Distributed Sensing Along Fibres for Smart Clothing

Abstract

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 5° error in reconstructing shoulder, elbow, and wrist joint angles.

Figures (7)

• Figure 1: A conceptual illustration of our distributed sensing approach used to measure the three major joint angles of the arm, with an envisioned extension of the sensing fibre around the torso.
• Figure 2: Prototype garment and sensing fibre.a, Rendering of the path of the sensor along the arm, where I, III, and V indicate the high sensitivity regions around the shoulder, elbow, and wrist joints, respectively. b, Top: geometric modelling of the HACS sensor low-sensitivity (II & IV: 3.5mm pitch) and high sensitivity (I, III, & V: 8mm pitch) regions under relaxed and 30% strain conditions; Bottom: photographs of the unstrained and strained sensor in the garment at the elbow joint. c, Response versus strain plots (solid), gauge factor (dashed black), and computational model predictions (round black marker) for the respective HACS sensitivity regions (model adapted from our previous work Cuthbert2023).
• Figure 3: Discretized sensor schematic and electronics overview and function.a, Schematic of sensor connectivity, split into four sensing regions. b, The corresponding RC ladder model circuit. c, Exploded view of the readout electronics, including enclosure, analog front-end printed circuit board, and field-programmable gate array (FPGA) board. d, Block diagram overview of the entire signal processing pathway with $n$ frequency channels, including sine wave generator (SWG), digital-to-analog converter (DAC), sensor model circuit, analog-to-digital converter (ADC), phase-sensitive demodulator (PSD), and filtering blocks.
• Figure 4: Frequency separation of strain across the transmission line.a, Response $\Delta C/C(0)$ versus strain for each sensing region, tested individually to determine the gauge factor via a linear fit. b, The simulated capacitance frequency response $C(\varepsilon)-C(0)$ of the model circuit from Fig. \ref{['fig:system']}b, using initial values and gauge factors from a under strains of 10%--40%. c, Corresponding frequency sweeps obtained with the LCR meter on the strain sensitive fibre samples. d, The response evaluated using our readout system at four discrete frequencies.
• Figure 5: Abbreviated schematic with example signals.a, Top: multi-sinusoid excitation current $i_{exc}(t)$ waveform (simulated and measured) and its Fourier transform. b, Voltage response from the sensor $v_{meas}(t)$ (measured, and simulated using the ladder model from equation (\ref{['eq:transmission-line']})) and its Fourier transform, with sensor model frequency magnitude response overlaid in dashed yellow. c, Fourier transform of the PSD mixer output $I_4[k]$, with DC component $\hat{I}_4[k]$ shown as hatched area and approximate low-pass filter response shown in dashed grey. d, The relative in-phase $\hat{I}_i[k]$ and quadrature $\hat{Q}_i[k]$ impedance components from the sensor model simulation.