The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enh...The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enhance productivity,safety,and ergonomics.However,achieving seamless collaboration requires robots to recognize the identity of individual human workers and perform appropriate collaborative operations.This paper presents a novel gait identity recognition method using Inertial Measurement Unit(IMU)data to enable personalized HRC in manufacturing settings,contributing to the human-centric vision of Industry 5.0.The hardware of the entire system consists of the IMU wearable device as the data source and a collaborative robot as the actuator,reflecting the interconnected nature of HCPS.The proposed method leverages wearable IMU sensors to capture motion data,including 3-axis acceleration,3-axis angular velocity.The two-tower Transformer architecture is employed to extract and analyze gait features.It consists of Temporal and Channel Modules,multi-head Auto-Correlation mechanism,and multi-scale convolutional neural network(CNN)layers.A series of optimization experiments were conducted to improve the performance of the model.The proposed model is compared with other state-of-the-art studies on two public datasets as well as one self-collected dataset.The experimental results demonstrate the better performance of our method in gait identity recognition.It is experimentally verified in the manufacturing environment involving four workers and one collaborative robot in an HRC assembly task,showcasing the practical applicability of this human-centric approach in the context of Industry 5.0.展开更多
Textiles with electronic components offer a portable and personalized approach for health monitoring and therapy.However,there is a lack of reliable strategy to integrate layered circuits and high-density chips on or ...Textiles with electronic components offer a portable and personalized approach for health monitoring and therapy.However,there is a lack of reliable strategy to integrate layered circuits and high-density chips on or inside textiles,which hinders system-level functionality and untethered user experiences.Herein,we propose monolithically integrated textile hybrid electronics(THE)on a textile platform,with multimodal functions and reliable performances.The textile system encompasses flexible electrodes,laser-induced sensors,and surface-mount devices,along with double-layer circuits interconnecting all of them.Vertical conductive paths are rendered by liquid metal composites infiltrated into textiles,which allows resistances less than 0.1?while reserving intact textile structures.The assembled THE exhibits endurance to handwashing and crumpling,as well as bendability.We customize a wireless textile patch for synchronously tracking multiple physiological indicators during exercise.Furthermore,a textile band is elaborated for monitoring and alleviating muscular fatigue,demonstrating potential in closed-loop diagnosis and treatment.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52375031,52405038)Zhejiang Provincial Natural Science Foundation(Grant No.LRG25E050001)+4 种基金China Postdoctoral Science Foundation(Grant Nos.GZB20240654,2024M762812,2025T180371)the Priority-Funded Postdoctoral Research Project of Zhejiang Province(Grant No.ZJ2024013)the Dongfang Electric Corporation-Zhejiang University Joint Innovation Research Institutethe Bellwethers+X Research and Development Plan of Zhejiang Province(Grant Nos.2024C04057(CSJ),2025C01012)the Joint Research Project of Sci-Tech Innovation Community in Yangtze River Delta(Grant No.2023CSJGG1400)。
文摘The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enhance productivity,safety,and ergonomics.However,achieving seamless collaboration requires robots to recognize the identity of individual human workers and perform appropriate collaborative operations.This paper presents a novel gait identity recognition method using Inertial Measurement Unit(IMU)data to enable personalized HRC in manufacturing settings,contributing to the human-centric vision of Industry 5.0.The hardware of the entire system consists of the IMU wearable device as the data source and a collaborative robot as the actuator,reflecting the interconnected nature of HCPS.The proposed method leverages wearable IMU sensors to capture motion data,including 3-axis acceleration,3-axis angular velocity.The two-tower Transformer architecture is employed to extract and analyze gait features.It consists of Temporal and Channel Modules,multi-head Auto-Correlation mechanism,and multi-scale convolutional neural network(CNN)layers.A series of optimization experiments were conducted to improve the performance of the model.The proposed model is compared with other state-of-the-art studies on two public datasets as well as one self-collected dataset.The experimental results demonstrate the better performance of our method in gait identity recognition.It is experimentally verified in the manufacturing environment involving four workers and one collaborative robot in an HRC assembly task,showcasing the practical applicability of this human-centric approach in the context of Industry 5.0.
基金support from the National Natural Science Foundation of China(Grant Nos.52475610 and 52105593)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LDQ24E050001)+2 种基金the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C01051)the Leading Innovation and Entrepreneurship Team Project in Zhejiang(Grant No.2022R01001)the Fundamental Research Funds for the Central Universities(Grant No.226-2024-00085)。
文摘Textiles with electronic components offer a portable and personalized approach for health monitoring and therapy.However,there is a lack of reliable strategy to integrate layered circuits and high-density chips on or inside textiles,which hinders system-level functionality and untethered user experiences.Herein,we propose monolithically integrated textile hybrid electronics(THE)on a textile platform,with multimodal functions and reliable performances.The textile system encompasses flexible electrodes,laser-induced sensors,and surface-mount devices,along with double-layer circuits interconnecting all of them.Vertical conductive paths are rendered by liquid metal composites infiltrated into textiles,which allows resistances less than 0.1?while reserving intact textile structures.The assembled THE exhibits endurance to handwashing and crumpling,as well as bendability.We customize a wireless textile patch for synchronously tracking multiple physiological indicators during exercise.Furthermore,a textile band is elaborated for monitoring and alleviating muscular fatigue,demonstrating potential in closed-loop diagnosis and treatment.
