2012 16th International Symposium on Wearable Computers 2012
DOI: 10.1109/iswc.2012.35
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Garment Positioning and Drift in Garment-Integrated Wearable Sensing

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Cited by 15 publications
(10 citation statements)
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“…This is not just a simplification of the problem. Apparel manufacturing techniques used to integrate our garment-integrated stitched stretch sensors are affected by many imprecision variables such as sensor placement, stitch length or stitch structure, as we discuss in [11]. Given that the pants tested are skintight and do not move significantly during the mannequin run, considering the sensors located on the very front is a meaningful choice.…”
Section: Knee Bend Sensingmentioning
confidence: 99%
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“…This is not just a simplification of the problem. Apparel manufacturing techniques used to integrate our garment-integrated stitched stretch sensors are affected by many imprecision variables such as sensor placement, stitch length or stitch structure, as we discuss in [11]. Given that the pants tested are skintight and do not move significantly during the mannequin run, considering the sensors located on the very front is a meaningful choice.…”
Section: Knee Bend Sensingmentioning
confidence: 99%
“…A novel garment movement approach allowed characterization of the garment noise distribution on a pair of pants for waist, hip, thigh, calf, and ankle [2]; and quantification of errors directly related to garment wearability and body movement [3]. Truly garment-integrated sensors made out of apparel material (i.e., a conductive yarn) feeling and looking like normal stitches have also been presented [4] and sensitivity analyzed [5].…”
Section: Introductionmentioning
confidence: 99%
“…We tested 25 pairs of custom made Denim jeans: we used 5 fabrics different for weight and stiffness, and for each fabric 5 jeans sizes [1]. Our methodological approach can be summarized in the following three bullets:…”
Section: Garment Movement Measurement and Error Analysismentioning
confidence: 99%
“…In this manner, a spatial reference for each marker, at each time instance, is defined. For each time instant the average Euclidean distance over all run gait cycles of each marker was computed providing an average estimate of the garment ease (see [1], formula (1)). Such average Euclidean error was then averaged again for all time instants, in order to extract a unique statistic for the markers distribution (see [1], formula (2)).…”
Section: Garment Movement Measurement and Error Analysismentioning
confidence: 99%
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