Measuring cycle riding comfort in Southampton using an instrumented bicycle

The increased environmental awareness and the rising fuel costs make bicycles a more and more attractive mode of travel for short journeys. Considering the future prospect of this mode of transportation and the great advantages that it offers in terms of space consumption, health and environmental sustainability, several city authorities worldwide are presently undertaking schemes aiming at improving cycling infrastructure. The aim of the present study is to monitor the impact of such schemes on the riding comfort of cyclists, as expressed by the, usually lower, quantity and magnitude of vibrations occurring as a result of cycling over pavement defects. Millbrook Road East in the western edge of the city center of Southampton is used as a case study, where vibration measurements are taken by means of an instrumented bicycle during periods before and after a redevelopment scheme involving the resurfacing of the road pavement. The results show a clear overall improvement in cycling comfort post-redevelopment, with statistically significant reductions in both the number of high severity vibrations and of their magnitude in "typical" cycling trips taken on the road. However, instances of finishing "snags" in some parts of the surface appear to introduce new minor defects (e.g. around manholes) that are not visible to the naked eye, and these still have some negative effect on the riding experience. Moreover, the study highlights the detrimental impact that widespread pavement defects can have on riding comfort, which affect cyclists of all ages, abilities and styles

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

[EN] 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.This research was funded by MCIN/AEI/10.13039/501100011033, grant number PID2019-111744RB-I00.Pérez Zuriaga, AM.; Llopis-Castelló, D.; Just-Martínez, V.; Fonseca-Cabrera, AS.; Alonso-Troyano, C.; García García, A. (2022). Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research. Sensors. 22(21):1-18. https://doi.org/10.3390/s22218215118222

Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians’ physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model’s performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions

Continuous, response-based road roughness measurements utilising data harvested from telematics device sensors

Roads need to be continuously monitored and maintained to ensure that they offer a driving surface that effectively address the safety and comfort needs of road users. Well maintained roads are also vital for freight transport companies, assisting with minimising vehicle and goods damage that can occur during transportation. Vehicle telematics is technology that is advancing in terms of complexity, diversity and data volume. Hundreds of thousands of these devices are installed in vehicles throughout South Africa and worldwide. The technology is predominantly used for the recovery of hijacked or stolen vehicles, driver behavioural insurance and monitoring and management of vehicle fleets. This paper demonstrates that vehicle telematics provides additional potential in terms of estimating road roughness (similar to a Class 3 level). This is demonstrated by utilising the global positioning system (time, latitude, longitude and speed) and vertical (z) acceleration data harvested from telematics device sensors. Road roughness data obtained from telematics technology can be used as ‘screening’ devices to measure road roughness on a real-time basis. It can also help close the gap between Class 1, Class 2 and Class 4 road roughness measurements.The University of Pretoriahttp://www.tandfonline.com/loi/gpav202019-06-18hj2019Civil Engineerin

Evaluation of accelerometric and cycling cadence data for motion monitoring

Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well. AuthorResearch through the Development of Advanced Computational Algorithms for Evaluating Post-Surgery Rehabilitation [LTAIN19007]; National Sustainability Programme of the Ministry of Education, Youth and Sports of the Czech Republic [LO1303 (MSMT-7778/2014)]; Ethics commission, Neurocentre Caregroup, Center for Neurological Care in Rychnov nad Kneznou, Czech RepublicMinisterstvo Školství, Mládeže a Tělovýchovy, MŠMT: LO1303, MSMT-7778/201

Theoretical Experiments on Road Profile Data Analysis using Filter Combinations

Identification of road profiles is needed to provide the input of automotive simulation and endurance testing. The analysis with estimation methods is mostly done to identify road profiles. The main goal of analysis methods is to obtain the data of vertical displacements due to road profile measurement. The acceleration data is obtained from measuring road profile by using 4 sensors of accelerometer placed on each car wheel.  The measuring data is converted to be vertical displacement data by using a "double integrator", however, it is not easy to get accurate results since the signal obtained carries a lot of noise and it is necessary to design the right filter reduce the noise. In this study, the signal filtering methods reducing the noise were used Fast Fourier Transform (FFT) and Kalman Filter (KF) combination. Experiments were carried out by combining Fast Fourier Transform and Kalman Filters using an input signal with unit (volt) in the time domain. In addition, this research focused on preparing the survey data that has been obtained by eliminating the noise to convert becoming the displacement input data for providing the loads of automotive simulation testing

A Driver, Vehicle and Road Safety System Using Smartphones

As vehicle manufacturers continue to increase their emphasis on safety with advanced driver assistance systems (ADAS), I propose a ubiquitous device that is able to analyze and advise on safety conditions. Mobile smartphones are increasing in popularity among younger generations with an estimated 64% of 25-34 year olds already using one in their daily lives. with over 10 million car accidents reported in the United States each year, car manufacturers have shifted their focus of a passive approach (airbags) to more active by adding features associated with ADAS (lane departure warnings). However, vehicles manufactured with these sensors are not economically priced while older vehicles might only have passive safety features. Given its accessibility and portability, I target a mobile smartphone as a device to compliment ADAS that can bring a driver assist to any vehicle without regards for any on-vehicle communication system requirements. I use the 3-axis accelerometer of multiple Android based smartphone to record and analyze various safety factors which can influence a driver while operating a vehicle. These influences with respect to the driver, vehicle and road are lane change maneuvers, vehicular comfort and road conditions. Each factor could potentially be hazardous to the health of the driver, neighboring public, and automobile and is therefore analyzed thoroughly achieving 85.60% and 89.89% classification accuracy for identifying road anomalies and lane changes, respectively. Effective use of this data can educate a potentially dangerous driver on how to operate a vehicle safely and efficiently. with real time analysis and auditory alerts of these factors, I hope to increase a driver's overall awareness to maximize safety

SmarterRoutes : data-driven road complexity estimation for level-of-detail adaptation of navigation services

SmarterRoutes aims to improve navigational services and make them more dynamic and personalised by data-driven and environmentally-aware road scene complexity estimation. SmarterRoutes divides complexity into two subtypes: perceived and descriptive complexity. In the SmarterRoutes architecture, the overall road scene complexity is indicated by combining and merging parameters from both types of complexity. Descriptive complexity is derived from geospatial data sources, traffic data and sensor analysis. The architecture is currently using OpenStreetMap (OSM) tag analysis, Meten-In-Vlaanderen (MIV) derived traffic info and the Alaro weather model of the Royal Meteorological Institute of Belgium (RMI) as descriptive complexity indicators. For the perceived complexity an image based complexity estimation mechanism is presented. This image based Densenet Convolutional Neural Network (CNN) uses Street View images as input and was pretrained on buildings with Bag-of-Words and Structure-from-motion features. The model calculates an image descriptor allowing comparison of images by calculation of the Euclidean distances between descriptors. SmarterRoutes extends this model by additional hand-labelled rankings of road scene images to predict visual road complexity. The reuse of an existing pretrained model with an additional ranking mechanism produces results corresponding with subjective assessments of end-users. Finally, the global complexity mechanism combines the aforementioned sub-mechanisms and produces a service which should facilitate user-centred context-aware navigation by intelligent data selection and/or omission based on SmarterRoutes’ complexity input

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll