This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move continuously according to the size of the hand and the bending of the joint. Representatively, four algorithms were proposed; point strain (PTS), area summation (AREA), proportional summation (PS), and PS/interference (PS/I or PS/I_ $\alpha$ ). For more accurate and efficient assessments, 3D printed hand replica with different finger sizes was adopted and quantitative evaluations were performed for index~little fingers (77 to 117 mm) and thumb (68~78 mm). For index~little fingers, the optimized algorithms were PS and PS/I_ $\alpha$ . For thumb, the optimized algorithms were PS/I_ $\alpha$ and AREA. The average error angle of the wearable hand module was observed to be 0.47 ± 2.51° and mean absolute error (MAE) was achieved at 1.63 ± 1.97°. These results showed that more accurate hand modules than other glove modules applied to different hand sizes can be manufactured using FBG strain sensors which move continuously and algorithms for tracking this movable FBG sensors.

Standard flexible colonoscopy is a powerful diagnostic tool for detecting colorectal cancer. However, the procedure entirely depends on the skill and perception of the colonoscopist, leading to unpredictable outcomes such as loop formation, potentially leading to severe complications. Consequently, there is a growing demand for a real-time shape monitoring system for colonoscopes. Despite various research efforts and commercial products that use magnetic sensors, challenges such as signal vulnerability, cost, and limited applicability persist. Optical fiber sensors (OFS), which address these drawbacks, are gaining increasing attention. However, measuring the shape of long colonoscopes accurately remains challenging for OFS due to twist-induced noise. In this study, we developed a twist-suppressed fiber Bragg grating (FBG) sensor to measure long 3-D shapes by enhancing the linearity of the sensor and introducing a node-type structure. We also introduced an insertion length measurement algorithm for stable monitoring with this shape sensor. The twist-suppressed shape sensor exhibited a torsion angle suppression of 92% compared to conventional FBG shape sensors, achieving a torsional angle of 0.48°/cm. We evaluated the performance of the sensor by measuring bending directions and various loop shapes. Clinical tests with FBG sensors demonstrated their potential for real-time monitoring and reducing the time required for loop reduction. A colonoscopic monitoring system capable of precise shape measurements can facilitate safer and quicker procedures.

The instantaneous center of rotation (ICOR) is essential engineering information, providing wheel slip data for fully autonomous skid-steering mobile robots driven by deep learning-based dead-reckoning algorithms. Conventionally, cost-effective and compact inertial measurement unit (IMU) sensors based on microelectromechanical systems (MEMSs) have been employed for ICOR measurement. However, these sensors inherently rely on the differential value of gyroscope data, leading to significant estimation errors due to the amplified noise at low sampling rates. To address this challenge, we developed an acceleration sensor array based on a fiber Bragg grating, inspired by existing MEMS acceleration sensor arrays, which allows ICOR estimation solely from the acceleration data provided by two sensors at the beginning of rotation. The estimation accuracies in the clockwise and counter-clockwise directions were measured to be 3.1 ± 5.1 mm and 3.7 ± 9.9 mm, respectively. Furthermore, a peak detection algorithm was proposed for extracting the peak acceleration under different ICOR conditions using the variance and Bollinger bands. The precision level was comparable to those of optical cameras and MEMS IMU sensors that use computationally intensive algorithms, such as Kalman filters, even without differentiation. This breakthrough in measurement capability opens up the possibility for real-time ICOR estimation, allowing the complete automation of skid-steering-based robots and benefiting fields constrained by the structural limitations of conventional MEMS IMU sensors.