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Comparing clothing-mounted sensors with wearable sensors for movement analysis and activity classification

Jayasinghe, U., Harwin, W. S. and Hwang, F. (2019) Comparing clothing-mounted sensors with wearable sensors for movement analysis and activity classification. Sensors, 20 (1). 82. ISSN 1424-8220

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To link to this item DOI: 10.3390/s20010082

Abstract/Summary

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:88124
Uncontrolled Keywords:actigraph, body worn sensors, clothing sensors, cross correlation analysis, healthcare movement sensing, wearable devices
Additional Information:All data used in the paper is available at 10.5281/zenodo.3597391
Publisher:MDPI

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