
Textile sensors transform our everyday clothing into a means to track movement and biosignals in a completely unobtrusive way. One major hindrance to the adoption of “smart” clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: Connections between rigid and textile elements are often unreliable, and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fiber. We use a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fiber. Compared to optical motion capture, we achieve around five degrees error in reconstructing shoulder, elbow, and wrist joint angles.
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Textiles, Movement, distributed parameter systems, Machine Learning (cs.LG), Smart Materials, wearable technology, machine learning, fiber strain sensor, FOS: Electrical engineering, electronic engineering, information engineering, Physical and Materials Sciences, Electrical Engineering and Systems Science - Signal Processing, Software, Algorithms
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Textiles, Movement, distributed parameter systems, Machine Learning (cs.LG), Smart Materials, wearable technology, machine learning, fiber strain sensor, FOS: Electrical engineering, electronic engineering, information engineering, Physical and Materials Sciences, Electrical Engineering and Systems Science - Signal Processing, Software, Algorithms
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