# Distributed Sensing Along Fibres for Smart Clothing

arXiv: https://arxiv.org/abs/2402.09057 · HTML: https://mlsanity.com/paper/2402.09057/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 59 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [SIAN: software for structural identifiability analysis of ODE models](https://mlsanity.com/paper/1812.10180/) (2018)
- [Nonlinear observability algorithms with known and unknown inputs: analysis and implementation](https://mlsanity.com/paper/2006.00859/) (2020)
- [Structured identification for network reconstruction of RC-models](https://mlsanity.com/paper/2007.10046/) (2020)
- [Soft Continuum Actuator Tip Position and Contact Force Prediction, Using Electrical Impedance Tomography and Recurrent Neural Networks](https://mlsanity.com/paper/2302.06456/) (2023)

## Abstract (arXiv)

Textile sensors transform our everyday clothing into a means to track movement and bio-signals 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 fibre. We employ 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 fibre. Compared to optical motion capture, we achieve around 5deg error in reconstructing shoulder, elbow, and wrist joint angles.