Machine learning approach applied to human activity recognition – an application to the VanKasteren dataset

Reminders are a core component of many assistive technology systems and are aimed specifically at helping people with dementia function more independently by compensating for cognitive deficits. These technologies are often utilized for prospective reminding, reminiscence, or within coaching-based systems. Traditionally, reminders have taken the form of nontechnology based aids, such as diaries, notebooks, cue cards and white boards. This article is based on the use of machine learning algorithms for the detection of Alzheimer’s disease. In the experimentation, the LWL, SimpleLogistic, Logistic, MultiLayerPercepton and HiperPipes algorithms were used. The result showed that the LWL algorithm produced the following results: Accuracy 98.81%, Precission 100%, Recall 97.62% and F- measure 98.80%

• Autores: Ariza Colpas, Paola Patricia; Oñate-Bowen, Alvaro Agustín; Suarez-Brieva, Eydy del Carmen; Oviedo Carrascal, Ana Isabel; Urina Triana, Miguel; Piñeres Melo, Marlon Alberto; Butt Shariq, Aziz; COLLAZOS MORALES, CARLOS ANDRES; Ramayo González, Ramón Enrique
• Tipo de recurso: Article of journal
• Fecha de publicación: 2021
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/8696
• Acceso en línea: https://hdl.handle.net/11323/8696; https://doi.org/10.1016/j.procs.2021.07.070; https://repositorio.cuc.edu.co/
• Palabra clave: Machine learning; HARADL; Human activity recognition; Activity daily living; VanKasteren dataset
• Rights: openAccess
• License: CC0 1.0 Universal