Machine Learning approach applied to Human Activity Recognition - An application to the VanKasteren dataset

Ariza Colpas Paola, Oñate Bowen Alvaro Agustín, Suarez Brieva Eydy Del Carmen, Oviedo Carrascal Ana, Urina Triana Miguel, Piñeres Melo Marlon, Butt Shariq Aziz, Carlos Andrés Collazos Morales, Ramayo González Ramón Enrique

Research output: Contribution to journalConference and proceedingspeer-review

2 Scopus citations

Abstract

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%.

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.. All rights reserved.

Keywords

  • ADL
  • Activity Daily Living
  • HAR
  • Human Activity Recognition
  • Machine learning
  • VanKasteren Dataset

Types Minciencias

  • Outreach articles

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