Unsupervised learning for product use activity recognition: an exploratory study of a “Chatty Device”

Lakoju, Mike, Ajienka, Nemitari, Khanesar, Mojtaba, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X and Branson, David 2021. Unsupervised learning for product use activity recognition: an exploratory study of a “Chatty Device”. Sensors 21 (15) , 4991. 10.3390/s21154991

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Publisher:	MDPI
ISSN:	1424-8220
Funders:	EPSRC
Date of First Compliant Deposit:	26 November 2021
Date of Acceptance:	19 July 2021
Last Modified:	04 May 2023 13:29
URI:	https://orca.cardiff.ac.uk/id/eprint/144842

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