Smart Wearables for Cardiac Autonomic Monitoring in Isolated, Confined and Extreme Environments: A Perspective from Field Research in Antarctica

Moraes, Michele Macedo; Mendes, Thiago Teixeira; Arantes, Rosa Maria Esteves

doi:10.3390/s21041303

Cited by 5 publications

(1 citation statement)

References 32 publications

(23 reference statements)

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“…Products retrofitted with a wide array of sensors improve the product’s capability to store, compute, and communicate data (chatty devices), for example, smartphones, tablets, smart wearables, water monitor, etc. [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device”

Lakoju

Ajienka

Khanesar

et al. 2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%