ORCID Profile
0000-0002-8685-5426
Current Organisation
Zhejiang University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Wiley
Date: 03-2021
Publisher: Wiley
Date: 08-07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: MDPI AG
Date: 27-11-2019
DOI: 10.3390/S19235197
Abstract: Gait analysis is an important assessment tool for analyzing vital signals collected from in iduals and for providing physical information of the human body, and it is emerging in a erse range of application scenarios, such as disease diagnosis, fall prevention, rehabilitation, and human–robot interaction. Herein, a kind of surface processed conductive rubber was designed and investigated to develop a pressure-sensitive insole to monitor planar pressure in a real-time manner. Due to a novel surface processing method, the pressure sensor was characterized by stable contact resistance, simple manufacturing, and high mechanical durability. In the experiments, it was demonstrated that the developed pressure sensors were easily assembled with the inkjet-printed electrodes and a flexible substrate as a pressure-sensitive insole while maintaining good sensing performance. Moreover, resistive signals were wirelessly transmitted to computers in real time. By analyzing s led resistive data combined with the gait information monitored by a visual-based reference system based on machine learning method (k-Nearest Neighbor algorithm), the corresponding relationship between plantar pressure distribution and lower limb joint angles was obtained. Finally, the experimental validation of the ability to accurately ide gait into several phases was conducted, illustrating the potential application of the developed device in healthcare and robotics.
Publisher: Wiley
Date: 26-04-2022
Abstract: Robots equipped with bionic skins for enhancing the robot perception capability are increasingly deployed in wide applications ranging from healthcare to industry. Artificial intelligence algorithms that can provide bionic skins with efficient signal processing functions further accelerate the development of this trend. Inspired by the somatosensory processing hierarchy of humans, the bioinspired co‐design of a tactile sensor and a deep learning‐based algorithm is proposed herein, simplifying the sensor structure while providing computation‐enhanced tactile sensing performance. The soft piezoresistive sensor, based on the carbon black‐coated polyurethane sponge, offers a continuous sensing area. By utilizing a customized deep neural network (DNN), it can detect external tactile stimulus spatially continuously. Besides, a novel data augmentation method is developed based on the sensor's hexagonal structure that has a sixfold rotation symmetry. It can significantly enhance the generalization ability of the DNN model by enriching the collected training data with generated pseudo‐data. The functionality of the sensor and the robustness of the proposed data augmentation strategy are verified by precisely recognizing five touch modalities, illustrating a well‐generalized performance, and providing a promising application prospect in human–robot interaction.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Wiley
Date: 22-09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: MDPI AG
Date: 05-11-2018
DOI: 10.3390/MI9110576
Abstract: For industrial manufacturing, industrial robots are required to work together with human counterparts on certain special occasions, where human workers share their skills with robots. Intuitive human–robot interaction brings increasing safety challenges, which can be properly addressed by using sensor-based active control technology. In this article, we designed and fabricated a three-dimensional flexible robot skin made by the piezoresistive nanocomposite based on the need for enhancement of the security performance of the collaborative robot. The robot skin endowed the YuMi robot with a tactile perception like human skin. The developed sensing unit in the robot skin showed the one-to-one correspondence between force input and resistance output (percentage change in impedance) in the range of 0–6.5 N. Furthermore, the calibration result indicated that the developed sensing unit is capable of offering a maximum force sensitivity (percentage change in impedance per Newton force) of 18.83% N−1 when loaded with an external force of 6.5 N. The fabricated sensing unit showed good reproducibility after loading with cyclic force (0–5.5 N) under a frequency of 0.65 Hz for 3500 cycles. In addition, to suppress the bypass crosstalk in robot skin, we designed a readout circuit for s ling tactile data. Moreover, experiments were conducted to estimate the contact/collision force between the object and the robot in a real-time manner. The experiment results showed that the implemented robot skin can provide an efficient approach for natural and secure human–robot interaction.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Wiley
Date: 18-05-2023
Abstract: Wearable sensors for human motion capture offer a promising human–robot interface to control robots in the teleoperation scenario, where robots could function as the second body of human operators to fulfill tasks remotely and accurately. In this paper, a novel strain sensor based on a soft polyurethane (PU) sponge and carbon nanotubes (CNT) is designed for motion capture of human joints. The unique 3D porous microstructure of the PU‐CNT sponge provides the sensor with high sensitivity. To bridge the gap between the high sensitivity and high stretchability of the strain sensor, a rhombic‐mesh structure with optimized geometric parameters, in conjunction with a pre‐compression design, is proposed for strain sensor prototyping, which endows the sensor with an extra elongation rate during the stretching process. The proposed PU‐CNT strain sensor manifests promising sensing performance with a stretchability of up to 300% and a maximum gauge factor of 3893, together with long‐term durability, low detection limit, and fast response capacity. Finally, the validation of the strain sensor is carried out by deploying the sensor on a human elbow to realize the teleoperation of a robot arm, which could be monitored through the digital twin model of the robot in a real‐time manner.
Publisher: Wiley
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
No related grants have been discovered for Geng Yang.