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Quantified Walk Project: Knitted Legging to Quantify a Walk

The project is to fabricate a pair of wearable leggings embedded with sensors, which can help identify a person’s a gait or walk. This is useful because if the legging walk identification is accurate enough they can be used for diagnoses of disease such as Parkinson’s or help athletes or dancers train their bodies based on the position of their limbs. The leggings are industrially knitted stretch cotton and polyester with integrated knitted conductive circuits, which connect to sensors that can measure the positions of the ankle, knee and hip of each leg relative to each other, as well as find the geo-spatial position of the person wearing the leggings. If this can be achieved, then sensor data measuring walking patterns may be done without requiring a Kinect type sensor, which can measure the patterns of walking in a fixed room, or area or alternatively the person would not have to strap on six sensors and coordinate them to monitor their walking patterns. Instead, the data can be centralized and collected via wireless transmission from the body of a person who is free to walk anywhere they could get a signal from a cell phone. The leggings use flexible, soft circuits integrated into a breathable cotton stretch material of our design that streams live data through a machine-learning algorithm.

Project type: 
Research project
Project duration: 
June, 2016 to February, 2018
Team: 

Principal Investigator (PI)
Felecia Davis
Assistant Professor, Stuckeman Center for Design Computing, School of Architecture and Landscape Architecture

Co PI
Associate Professor Conrad Tucker
PI & Site Director: NSF Center for Health Organization Transformation (CHOT),
Associate Professor: Engineering Design and Industrial and Manufacturing Engineering,
Affiliate Faculty: Computer Science and Engineering
Professor Delia Dumitrescu
Professor in Textile Design, the Swedish School of Textiles,
Director of the Smart Textiles Design Lab/ Director of Studies for ArcIntexETN

RESEARCH ASSISTANTS
Shokofeh Darbari, Master of Architecture Student, the Stuckeman School of Architecture and Landscape Architecture
Yi Dong, Master of Science Student, the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
Vernelle Noel, PhD Candidate, the Stuckeman Center for Design Computing, School of Architecture and Landscape Architecture