A Compact Dual-Core Waveguide Bending Sensor for Embodied Joint Angle Measurement

• Published in: IEEE/ASME transactions on mechatronics pp. 1 - 11
• Main Authors: Liu, Zeyu, Cheng, Long, Li, Zhengwei, Li, Xiao, Wei, Jiachen, Feng, Pengpeng
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Accuracy; Adaptive optics; Artificial intelligent sensors; Bending; Biomedical optical imaging; dual-core optical waveguide; embodied measurement; human–machine interaction (HMI); joint angle monitoring; Optical device fabrication; Optical mixing; Optical scattering; Optical sensors; Optical waveguides; Sensors; wearable bending sensor
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2025.3576678

Bending sensors are extensively employed for joint angle measurement across various applications, including human-machine interaction (HMI), embodied intelligent systems, sports performance analysis, and rehabilitation therapy. Compared to video-based methods, wearable soft bending sensors offer broader applicability and are immune to occlusion. This article presents a compact dual-core waveguide bending sensor specifically designed for embodied joint angle measurement. Building upon prior tri-core research, the enhanced sensor leverages optical frequency for multichannel signal coupling and decoupling, enabling a more compact design. This advancement facilitates finger joint angle detection while significantly improving measurement capabilities. The proposed sensor consists of two detection core segments with distinct optical characteristics. Each segment contains two semi-cores of different colors to determine the bending direction. This design achieves compactness and reduces power consumption. The sensor exhibits excellent linearity, remarkable stability, and an average sensitivity of 0.17 dB/deg over a range of 0<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> to 90<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> in both directions. In addition, a bidirectional mamba neural network (BMNN) light signal decoupling algorithm is developed to map short-time optical signals to joint angles, achieving a cross-subject joint angle measurement error of 0.4<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula>. This advancement significantly enhances existing joint angle sensors, addressing challenges of reliability and accuracy in joint angle measurement. It holds the potential to advance fields reliant on precise human movement analysis, facilitating more accurate HMIs and the development of universally applicable embodied intelligent systems.