | Article |
| id | doaj.art-df9dd30420cc4fdea5676a5516499ac8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:31:08Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-df9dd30420cc4fdea5676a5516499ac82023-11-21T19:16:14ZengMDPI AGSensors1424-82202021-05-012110334610.3390/s21103346Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower ResolutionMingming Zhao0Georges Beurier1Hongyan Wang2Xuguang Wang3School of Automotive Studies, Tongji University, Shanghai 201804, ChinaUniv Lyon, Univ Gustave Eiffel, Université Claude Bernard Lyon 1, LBMC UMR_T9406, F69622 Lyon, FranceSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaUniv Lyon, Univ Gustave Eiffel, Université Claude Bernard Lyon 1, LBMC UMR_T9406, F69622 Lyon, FrancePressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures, and three typical right foot postures. The features from lower-resolution mapping were compared with those from high-resolution Xsensor pressure mats on the backrest and seat pan. We applied five different supervised machine-learning techniques to recognize the postures of each body part and used leave-one-out cross-validation to evaluate their performance. A uniform sampling method was used to reduce number of pressure sensors, and five new layouts were tested by using the best classifier. Results showed that the random forest classifier outperformed the other classifiers with an average classification accuracy of 86% using the original pressure mats and 85% when only 8% of the pressure sensors were available. This study demonstrates the feasibility of using fewer pressure sensors for driver posture monitoring and suggests research directions for better sensor designs.https://www.mdpi.com/1424-8220/21/10/3346driver posture monitoringpressure measurementsensor layoutmachine learning |
| spellingShingle | Mingming Zhao Georges Beurier Hongyan Wang Xuguang Wang Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution Sensors driver posture monitoring pressure measurement sensor layout machine learning |
| title | Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution |
| title_full | Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution |
| title_fullStr | Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution |
| title_full_unstemmed | Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution |
| title_short | Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution |
| title_sort | exploration of driver posture monitoring using pressure sensors with lower resolution |
| topic | driver posture monitoring pressure measurement sensor layout machine learning |
| url | https://www.mdpi.com/1424-8220/21/10/3346 |
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