首页|Study Results from Pontificia University Javeriana Broaden Understanding of Mach ine Learning (Classification of Activities of Daily Living for Older Adults Usin g Machine Learning and Fixed Time Windowing Technique)
Study Results from Pontificia University Javeriana Broaden Understanding of Mach ine Learning (Classification of Activities of Daily Living for Older Adults Usin g Machine Learning and Fixed Time Windowing Technique)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting from Bogota, Colombia, by N ewsRx journalists, research stated, "The classification of activities of daily l iving (ADLs) in the home of older adults makes it possible to identify risk situ ations and changes in behavior that may be associated with some type of problem. This information allows caregivers and health professionals to take action when these types of situations are detected." Financial support for this research came from Pontificia Universidad Javeriana. The news correspondents obtained a quote from the research from Pontificia Unive rsity Javeriana, "Although many machine learning classification techniques have been proposed, the effectiveness of the solution in a real-world context remains unclear in most cases due to the large number of sensors required, the type of sensors used which may pose privacy issues, and the assumption of considering on ly segmented sensor events for each activity before training the models. This ar ticle presents an evaluation of different machine learning techniques using fixe d time windows to extract spatiotemporal features and classify ten human activit ies in a real smart home with unobtrusive sensors using the Aruba CASAS dataset. The three classification techniques that achieved better performance were rando m forest, XGBoost, and support vector machine (SVM), achieving an accuracy of 97 % with our best model, outperforming other approaches from the lit erature that were using the same dataset under similar conditions."
BogotaColombiaSouth AmericaActivit ies of Daily LivingCyborgsEmerging TechnologiesHealth and MedicineMachin e LearningRehabilitationPontificia University Javeriana