Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.

This study investigates a road surface monitoring system that integrates machine learning (ML) classification ensemble models to classify road surface elements based on raw acceleration and angular velocity data. The models aim to categorize roads into five distinct types, including speed bumps, patches, potholes-manholes, and both even and uneven road surfaces. To improve the performance of the models, a semi-supervised training methodology is employed, utilizing pseudo-labels obtained from the predictions of the original system for model retraining. The effectiveness of the ensemble models is evaluated using the F1-score metric, with the binary ensemble achieving an F1-score of 0.967, and the multiclass model obtaining an F1-score of 0.854. Testing on independent data confirms an overall F1-score of 0.877 for the classification of the five surface classes, representing an improvement over the original F1-score of 0.827. Further research is warranted to automate the semi-supervised training process, taking into account confidence thresholds and efficient retraining methods. This study contributes to the field of road surface monitoring by demonstrating the potential of semi-supervised training to enhance the accuracy and performance of ML ensemble models in classifying road surface conditions.

The design process of a chassis for a high-efficiency vehicle involves considering several unique parameters due to its distinct operational needs. The constraints specified in the official regulations for the Shell Eco-Marathon prototype battery electric class could ostensibly limit design innovations. However, this paper introduces a methodological approach employing Machine Learning, facilitated by Large Language Models, which substantially broadens the spectrum of conceptual design possibilities. Further, the widely recognized technique of Computer-Aided Design, coupled with Generative Design, is leveraged to optimize specific sections of the chassis structure. The final stage of this study involves rigorous testing of the designed chassis to assess its mechanical performance, guided by the stringent benchmarks set by the competition.

Health services continually confront a range of challenges that are intricately tied to the geographical and economic conditions of each region, leading to the deterioration, or even absence, of medical care. E-Health offers a solution by enabling connections with healthcare providers for remote assistance through Information and Communication Technologies (ICTs), irrespective of the geographical constraints faced by individual patients. The current study proposes a four-stage methodology for designing a hand exoskeleton system aimed at patient rehabilitation. This methodology is substantiated through a practical case study, which demonstrates the development of a computer vision system, the assessment and engineering model called Twisted String Actuation System (TSAS), and the system's simulated integration within a virtual environment to validate the final design. The development of this methodology sets the stage for the introduction of novel remote rehabilitation tools, thereby providing individuals with limited resources or geographical barriers easier access to medical assistance.