Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
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Fecha
2021
Autores
Ariza-Colpas, Paola
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Oviedo-Carrascal, Ana
Urina Triana, Miguel
Piñeres-Melo, Marlon
Butt, Shariq Aziz,
Collazos Morales, Carlos Andrés
Ramayo González, Ramón Enrique
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Elsevier
Resumen
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%
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Machine learning, HAR, ADL, Human Activity Recognition, Activity Daily Living, VanKasteren Dataset