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Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device”
To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of design and man...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347061/ https://www.ncbi.nlm.nih.gov/pubmed/34372228 http://dx.doi.org/10.3390/s21154991 |
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author | Lakoju, Mike Ajienka, Nemitari Khanesar, M. Ahmadieh Burnap, Pete Branson, David T. |
author_facet | Lakoju, Mike Ajienka, Nemitari Khanesar, M. Ahmadieh Burnap, Pete Branson, David T. |
author_sort | Lakoju, Mike |
collection | PubMed |
description | To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of design and manufacturing processes, aligned to the Industry 4.0 paradigm. In this paper, we propose a model that enables new forms of agile engineering product development via “chatty” products. Products relay their “experiences” from the consumer world back to designers and product engineers through the mediation provided by embedded sensors, IoT, and data-driven design tools. Our model aims to identify product “experiences” to support the insights into product use. To this end, we create an experiment to: (i) collect sensor data at 100 Hz sampling rate from a “Chatty device” (device with sensors) for six common everyday activities that drive produce experience: standing, walking, sitting, dropping and picking up of the device, placing the device stationary on a side table, and a vibrating surface; (ii) pre-process and manually label the product use activity data; (iii) compare a total of four Unsupervised Machine Learning models (three classic and the fuzzy C-means algorithm) for product use activity recognition for each unique sensor; and (iv) present and discuss our findings. The empirical results demonstrate the feasibility of applying unsupervised machine learning algorithms for clustering product use activity. The highest obtained F-measure is 0.87, and MCC of 0.84, when the Fuzzy C-means algorithm is applied for clustering, outperforming the other three algorithms applied. |
format | Online Article Text |
id | pubmed-8347061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83470612021-08-08 Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” Lakoju, Mike Ajienka, Nemitari Khanesar, M. Ahmadieh Burnap, Pete Branson, David T. Sensors (Basel) Article To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of design and manufacturing processes, aligned to the Industry 4.0 paradigm. In this paper, we propose a model that enables new forms of agile engineering product development via “chatty” products. Products relay their “experiences” from the consumer world back to designers and product engineers through the mediation provided by embedded sensors, IoT, and data-driven design tools. Our model aims to identify product “experiences” to support the insights into product use. To this end, we create an experiment to: (i) collect sensor data at 100 Hz sampling rate from a “Chatty device” (device with sensors) for six common everyday activities that drive produce experience: standing, walking, sitting, dropping and picking up of the device, placing the device stationary on a side table, and a vibrating surface; (ii) pre-process and manually label the product use activity data; (iii) compare a total of four Unsupervised Machine Learning models (three classic and the fuzzy C-means algorithm) for product use activity recognition for each unique sensor; and (iv) present and discuss our findings. The empirical results demonstrate the feasibility of applying unsupervised machine learning algorithms for clustering product use activity. The highest obtained F-measure is 0.87, and MCC of 0.84, when the Fuzzy C-means algorithm is applied for clustering, outperforming the other three algorithms applied. MDPI 2021-07-22 /pmc/articles/PMC8347061/ /pubmed/34372228 http://dx.doi.org/10.3390/s21154991 Text en © 2021 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 Lakoju, Mike Ajienka, Nemitari Khanesar, M. Ahmadieh Burnap, Pete Branson, David T. Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” |
title | Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” |
title_full | Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” |
title_fullStr | Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” |
title_full_unstemmed | Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” |
title_short | Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device” |
title_sort | unsupervised learning for product use activity recognition: an exploratory study of a “chatty device” |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347061/ https://www.ncbi.nlm.nih.gov/pubmed/34372228 http://dx.doi.org/10.3390/s21154991 |
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