1‐D Collocated Dual‐Gradient Sensory Fibers for Comprehensive Sensing and Decoding of Complex Human Motion and Physiology

材料科学 纤维 标度系数 渗透(认知心理学) 生物系统 变形(气象学) 图层(电子) 计算机科学 解码方法 压阻效应 噪音(视频) 声学 限制 接头(建筑物) 网络层 过程(计算) 可穿戴计算机 感觉系统 同轴 纳米技术 光电子学 运动(物理) 加速度 分割 信噪比(成像) 突出 流变学 航程(航空)
作者
Yunheum Lee,Sungha Jeon,Min Kim,Joonhee Won,Jinwook Yeo,Kyunghyun Jo,S Lee,K K. Park,Yechan Yang,Chul Kim,Gunhee Lee,S.E. Ryu,Sungho Jo,Seongjun Park
出处
期刊:Advanced Materials [Wiley]
卷期号:38 (53): e74328-e74328
标识
DOI:10.1002/adma.74328
摘要

Wearable interfaces that capture both weak physiological fluctuations and large body motions remain difficult to realize in a single fiber because most strain-sensing fibers are governed by one dominant electromechanical response mode. Consequently, mechanically distinct deformation regimes are ambiguously represented, limiting sensing range and information quality for downstream motion interpretation. Here, we develop a monolithic dual-gradient fiber with complementary sensing regimes programmed through coupled materials and process design. By jointly tuning the percolation behavior and rheological drawability of carbon black/carbon nanotube-filled styrene-ethylene-butylene-styrene (SEBS) composites, we identified two formulations suitable for coaxial co-drawing within one continuous strand. The resulting fiber pairs a highly responsive layer for small deformation with a robust layer that stays informative at larger strain, producing synchronized but nonredundant signals. At low strain (0%-10%), the high-sensitivity layer exhibits a gauge factor of 51.28, over four times the low-sensitivity layer (12.42), while the two layers remain functional up to approximately 40% and 160% strain, respectively. This architecture preserves mechanically salient features across distinct and superimposed inputs, supporting measurements from pulse and respiration to joint motion. In a single-fiber glove, the dual-sensitivity design improves gesture-classification accuracy by more than 10% relative to single-sensitivity controls, enriching features for decoding.
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