Optical Marker-Based Motion Capture of the Human Spine: A Scoping Review of Study Design and Outcomes.

Biomechanical analysis of the human spine is crucial to understanding injury patterns. Motion capture technology has gained attention due to its non-invasive nature. Nevertheless, traditional motion capture studies consider the spine a single rigid segment, although its alignment changes during movement. Moreover, guidelines that indicate where markers should be placed for a specific exercise do not exist. This study aims to review the methods used to assess spine biomechanics using motion capture systems to determine the marker sets used, the protocols used, the resulting parameters, the analysed activities, and the characteristics of the studied populations. PRISMA guidelines were used to perform a Scoping Review using SCOPUS and Web of Science databases. Fifty-six journal and conference articles from 1997 to 2023 were considered for the analysis. This review showed that Plug-in-Gait is the most used marker set. The lumbar spine is the segment that generates the most interest because of its high mobility and function as a weight supporter. Furthermore, angular position and velocity are the most common outcomes when studying the spine. Walking, standing, and range of movement were the most studied activities compared to sports and work-related activities. Male and female participants were recruited similarly across all included articles. This review presents the motion capture techniques and measurement outcomes of biomechanical studies of the human spine, to help standardize the field. This work also discusses trends in marker sets, study outcomes, studied segments and segmentation approaches.

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning.

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.