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I-Corps: Embedding fabric-based sensors into apparel

I-Corps: Embedding fabric-based sensors into apparel
I-Corps:将基于织物的传感器嵌入服装中
批准号:
1931623
负责人:
Roman Lubynsky
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-01 至 2022-08-31

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中文摘要
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英文摘要
The broader impact/commercial potential of this I-Corps project is to leverage fabric-based technologies to create functional apparel that can track body movements and in return analyze exercise technique, track performance, and identify likelihood of injuries. The global sports apparel market is currently a $200 billion dollar industry, yet the focus in apparel technologies have been on incremental improvements on material properties and optimizing manufacturability. Therefore, consumers rely on other technologies to inform them of their fitness levels which has corresponded to a dramatic rise in smart watch sales, doubling every year since 2015. Even so, athletes still rely on coaching, personal trainers, and physical therapy for exercise guidance. There is no single platform in which consumers can access data on their health given the disparate fitness markets. The technology pursued in this I-Corps project aims to combine these value propositions directly into users' clothing by leveraging new sewing technologies to embed fabric-based electronics for motion sensing.This I-Corps project aims to advance the scope and application of wearable technologies by re-inventing traditional sewing methodologies. Using a combination of new conductive materials and fibers, electrically active threads can be sewn into clothing using tradition sewing machines without much disturbance in apparel manufacturability. Over lays and patterning of different conductive fibers can produce various functional fabric-based sensors such as pressure, bend, or stretch responsive sensors. These sensing mechanisms can then be harnessed to directly measure the movement of the clothed joint or body part. Currently a custom algorithm is being developed to analyze these new datasets in order to extract relevant metrics such as exercise technique, effort level, and eventually machine learn likelihood of injuries or provide user-specific feedback.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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