A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters
Outdoor air pollution has been found to have a significant adverse effect on health. When the authors attempted to monitor air quality that cyclists or e-scooter users’ breath during commuting in different locations for health and safety analysis, it was found that the existence of internal combusti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123005307 |
| _version_ | 1797372969018720256 |
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| author | Amin Al-Habaibeh Bubaker Shakmak Matthew Watkins Hyunjae Daniel Shin |
| author_facet | Amin Al-Habaibeh Bubaker Shakmak Matthew Watkins Hyunjae Daniel Shin |
| author_sort | Amin Al-Habaibeh |
| collection | DOAJ |
| description | Outdoor air pollution has been found to have a significant adverse effect on health. When the authors attempted to monitor air quality that cyclists or e-scooter users’ breath during commuting in different locations for health and safety analysis, it was found that the existence of internal combustion engine (ICE) cars has a significant effect on the pollution levels and the monitoring process. To comprehensively study the effect of cars and traffic on air quality that cyclists and e-scooters users experience, a low-cost and reliable system was needed to detect the proximity of cars that have diesel or petrol engines. Video cameras can be used to visually detect vehicles, but in the modern age with the existence of many electric and hybrid vehicles and the need to reduce the cost of instrumentation, there was a need to determine the passing of vehicles near e-scooter and bike users from the combined engine and tires sounds.To address this issue, this study suggests a novel approach of using sound waves of internal combustion engines and tire sounds during the passing of cars, combined with AI techniques (neural networks), to detect the proximity of cars from cyclists and e-scooter users. Audio-visual data was collected using Go-Pro cameras in order to combine the data with GPS location and pollution levels. Geographical data maps were produced to demonstrate the density of cars that cyclists encounter when on or near the road. This method will enable air quality monitoring research to detect the existence of ICE cars for future correlation with measured pollution levels. The proposed method allows for: • The automated selection of sensitive features from sound waves to detect vehicles. • Low-cost hardware which is independent of orientation that can be integrated with other air quality and GPS sensors. • The successful application of sensor fusion and neural networks. |
| first_indexed | 2024-03-08T18:43:28Z |
| format | Article |
| id | doaj.art-ef8acb6d4af24bb1bd2c1c401f6bb5c6 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-08T18:43:28Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-ef8acb6d4af24bb1bd2c1c401f6bb5c62023-12-29T04:45:23ZengElsevierMethodsX2215-01612024-06-0112102534A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scootersAmin Al-Habaibeh0Bubaker Shakmak1Matthew Watkins2Hyunjae Daniel Shin3Product Innovation Centre, Department of Product Design, Nottingham Trent University, UK; Corresponding author.Product Innovation Centre, Department of Product Design, Nottingham Trent University, UKDepartment of Engineering, Nottingham Trent University, UKDepartment of Human and Environment Design, Yonsei University, South KoreaOutdoor air pollution has been found to have a significant adverse effect on health. When the authors attempted to monitor air quality that cyclists or e-scooter users’ breath during commuting in different locations for health and safety analysis, it was found that the existence of internal combustion engine (ICE) cars has a significant effect on the pollution levels and the monitoring process. To comprehensively study the effect of cars and traffic on air quality that cyclists and e-scooters users experience, a low-cost and reliable system was needed to detect the proximity of cars that have diesel or petrol engines. Video cameras can be used to visually detect vehicles, but in the modern age with the existence of many electric and hybrid vehicles and the need to reduce the cost of instrumentation, there was a need to determine the passing of vehicles near e-scooter and bike users from the combined engine and tires sounds.To address this issue, this study suggests a novel approach of using sound waves of internal combustion engines and tire sounds during the passing of cars, combined with AI techniques (neural networks), to detect the proximity of cars from cyclists and e-scooter users. Audio-visual data was collected using Go-Pro cameras in order to combine the data with GPS location and pollution levels. Geographical data maps were produced to demonstrate the density of cars that cyclists encounter when on or near the road. This method will enable air quality monitoring research to detect the existence of ICE cars for future correlation with measured pollution levels. The proposed method allows for: • The automated selection of sensitive features from sound waves to detect vehicles. • Low-cost hardware which is independent of orientation that can be integrated with other air quality and GPS sensors. • The successful application of sensor fusion and neural networks.http://www.sciencedirect.com/science/article/pii/S2215016123005307Automated Sensor and Signal Processing Selection (ASPS) for Safety and Air Quality Monitoring ASPS-SAQM |
| spellingShingle | Amin Al-Habaibeh Bubaker Shakmak Matthew Watkins Hyunjae Daniel Shin A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters MethodsX Automated Sensor and Signal Processing Selection (ASPS) for Safety and Air Quality Monitoring ASPS-SAQM |
| title | A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters |
| title_full | A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters |
| title_fullStr | A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters |
| title_full_unstemmed | A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters |
| title_short | A novel method of using sound waves and artificial intelligence for the detection of vehicle's proximity from cyclists and E-scooters |
| title_sort | novel method of using sound waves and artificial intelligence for the detection of vehicle s proximity from cyclists and e scooters |
| topic | Automated Sensor and Signal Processing Selection (ASPS) for Safety and Air Quality Monitoring ASPS-SAQM |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123005307 |
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