Flexible devices are increasingly crucial in various aspects of our lives,including healthcare devices and humanmachine interface systems,revolutionizing human life.As technology evolves rapidly,there is a high demand...Flexible devices are increasingly crucial in various aspects of our lives,including healthcare devices and humanmachine interface systems,revolutionizing human life.As technology evolves rapidly,there is a high demand for innovative manufacturing methods that enable rapid prototyping of custom and multifunctional flexible devices with high quality.Recently,digital light processing(DLP)3D printing has emerged as a promising manufacturing approach due to its capabilities of creating intricate customized structures,high fabrication speed,low-cost technology and widespread adoption.This review provides a state-of-the-art overview of the recent advances in the creation of flexible devices using DLP printing,with a focus on soft actuators,flexible sensors and flexible energy devices.We emphasize how DLP printing and the development of DLP printable materials enhance the structural design,sensitivity,mechanical performance,and overall functionality of these devices.Finally,we discuss the challenges and perspectives associated with DLP-printed flexible devices.We anticipate that the continued advancements in DLP printing will foster the development of smarter flexible devices,shortening the design-to-manufacturing cycles.展开更多
Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simul...Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability.To achieve real-time and accurate upper limb motion intention recognition,a multi-modal fusion method based on surface electromyography(sEMG)signals and arrayed flexible thin-film pressure(AFTFP)sensors was proposed.Through experimental tests on 10 healthy subjects(5 males and 5 females,age 23±2 years),sEMG signals and human-machine interaction force(HMIF)signals were collected during elbow flexion,extension,and shoulder internal and external rotation.The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion,and the recognition performance of single signals and fused signals was compared using a support vector machine(SVM).The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals(p<0.01,paired t-test).In offline conditions,the recognition accuracy of the fused signals exceeded 99.77%across different time windows.Under a 0.1 s time window,the real-time recognition accuracy of the fused signals was 14.1%higher than that of the single sEMG signal,and the system’s end-to-end delay was reduced to less than 100 ms.The AFTFP sensor is applied to motion intention recognition for the first time.And its low-cost,high-density array design provided an innovative solution for rehabilitation robots.The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance.The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities,enabling real-time and accurate motion intention recognition.This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.展开更多
基金supported by the Science and Technology Development Fund,Macao SAR(0119/2022/A3 and 0009/2023/ITP1)the Research Grant from the University of Macao and the University of Macao Development Foundation(SRG2022-00038-FST and MYRG-GRG2023-00225-FST-UMDF).
文摘Flexible devices are increasingly crucial in various aspects of our lives,including healthcare devices and humanmachine interface systems,revolutionizing human life.As technology evolves rapidly,there is a high demand for innovative manufacturing methods that enable rapid prototyping of custom and multifunctional flexible devices with high quality.Recently,digital light processing(DLP)3D printing has emerged as a promising manufacturing approach due to its capabilities of creating intricate customized structures,high fabrication speed,low-cost technology and widespread adoption.This review provides a state-of-the-art overview of the recent advances in the creation of flexible devices using DLP printing,with a focus on soft actuators,flexible sensors and flexible energy devices.We emphasize how DLP printing and the development of DLP printable materials enhance the structural design,sensitivity,mechanical performance,and overall functionality of these devices.Finally,we discuss the challenges and perspectives associated with DLP-printed flexible devices.We anticipate that the continued advancements in DLP printing will foster the development of smarter flexible devices,shortening the design-to-manufacturing cycles.
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012810).
文摘Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability.To achieve real-time and accurate upper limb motion intention recognition,a multi-modal fusion method based on surface electromyography(sEMG)signals and arrayed flexible thin-film pressure(AFTFP)sensors was proposed.Through experimental tests on 10 healthy subjects(5 males and 5 females,age 23±2 years),sEMG signals and human-machine interaction force(HMIF)signals were collected during elbow flexion,extension,and shoulder internal and external rotation.The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion,and the recognition performance of single signals and fused signals was compared using a support vector machine(SVM).The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals(p<0.01,paired t-test).In offline conditions,the recognition accuracy of the fused signals exceeded 99.77%across different time windows.Under a 0.1 s time window,the real-time recognition accuracy of the fused signals was 14.1%higher than that of the single sEMG signal,and the system’s end-to-end delay was reduced to less than 100 ms.The AFTFP sensor is applied to motion intention recognition for the first time.And its low-cost,high-density array design provided an innovative solution for rehabilitation robots.The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance.The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities,enabling real-time and accurate motion intention recognition.This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.
