Sensors

To tackle the challenges of missed detections, false alarms, electromagnetic noise, and constrained deployment resources in fabric-defect inspection, we propose a lightweight and interference-resilient fabric-defect detector based on the Discrete Wavelet Transform (DWT). First, a color-space channel separation filter leverages Hue–Saturation–Value (HSV) decomposition to suppress illumination and electromagnetic interference while preserving fabric structural details. Second, DWT is employed to extract directional texture features (horizontal, vertical, and diagonal) from complex woven structures. Third, the backbone of the You Only Look Once version 7 Tiny (YOLOv7-Tiny) is modified by replacing pooling with a Spatial Pyramid Dilated Convolution (SPD) block, which maintains fine-grained detail during downsampling. For upsampling, an inverted SPD block with channel concatenation is introduced to mitigate background redundancy caused by interpolation. Experimental results on the TILDA and DAGM datasets show that the proposed IR-YOLOv7-Tiny achieves mAP@0.5 of 96.8% and 98.8%, respectively, with only 3.5 M parameters. Outperforming baseline models achieved 2.2% and 3.9% in the mean Average Precision (mAP) at Intersection over Union (IoU) 0.5 (mAP@0.5). The results demonstrate excellent effectiveness and high deployability for resource-constrained industrial scenarios.

Drivers with color vision deficiency (CVD) often face difficulty recognizing traffic light colors at intersections. Relying solely on their limited color vision can increase safety risks while driving in urban environments. In the era of technological development, Intelligent Transportation Systems (ITSs) increasingly aim to provide support for traffic users, including individuals with CVD. To address user needs from diverse backgrounds, this study aims to develop a traffic light recognition system that provides offline multilingual audio feedback in Indonesian, Mandarin, and English. The proposed approach introduces a spatial-position inference framework by applying a full-frame traffic light annotation strategy to a YOLOv12 model, enabling traffic light state recognition based on the relative position of active lights rather than relying primarily on color information. This work contributes to reducing reliance on color-based perception traffic signal recognition frameworks tailored for assistive ITS applications targeting users with color vision deficiency. System performance is evaluated to verify its feasibility using a comprehensive dataset consisting of various traffic light conditions, including daytime and nighttime scenarios, varying weather, and different traffic densities. Experimental results show an average detection confidence of approximately 0.73, with a maximum confidence of 0.95 and low processing latency of 0.214 s on a CPU-only configuration. The system has the potential to enhance driving safety for individuals with color vision deficiency by offering an additional intelligent assistive tool instead of replacing standard driving regulations.

The goal of this study was to determine if a robot-assisted exercise system could lead individuals with Parkinson’s disease (PD) through different joint ranges of motion in a fun and effective manner. Eleven individuals with PD participated. A novel robotic system placed a target at different places in space for participants to tap with their hand, foot or knee. The range of motion (ROM) was collected by inertial measurement units (APDM), and was extracted using a custom code (Matlab). ROM was dependent upon the exercise and joint of interest. Participants illustrated acceptable levels of fatigue during each session, based on an average ending heart rate of 107.0 ± 11.9 bpm (~70% of maxHR) and an ending RPE of 6.5 ± 1.8 on a 10-point scale, indicating that the sessions were appropriately challenging. Standing forward reach, used to assess static balance and flexibility, improved by an average of 1.7 inches (p < 0.01), demonstrating immediate improvements from exercising with the robot. The results demonstrate the potential benefits of exercising with a robotic exercise system. The number of sessions spent with a PT can be limited by availability, so this system could be a fun way to encourage individuals with PD to complete their PT exercises at home.