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

doi:10.1016/j.procs.2021.07.070

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

Information and Communication Technologies

Research output: Contribution to scientific journal › Conference and proceedings › peer-review

3 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%.

Original language	English
Pages (from-to)	367-372
Number of pages	6
Journal	Procedia Computer Science
Volume	191
DOIs	https://doi.org/10.1016/j.procs.2021.07.070
State	Published - 2021
Event	18th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021 - Leuven, Belgium Duration: 9 Aug 2021 → 12 Aug 2021

Bibliographical note

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Types Minciencias

Science communication articles

Access to Document

10.1016/j.procs.2021.07.070

Cite this

Paola, A. C., Alvaro Agustín, O. B., Eydy Del Carmen, S. B., Ana, O. C., Miguel, U. T., Marlon, P. M., Shariq Aziz, B., Collazos Morales, C. A., & Ramón Enrique, R. G. (2021). Machine Learning approach applied to Human Activity Recognition - An application to the VanKasteren dataset. Procedia Computer Science, 191, 367-372. https://doi.org/10.1016/j.procs.2021.07.070

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title = "Machine Learning approach applied to Human Activity Recognition - An application to the VanKasteren dataset",

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\%.",

keywords = "ADL, Activity Daily Living, HAR, Human Activity Recognition, Machine learning, VanKasteren Dataset",

author = "Paola, \{Ariza Colpas\} and \{Alvaro Agust{\'i}n\}, \{O{\~n}ate Bowen\} and \{Eydy Del Carmen\}, \{Suarez Brieva\} and Ana, \{Oviedo Carrascal\} and Miguel, \{Urina Triana\} and Marlon, \{Pi{\~n}eres Melo\} and \{Shariq Aziz\}, Butt and \{Collazos Morales\}, \{Carlos Andr{\'e}s\} and \{Ram{\'o}n Enrique\}, \{Ramayo Gonz{\'a}lez\}",

note = "Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.. All rights reserved.; 18th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021 ; Conference date: 09-08-2021 Through 12-08-2021",

year = "2021",

doi = "10.1016/j.procs.2021.07.070",

language = "Ingl{\'e}s",

volume = "191",

pages = "367--372",

journal = "Procedia Computer Science",

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T1 - Machine Learning approach applied to Human Activity Recognition - An application to the VanKasteren dataset

AU - Paola, Ariza Colpas

AU - Alvaro Agustín, Oñate Bowen

AU - Eydy Del Carmen, Suarez Brieva

AU - Ana, Oviedo Carrascal

AU - Miguel, Urina Triana

AU - Marlon, Piñeres Melo

AU - Shariq Aziz, Butt

AU - Collazos Morales, Carlos Andrés

AU - Ramón Enrique, Ramayo González

PY - 2021

Y1 - 2021

N2 - 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%.

AB - 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%.

KW - ADL

KW - Activity Daily Living

KW - HAR

KW - Human Activity Recognition

KW - Machine learning

KW - VanKasteren Dataset

UR - https://www.scopus.com/pages/publications/85115235282

U2 - 10.1016/j.procs.2021.07.070

DO - 10.1016/j.procs.2021.07.070

M3 - Ponencia publicada en las memorias del evento con ISSN

AN - SCOPUS:85115235282

SN - 1877-0509

VL - 191

SP - 367

EP - 372

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 18th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

Y2 - 9 August 2021 through 12 August 2021

ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

Types Minciencias

Access to Document

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Cite this