2025 ISAKOS Biennial Congress Paper
A 3D-Printed Wearable Sensor Based on Fiber Bragg Gratings for Shoulder Motion Monitoring
Alfredo Dimo, PhD, Rome ITALY
Arianna Carnevale, PhD, Roma, Roma ITALY
Vincenzo Candela, MD, PhD ITALY
Alessandra Berton, MD, Latina, LT ITALY
Giuseppe Salvatore, MD, PhD, Roma ITALY
Umile Giuseppe Longo, MD, MSc, PhD, Prof., Rome ITALY
Emiliano Schena, Eng, Rome, --- Select One --- ITALY
Daniela Lo Presti ITALY
Fondazione Policlinico Universitario Campus Bio-Medico, Rome, ITALY
FDA Status Not Applicable
Summary
The study presents a 3D-printed wearable sensor using fiber Bragg grating (FBG) technology to monitor shoulder movements. It aims to provide an accurate, affordable, and user-friendly solution for assessing patient recovery outside of traditional clinical settings.
Abstract
Background
Shoulder injuries, particularly rotator cuff (RC) injuries, can significantly impair a person’s ability to perform daily tasks due to pain and decreased mobility. Effective rehabilitation is essential for restoring the shoulder's range of motion (ROM). Currently, patient recovery assessment mainly relies on subjective evaluations by physicians. While motion capture (MOCAP) systems provide more objective assessments, they are costly and not readily available for routine clinical use. Thus, there is a need for affordable, user-friendly, and accurate systems to monitor shoulder movements during rehabilitation.
AIMS
This study aims to develop a new wearable sensor system that combines fiber Bragg grating (FBG) technology with 3D printing to monitor shoulder movements objectively. The goal is to offer a reliable, cost-effective alternative to traditional MOCAP systems. By integrating FBGs into a flexible 3D-printed structure, the sensor is designed to enhance movement tracking accuracy while ensuring comfort and compliance with the skin.
Methods
The research involved designing and fabricating a 3D-printed wearable sensor using thermoplastic polyurethane (TPU) as the filament. An FBG was embedded within a TPU substrate shaped like a dog bone to maximize sensitivity to uniaxial strain. The sensor's design featured two extensible bands for anchorage, integrated during the printing process—a novel approach. The sensor underwent assessments to evaluate its strain and temperature sensitivity and hysteresis error using a tensile testing machine and a laboratory oven to simulate various conditions. Additionally, a preliminary test was conducted on a healthy volunteer to assess the sensor’s performance in monitoring shoulder movements at different ranges (0°-30°; 0°-60°; 0°-90°) and speeds (0°-90° in 3s and 6s).
Results
The 3D-printed sensor demonstrated good strain sensitivity, with an average strain sensitivity (Sε) of about 1.45 nm/mε. The temperature sensitivity (ST) was 0.02 nm/°C, slightly higher than a bare FBG due to TPU's thermal expansion. Hysteresis error was high, up to 51%, during cyclic loading tests, indicating some non-linear response. In preliminary tests with a healthy volunteer, the sensor effectively detected shoulder movements in the sagittal plane, showing distinct output changes for different ROMs (0.22 nm at 30°, 0.46 nm at 60°, 0.77 nm at 90°) and movement rates (ten cycles at 6 seconds had a mean ΔλB of 0.784 nm±0.028 nm, while cycles at 3 seconds had a mean ΔλB of 0.762 nm±0.029 nm). Specifically, FBG output amplitude varied with arm angles and movement speeds, demonstrating the sensor's capability for real-time monitoring and suggesting potential clinical applications.
Discussion
This study presents a novel wearable sensor designed for monitoring shoulder movements by leveraging FBG technology and 3D printing. The integration of FBG within a flexible TPU substrate and the direct inclusion of anchorage mechanisms during printing represents a significant advancement in wearable sensor technology. Although the sensor showed promising results, the high hysteresis error suggests a need for further optimization. Future work will improve linearity, reduce temperature effects, and minimize hysteresis to enhance measurement accuracy. Expanding the study to include larger sample sizes and various movement conditions will be crucial for validating the sensor's clinical effectiveness.