Background: As the elderly population continues to grow, so does the demand for new and innovative solutions to tackle age-related chronic diseases and disabilities. Virtual Reality (VR) has been explored as a novel therapeutic tool for numerous health-related applications. Although findings frequently favors VR, methodological shortcomings prevent clinical recommendations. Moreover, the term “VR” is frequently used ambiguously to describe e.g., video games; the distinction remains vague between immersive VR (IVR) systems and non-immersive VR (NVR). With no distinct demarcation, results of outcome measures are often pooled in meta-analyses, without accounting for the immersiveness of the system. Objective: This systematic review focused on virtual reality-based rehabilitation of older adults (+60) in motor rehabilitation programs. The review aims to retrospectively classify previous studies according to the level of immersion, in order to get an overview of the ambiguity-phenomenon, and to utilize meta-analyses and subgroup analyses to evaluate the comparative efficacy of system immersion in VR-based rehabilitation. Methods: Following PRISMA guidelines, we conducted a systematic search for randomized controlled trials, describing virtual rehabilitation or video games interventions for older adults (+60). Main outcomes were pain, motivation, mobility, balance, and adverse events. Results: We identified 15 studies which included 743 patients. Only three studies utilized IVR. The rest used various NVR-equipment ranging from commercial products (e.g., Nintendo Wii), to bespoke systems that combine tracking devices, software, and displays. A random effects meta-analysis of 10 studies analyzed outcome measures of mobility, balance, and pain. Protocols and dosage varied widely, but outcome results were in favor of immersive and non-immersive interventions, however, dropout rates and adverse events were mostly in favor of the control. Conclusions: We initialize a call-for-action, to distinguish between types of VR-technology and propose a taxonomy of virtual rehabilitation systems based on our findings. Most interventions use NVR-systems, which have demonstrably lower cybersickness-symptoms than IVR-systems. Therefore, adverse events may be under-reported in RCT-studies. An increased demand for IVR-systems highlight this challenge. Care should be given, when applying the results of existing NVR tools to new IVR-technologies. Future studies should provide more detail about their interventions, and future reviews should differentiate between NVR and IVR.
Minute amounts of noncotton fiber impurities that mix with cotton fiber during cotton picking, drying, transportation, and processing are known as cotton foreign fibers. Foreign fibers reduce the cotton grade and affect the quality of cotton processing, causing problems such as stains and uneven dyeing of textile fabrics, leading to quality decline of the final product. Therefore, detecting foreign fiber in cotton is essential. In this paper, we analyze the harm due to foreign fiber in cotton and problems associated with its detection and summarize the foreign fiber-related detection equipment. Simultaneously, a summary of the recent studies and progress in foreign fiber-detection technologies and methods with regard to image segmentation, feature selection, and image classification, and prospects for future research directions is discussed in this paper.
The control precision of the rewinding tension system is an important index to determine the performance of the roll-to-roll precision coating machine. However, the characteristics of the rewinding tension system including non-linearity, multi-interference and strong coupling make it very difficult to control the rewinding tension system with high precision, which seriously restricts the tension control precision of the rewinding system. Therefore, a controller based on PID and feedforward control strategy is designed to solve the control problem of rewinding tension system. Firstly, a nonlinear coupling tension model is established for the rewinding system. Secondly, the tension model is linearized, and then the feedforward controller is designed for each modeled disturbance. Finally, the control performance of the PID feedforward controller is verified by MATLAB simulation. The results show that the designed controller has an effective anti-disturbance and improves the control precision of the rewinding tension system.
Automatic segmentation of fluorescein leakage in fundus fluorescein angiography images is important in the clinical diagnosis of advanced diabetic retinopathy. Despite the recent success of deep-learning-based models in improving medical image segmentation, segmentation of fluorescein leakage has been ignored owing to (1) a lack of publicly available data with sufficient annotations for training a segmentation network and (2) incapability of supervised models to accurately localize fluorescein leakage at different imaging angles. To address these issues, we studied the automatic segmentation of fluorescein leakage in fundus fluorescein angiography images and devised a method involving (1) a cross-modal learning framework for fluorescein leakage segmentation using both image and text data, (2) a dual attention learning module for identifying important linguistic and visual features, and (3) fluorescein-related-keyword classification for identifying meaningful textual expressions pertaining to the location and type of fluorescein leakage. We demonstrate the effectiveness of the proposed method for an in-house fundus fluorescein angiography image data set.
