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Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches conside...

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Autores principales: Chifu, Viorica Rozina, Pop, Cristina Bianca, Rancea, Alexandru Miron, Morar, Andrei, Cioara, Tudor, Antal, Marcel, Anghel, Ionut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269491/
https://www.ncbi.nlm.nih.gov/pubmed/35808297
http://dx.doi.org/10.3390/s22134803
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author Chifu, Viorica Rozina
Pop, Cristina Bianca
Rancea, Alexandru Miron
Morar, Andrei
Cioara, Tudor
Antal, Marcel
Anghel, Ionut
author_facet Chifu, Viorica Rozina
Pop, Cristina Bianca
Rancea, Alexandru Miron
Morar, Andrei
Cioara, Tudor
Antal, Marcel
Anghel, Ionut
author_sort Chifu, Viorica Rozina
collection PubMed
description The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.
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spelling pubmed-92694912022-07-09 Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns Chifu, Viorica Rozina Pop, Cristina Bianca Rancea, Alexandru Miron Morar, Andrei Cioara, Tudor Antal, Marcel Anghel, Ionut Sensors (Basel) Article The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%. MDPI 2022-06-25 /pmc/articles/PMC9269491/ /pubmed/35808297 http://dx.doi.org/10.3390/s22134803 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chifu, Viorica Rozina
Pop, Cristina Bianca
Rancea, Alexandru Miron
Morar, Andrei
Cioara, Tudor
Antal, Marcel
Anghel, Ionut
Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
title Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
title_full Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
title_fullStr Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
title_full_unstemmed Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
title_short Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
title_sort deep learning, mining, and collaborative clustering to identify flexible daily activities patterns
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269491/
https://www.ncbi.nlm.nih.gov/pubmed/35808297
http://dx.doi.org/10.3390/s22134803
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