In this study, the design of a Digital-twin human-machine interface sensor (DT-HMIS) is proposed. This is a digital-twin sensor (DT-Sensor) that can meet the demands of human-machine automation collaboration in Industry 5.0. The DT-HMIS allows users/patients to add, modify, delete, query, and restore their previously memorized DT finger gesture mapping model and programmable logic controller (PLC) logic program, enabling the operation or access of the programmable controller input-output (I/O) interface and achieving the extended limb collaboration capability of users/patients. The system has two main functions: the first is gesture-encoded virtual manipulation, which indirectly accesses the PLC through the DT mapping model to complete control of electronic peripherals for extension-limbs ability by executing logic control program instructions. The second is gesture-based virtual manipulation to help non-verbal individuals create special verbal sentences through gesture commands to improve their expression ability. The design method uses primitive image processing and eight-way dual-bit signal processing algorithms to capture the movement of human finger gestures and convert them into digital signals. The system service maps control instructions by observing the digital signals of the DT-HMIS and drives motion control through mechatronics integration or speech synthesis feedback to express the operation requirements of inconvenient work or complex handheld physical tools. Based on the human-machine interface sensor of DT computer vision, it can reflect the user’s command status without the need for additional wearable devices and promote interaction with the virtual world. When used for patients, the system ensures that the user’s virtual control is mapped to physical device control, providing the convenience of independent operation while reducing caregiver fatigue. This study shows that the recognition accuracy can reach 99%, demonstrating practicality and application prospects. In future applications, users/patients can interact virtually with other peripheral devices through the DT-HMIS to meet their own interaction needs and promote industry progress.

We propose two single-wavelength notch filters and one dual-wavelength (480 and 620 nm) notch filter to enhance image contrast. The stack structure of the notch filters was designed as (Ta2O5/SiO2)4Ta2O5 in Essential Macleod thin film simulation software. Dual-electron-beam evaporation with ion beam-assisted deposition was used to prepare optical interference filters with different center wavelengths. A multilayer notch filter with a center wavelength of 620 nm was deposited on the front surface of the glass, and then a notch filter with a center wavelength of 480 nm was coated on the rear surface of the same glass. The proposed dual-wavelength (480 and 620 nm) notch filter is a combination of two single-wavelength notch filters coated on a double-sided glass substrate to compensate for residual stress. The transmittance, residual stress, and surface roughness of the proposed notch filter were evaluated using different measuring instruments. The experimental results show that the residual stress of the dual-wavelength notch filter could be reduced to 10.8 MPa by using a double-sided coating technique. The root-mean-square (RMS) surface roughness of the notch filters was measured by using a Linnik microscopic interferometer. The RMS surface roughness was 1.80 for the 620 nm notch filter and 2.09 for the 480 nm notch filter. The image contrast obtained with the three different notch filters was measured using an optical microscope and a CMOS camera. The contrast value could be increased from 0.328 (without a filter) to 0.696 (dual-wavelength notch filter).

This study uses machine vision combined with drones to detect cracks in retaining walls in mountaineering areas or forest roads. Using the drone’s pre-collected images of retaining walls, the gaps in the wall are obtained as the target for sample data. Deep learning is carried out with neural network architecture. After repeated training of the module, the characteristic conditions of the crack are extracted from the image to be tested. Then, the various characteristics of the gap feature are extracted through image conversion, and the factors are analyzed to evaluate the danger degree of the gap. This study proposes a series of gap danger factor equations for the gap to analyze the safety of the detected gap image so that the system can judge the image information collected by the drone to assist the user in evaluating the safety of the gap. At present, deep learning modules and gap hazard evaluation methods are used to make suggestions on gaps. The expansion of the database has effectively improved the efficiency of gap identification. The detection process is about 20–25 frames per second, and the processing time is about 0.04 s. During the capture process, there will still be a few misjudgments and improper circle selections. The misjudgment rate is between 2.1% and 2.6%.