CRIS
Permanent URI for this communityhttps://cris.ute.edu.ec/handle/123456789/1
Browse
2 results
Search Results
Now showing 1 - 2 of 2
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Random Forest modeling of bipolar affective disorder in Ecuador(AG Editor (Argentina), 2025-07-31); Zilma Diago AlfesBipolar affective disorder is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity of the diagnosis of bipolar affective disorder due to the overlapping of its symptoms with other mood disorders led researchers and doctors to search for new and advanced techniques for the precise detection of bipolar affection disorder. One of these methods is the use of advanced machine learning algorithms under a statistical methodology for building logistical regression models, Random Forest. Support vector machines, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, with 146 data collected from the psychiatric services affiliated with the mental health system of Ecuador. At the inferential level, the results suggest that the implementation of automatic algorithms based on the different methodologies for building models enables the successful prediction or classification of individuals with bipolar affective disorders in Ecuador compared to controlled patients who do not profile under this pathological picture. It is the best Random Forest statistical model (89.35 %) that dictates the best performance metrics compared to the Gradient Boosting model. The evolution of the overall prevalence of bipolar affective disorders in Ecuador over the past 22 years has increased by a small differential. However, from 2020 to 2022, there has been a considerable increase in the percentage prevalence of cases of bipolar affective disorders in Ecuador. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Comprehensive Review of Vision-Based Sensor Systems for Human Gait AnalysisAnalysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].