Automatic estimation of pose and falls in videos using computer vision model

Human pose estimation is defined as the process of locating joints of a person or a crowd given an image or video. Currently, this estimation is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation is not an easy task as it requires experts to manually assess the person’s position by using specialized equipment such as e-health devices (watches, bands, handles), markers, and high-cost cameras to monitor a limited scenario. The main goal of this article is to evaluate a marker-less low-cost computer vision system to get the automatic estimation of poses and fall detection on video by calculating the person’s joint angle with a high level of adaptability to any space. The proposed model is the first step in the construction of a tool that allows monitoring and generating alerts to prevent falls at home and clinical settings.

• Autores: Calvache, Daniela; Bernal, Hernán; Guarín, Juan F.; Aguía, Karen; Orjuela Cañón, Álvaro D.; Perdomo, Oscar J.
• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Escuela Colombiana de Ingeniería Julio Garavito
• Repositorio: Repositorio Institucional ECI
• Idioma: eng
• OAI Identifier: oai:repositorio.escuelaing.edu.co:001/3292
• Acceso en línea: https://repositorio.escuelaing.edu.co/handle/001/3292; https://repositorio.escuelaing.edu.co/
• Palabra clave: Tecnología médica; Medical technology; Monitoreo del paciente - Equipo y accesorios; Patient monitoring - Equipment and supplies; Trastornos de la postura; Posture disorders; Caídas (Accidentes); Falls (Accidents); Aprendizaje profundo; Estimación de la postura humana; Detección de caídas; Visión por computadora; Procesamiento de video digital; Deep learning; Human pose estimation; Fall detection; Computer vision; Digital video processing
• Rights: closedAccess
• License: http://purl.org/coar/access_right/c_14cb