One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi

Henao, Andres; Apparicio, Philippe; Maignan, David

doi:10.3390/s21175812

Cited by 5 publications

(11 citation statements)

References 38 publications

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“…However, these devices are not usually implemented in the same system and are very expensive. Therefore, recent studies have developed their own custom-made devices for data collection based on Arduino and Raspberry Pi, among others [ 5 , 6 , 7 , 8 ].…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the proposed video camera, the Garmin VIRB Elite video camera has been used because it was available at the research group and integrated a GPS unit. However, similar video cameras, such as the one used by Henao et al [ 8 ], could be installed. In that case, an external GPS device could be needed.…”

Section: Discussionmentioning

confidence: 99%

“…Used instrumented bicycles are usually equipped with expensive laser systems to collect distance and speed data, a global positioning system (GPS) to collect position and speed data, and video cameras [ 4 ]. In order to reduce costs, several studies have based their findings on custom-made devices using: (i) distance sensors connected to an Arduino Uno microcontroller card [ 5 ]; (ii) an ultrasonic sensor, a GPS receiver, (iii) a microcontroller and a data logger [ 6 ]; (iv) a device including an Adafruit microprocessor, GPS sensor, ultrasonic sensor, and lithium ion [ 7 ]; (v) a device based on Raspberry Pi, with a micro lidar as a distance sensor, a GPS, and a camera [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

Pérez-Zuriaga

Llopis-Castelló

Just-Martínez

et al. 2022

Sensors

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In recent years, cities are experiencing changes in the ways of moving around, increasing the use of micromobility vehicles. Bicycles are the most widespread transport mode and, therefore, cyclists’ behaviour, safety, and comfort have been widely studied. However, the use of other personal mobility vehicles is increasing, especially e-scooters, and related studies are scarce. This paper proposes a low-cost open-source data acquisition system to be installed on an e-scooter. This system is based on Raspberry Pi and allows collecting speed, acceleration, and position of the e-scooter, the lateral clearance during meeting and overtaking manoeuvres, and the vibrations experienced by the micromobility users when riding on a bike lane. The system has been evaluated and tested on a bike lane segment to ensure the accuracy and reliability of the collected data. As a result, the use of the proposed system allows highway engineers and urban mobility planners to analyse the behaviour, safety, and comfort of the users of e-scooters. Additionally, the system can be easily adapted to another micromobility vehicle and used to assess pavement condition and micromobility users’ riding comfort on a cycling network when the budget is limited.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

Pérez-Zuriaga

Llopis-Castelló

Just-Martínez

et al. 2022

Sensors

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“…Phenylhydrazine (PHZ) and ractopamine (RAC) (Shanghai Macklin Biochemical Co., Ltd., Shanghai, China) were diluted as a stock solution of 1000 ppm ( w / v ) by using 100% DMSO as a solvent and were further diluted using ddH 2 O. Zebrafish larvae at 48 hpf were treated with either 0.15 ppm ( w / v ) PHZ or 4 ppm RAC for 24 h and the corresponding blood flow alterations measured at 72 hpf. The control group was treated with 0.05% of DMSO as the solvent control and the final DMSO concentration in the treatment group was lower than 0.5%, which is still acceptable, according to the previous study [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos

Farhan

Saputra

Suryanto

et al. 2022

Biology

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The transparent appearance of fish embryos provides an excellent assessment feature for observing cardiovascular function in vivo. Previously, methods to conduct vascular function assessment were based on measuring blood-flow velocity using third-party software. In this study, we reported a simple software, free of costs and skills, called OpenBloodFlow, which can measure blood flow velocity and count blood cells in fish embryos for the first time. First, videos captured by high-speed CCD were processed for better image stabilization and contrast. Next, the optical flow of moving objects was extracted from the non-moving background in a frame-by-frame manner. Finally, blood flow velocity was calculated by the Gunner Farneback algorithm in Python. Data validation with zebrafish and medaka embryos in OpenBloodFlow was consistent with our previously published ImageJ-based method. We demonstrated consistent blood flow alterations by either OpenBloodFlow or ImageJ in the dorsal aorta of zebrafish embryos when exposed to either phenylhydrazine or ractopamine. In addition, we validated that OpenBloodFlow was able to conduct precise blood cell counting. In this study, we provide an easy and fully automatic programming for blood flow velocity calculation and blood cell counting that is useful for toxicology and pharmacology studies in fish.

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“…However, most IBs and instrumented e-scooters result in sensors taking up a lot of space on the bicycle, which can interfere with the natural user behavior. Thus, other researchers started to look into alternatives to develop DAS that can be customized to support the same sensors as IBs but are portable and can be easily mounted and un-mounted on bicycles for collecting naturalistic cycling data, especially LPDs [ 23 , 24 , 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

BSafe-360: An All-in-One Naturalistic Cycling Data Collection Tool

Bernardes

Özbay

2023

Sensors

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The popularity of bicycles as a mode of transportation has been steadily increasing. However, concerns about cyclist safety persist due to a need for comprehensive data. This data scarcity hinders accurate assessment of bicycle safety and identification of factors that contribute to the occurrence and severity of bicycle collisions in urban environments. This paper presents the development of the BSafe-360, a novel multi-sensor device designed as a data acquisition system (DAS) for collecting naturalistic cycling data, which provides a high granularity of cyclist behavior and interactions with other road users. For the hardware component, the BSafe-360 utilizes a Raspberry Pi microcomputer, a Global Positioning System (GPS) antenna and receiver, two ultrasonic sensors, an inertial measurement unit (IMU), and a real-time clock (RTC), which are all housed within a customized bicycle phone case. To handle the software aspect, BSafe-360 has two Python scripts that manage data processing and storage in both local and online databases. To demonstrate the capabilities of the device, we conducted a proof of concept experiment, collecting data for seven hours. In addition to utilizing the BSafe-360, we included data from CCTV and weather information in the data analysis step for verifying the occurrence of critical events, ensuring comprehensive coverage of all relevant information. The combination of sensors within a single device enables the collection of crucial data for bicycle safety studies, including bicycle trajectory, lateral passing distance (LPD), and cyclist behavior. Our findings show that the BSafe-360 is a promising tool for collecting naturalistic cycling data, facilitating a deeper understanding of bicycle safety and improving it. By effectively improving bicycle safety, numerous benefits can be realized, including the potential to reduce bicycle injuries and fatalities to zero in the near future.

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One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi

Cited by 5 publications

References 38 publications

Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos

BSafe-360: An All-in-One Naturalistic Cycling Data Collection Tool

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