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Jiayu Ma1, Yuyu Feng1, Ye Tian1

  • 1School of Semiconductor and Physics, North University of China, Taiyuan 030051, China.

Micromachines
|June 26, 2026
PubMed
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This study introduces a novel method for flexible strain sensors used in gait recognition. By optimizing sensor placement using lines-of-non-extension (LoNE) theory and employing analog-domain inference, it achieves high accuracy with low power consumption for wearable motion monitoring.

Area of Science:

  • Materials Science and Engineering
  • Biomedical Engineering
  • Wearable Technology

Background:

  • Flexible strain sensors are crucial for gait recognition but face challenges in sensor placement and power-hungry digital processing.
  • Current methods rely on empirical anatomical knowledge for sensor orientation, limiting optimization.
  • Existing systems' practicality in wearables is hindered by the power consumption and latency of back-end digital processors.

Purpose of the Study:

  • To develop an integrated design path for flexible strain sensors, optimizing placement and enabling analog-domain inference for enhanced gait recognition.
  • To replace empirical sensor placement with a mechanics-based approach using lines-of-non-extension (LoNE) theory.
  • To implement in-sensor computing (ISC) for low-power, low-latency analog classification in wearable motion monitoring.
Keywords:
analog-domain classificationflexible in-sensor-computing strain sensorgait recognitionlines-of-non-extension (LoNE)strain sensor

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Main Methods:

  • Utilized lines-of-non-extension (LoNE) theory to determine optimal sensor positions and angles on the skin for maximum strain transduction.
  • Developed an analog-domain front-end circuit that performs weighted summation via Kirchhoff's current law for classification.
  • Fabricated flexible strain sensors using screen-printed conductive ink on a TPU film substrate.

Main Results:

  • Achieved high sensor performance with a gauge factor RSD of 6.8% and tensile linearity R2>0.99.
  • Demonstrated 99% accuracy at the circuit level for classifying walking, running, and stair descent.
  • Attained 87.0%±8.4% accuracy on real multi-subject data with analog-domain inference response faster than 100μs.

Conclusions:

  • The integrated design path offers a promising low-power, lightweight solution for wearable motion monitoring and gait recognition.
  • The LoNE theory-based layout optimization and analog ISC provide a calculable and efficient alternative to empirical methods.
  • The proposed workflow is adaptable to different joints and sensor materials, paving the way for broader applications in motion analysis.