Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
| Autor: | Oviedo-Carrascal Ana, Oñate-Bowen Alvaro Agustín, Ramayo González Ramón Enrique, Pineres-Melo Marlon, Carlos Andrés Collazos Morales, Butt Shariq Aziz, Ariza-Colpas Paola, Suarez-Brieva Eydy del Carmen, Urina Triana Miguel |
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| Rok vydání: | 2021 |
| Předmět: |
ADL
Computer science media_common.quotation_subject VanKasteren Dataset Machine learning computer.software_genre Coaching Activity recognition Reminiscence HARADL medicine Dementia Function (engineering) General Environmental Science media_common Activity Daily Living Recall business.industry Human Activity Recognition Core component Cognition medicine.disease HAR General Earth and Planetary Sciences Artificial intelligence business computer |
| Zdroj: | FNC/MobiSPC Procedia Computer Science Vol. 191, (2021) REDICUC-Repositorio CUC Corporación Universidad de la Costa instacron:Corporación Universidad de la Costa Repositorio Digital USB Universidad Simón Bolívar instacron:Universidad Simón Bolívar |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.07.070 |
| Popis: | 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% |
| Databáze: | OpenAIRE |
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