The paper introduces a distributed sensing technology for smart clothing, focusing on transforming everyday clothing into functional devices that track movement and biosignals unobtrusively. The main challenge in scaling up sensor density in wearable electronics is the unreliable connections between rigid and textile elements. To address this, the authors propose a prototype garment, a compact readout circuit, and an algorithm to measure localized strain along multiple regions of a fiber. They use a helical auxetic yarn sensor with tunable sensitivity to selectively respond to strain signals. The system is demonstrated in a prototype garment that monitors arm joint angles from a single continuous fiber, achieving an error of around five degrees in reconstructing shoulder, elbow, and wrist joint angles compared to optical motion capture. The distributed sensing approach simplifies electrical connectivity, increases sensitivity, and allows for high spatial resolution, making it suitable for integration into textiles. The authors also developed a compact electronic impedance analyzer circuit to collect high-speed impedance measurements at multiple frequencies and used machine learning to reconstruct strain and joint angles. The results show promising accuracy in strain reconstruction and joint angle monitoring, with potential for further improvement in real-world scenarios.The paper introduces a distributed sensing technology for smart clothing, focusing on transforming everyday clothing into functional devices that track movement and biosignals unobtrusively. The main challenge in scaling up sensor density in wearable electronics is the unreliable connections between rigid and textile elements. To address this, the authors propose a prototype garment, a compact readout circuit, and an algorithm to measure localized strain along multiple regions of a fiber. They use a helical auxetic yarn sensor with tunable sensitivity to selectively respond to strain signals. The system is demonstrated in a prototype garment that monitors arm joint angles from a single continuous fiber, achieving an error of around five degrees in reconstructing shoulder, elbow, and wrist joint angles compared to optical motion capture. The distributed sensing approach simplifies electrical connectivity, increases sensitivity, and allows for high spatial resolution, making it suitable for integration into textiles. The authors also developed a compact electronic impedance analyzer circuit to collect high-speed impedance measurements at multiple frequencies and used machine learning to reconstruct strain and joint angles. The results show promising accuracy in strain reconstruction and joint angle monitoring, with potential for further improvement in real-world scenarios.