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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Bayesian deep multi-instance learning for student performance prediction based on campus big data

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Neurocomputing 2025年 647卷

作者： Huang, Jiayang Yang, Keyi Wang, Quan Yang, Pengfei Ruan, Ziling Wang, Jiaxi Zhang, Zhi-Qiang School of Computer Science and Technology Xidian University Xi'an710126 China The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an710126 China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds Leeds United Kingdom

Problem: Predicting student performance using campus big data and deep learning methods has emerged as a promising alternative to traditional psychological assessments, which are often delayed and subjective. However, existing deep learning approaches face significant challenges, including the sparsity of campus big data and susceptibility to overfitting. Objective: To address these issues, this study aims to develop a robust and effective framework for predicting student performance by overcoming data sparsity and overfitting problems. Method: We propose a novel Bayesian Deep Multi-Instance Learning (Bay-DeepMIL) framework. The method integrates multi-instance learning (MIL) to handle data sparsity and incorporates a Bayesian framework to treat network parameters as probabilistic distributions, enhancing robustness and mitigating overfitting. Additionally, a Bayesian multi-head attention mechanism is introduced to dynamically assign importance to different instances, improving the extraction of key information from multi-instance data. Results: Extensive experiments demonstrate that Bay-DeepMIL outperforms state-of-the-art methods in prediction accuracy. The framework also provides uncertainty estimates for each prediction, offering valuable confidence measures and decision support for educational stakeholders. Conclusion: The proposed Bay-DeepMIL framework not only advances the technical capabilities of predictive models but also provides practical tools for enhancing educational decision-making. This study underscores the effectiveness of integrating Bayesian inference with multi-instance learning to address core challenges such as data sparsity and model overfitting in student performance prediction. © 2025 Elsevier B.V.

关键词： Students

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A human-human Physical interaction System for Training Handwriting

A Human-Human Physical Interaction System for Training Handw...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Hybrid Long Short-Term Motor Optimization and Control of a Walking Exoskeleton

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IEEE Transactions on robotics 2025年 41卷 3820-3840页

作者： Huang, Pengbo Li, Zhijun Zhou, Mengchu Li, Guoxin Song, Yang Cui, Rongxin Tongji University School of Mechanical Engineering Translational Research Center Shanghai Yangzhi Rehabilitation Hospital Shanghai Sunshine Rehabilitation Center Shanghai 200092 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Shanghai 201804 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States Tongji University School of Electronic and Information Engineering Shanghai China Northwestern Polytechnical University School of Marine Science and Technology Xi'an 710072 China

This paper proposes a hybrid long short-term motor (HLSM) optimization and control approach for a walking exoskeleton. It consists of long-term global optimization, short-term local optimization, human-in-the-loop trajectory adaptation, and hybrid cerebellar model articulation controller (HCMAC). In the long-term global optimization, a graphic Spiking Neural Network (SNN) is utilized for an optimal global path. Along the path, the short-term motor optimization includes footstep optimization and obtains a sequence of footsteps. While in response to the unexpected obstacles along the footstep sequence, a human-in-the-loop planning strategy is designed by a virtual impedance model between the Centers of Mass (COMs) of the human and the exoskeleton, regulating the COM of the exoskeleton and generating footstep adaptation of the exoskeleton such that the exoskeleton can avoid obstacles and maintain its original global trajectory. Moreover, considering the unmodeled dynamics, we propose an HCMAC based on an integral Lyapunov function , which is exploited to counteract the system's nonlinear uncertainties, external disturbances, and reduces a relatively high computational cost. We validate the effectiveness of the HLSM planner and controller in a practical indoor setting. The results demonstrate the effectiveness of HLSM planning and control in a real scenario for a walking exoskeleton. © 2004-2012 IEEE.

关键词： human-in-the-loop hybrid cerebellar model articulation controller (HCMAC) Long short-term motor optimization walking exoskeleton

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A General and Flexible Search Framework for Disassembly Planning

A General and Flexible Search Framework for Disassembly Plan...

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IEEE International Conference on robotics and Automation

作者： Timothy Ebinger Sascha Kaden Shawna Thomas Robert Andre Nancy M. Amato Ulrike Thomas Member of the Department of Computer Science & Engineering Texas A&M University TX USA Member of the Robotics and Human Machine Interaction Group Chemnitz University of Technology Chemnitz Germany

We present a new general framework for disassembly sequence planning. This framework is versatile allowing different types of search schemes (exhaustive vs. preemptive), various part separation techniques, and the ability to group parts, or not, into subassemblies to improve the solution efficiency and parallelism. This enables a truly hierarchical approach to disassembly sequence planning. We demonstrate two different search strategies using this framework that can either yield a single solution quickly or provide a spectrum of solutions from which an optimal may be selected. We also develop a method for subassembly identification based on collision information. Our results show improved performance over an iterative motion planning based method for finding a single solution and greater functionality through hierarchical planning and optimal solution search.

关键词： Planning Trajectory Measurement Data structures Search problems Learning systems Containers

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Control strategy for stabilization of the biped trunk-SLIP walking model

Control strategy for stabilization of the biped trunk-SLIP w...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Minh Nhat Vu Jongwoo Lee Yonghwan Oh Department of Human-Computer Interaction and Robotics University of Science and Technology (UST) Korea Center for Robotics Research Korea Institute of Science and Technology (KIST) Seoul Korea

In this paper, we present a new control strategy for the Bipedal Trunk Spring Loaded Inverted Pendulum (BTSLIP) model, consisting of springy legs and rigid trunk. Our walking controller is inspired from the Virtual Pendulum (VP) concept. In this concept, by intersecting the ground reaction forces (GRF) to a virtual pivot point (VPP), the trunk is provided the restoring moment to keep its balance and to behave like a virtual hanging pendulum. The simple hip torque control was presented with the name the VPP method. However, this approach is not reliable in terms of rejecting disturbances. Thus, we propose a new control strategy to achieve the robustness and stabilize the walking gait. Herein, the control is introduced with the combination of two strategies, 1) updating the position of the VPP by using Discrete Linear Quadratic Regulator controller and 2) controlling the stiffness of springy legs by feedback linearization controller to track both reference vertical position and velocity of the Center of Mass (CoM). We present the dynamic simulation of the BTSLIP model under the presence of external disturbances. The performance of the proposed control strategy shows stable, robust walking which is compared with the VPP method.

关键词： Legged locomotion Hip Force Trajectory Springs Torso Load modeling

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Mathematical models of adaptation in human-robot collaboration

arXiv

引用

arXiv 2017年

作者： Nikolaidis, Stefanos Forlizzi, Jodi Hsu, David Shah, Julie Srinivasa, Siddhartha Robotics Institute Carnegie Mellon University Human-Computer Interaction Institute Carnegie Mellon University Department of Computer Science National University of Singapore Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interacting with people needs to reason over its uncertainty over the human internal state, as well as over how this state may change, as humans adapt to the robot. This paper summarizes our own work in this area, which depicts the different ways that probabilistic planning and game-theoretic algorithms can enable such reasoning in robotic systems that collaborate with people. We start with a general formulation of the problem as a two-player game with incomplete information. We then articulate the different assumptions within this general formulation, and we explain how these lead to exciting and diverse robot behaviors in real-time interactions with actual human subjects, in a variety of manufacturing, personal robotics and assistive care settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Game theory

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A novel multi-articular leg mechanism for biped robots inspired by bi-articular muscle

A novel multi-articular leg mechanism for biped robots inspi...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Jongwoo Lee Giuk Lee Seokmin Hong SangWook Lee Jung Hoon Kim Yonghwan Oh Center for Robotics Research Korea Institute of Science and Technology (KIST) Seoul Korea Department of Human-Computer Interaction and Robotics University of Science and Technology (UST) Korea

ISBN: (纸本)9781509032884

This paper proposes a novel multi-articular leg system for biped robot. The structure and actuation scheme of the mechanism are inspired by redundantly-actuated manipulators, of which actuators span over multiple joints like bi-articular muscles. The proposed mechanism for robotic leg contributes to improved capacity in end-effector force exertion, which allows less performance requirements in individual actuators. For the purpose of evaluation, we provide the static torque-force transmission analysis, as well as walking simulation results conducted in the dynamic simulator. The results indicate that the proposed mechanism can benefit from the improved output force capacity.

关键词： Legged locomotion Force Muscles Actuators Dynamics Manipulators

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Walking Pattern Generation in Sagittal Plane Possessing Characteristics of human Normal Walking

Walking Pattern Generation in Sagittal Plane Possessing Char...

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IEEE International Conference on Mechatronics and Automation

作者： Sangwook Lee Jongwoo Lee Dong Gun Lee Yonghwan Oh Human Computer Interaction and Robotics University of Science and Technology 217 Gajeong-ro Yuseong-gu Daejeon Korea Center for Robotics Korea Institute of Science and Technology 39-1 Hwarang-ro Seongbuk-gu Seoul Korea Center of Human-centered Interaction for Coexistence 39-1 Hwarang-ro Seongbuk-gu Seoul Korea

ISBN: (纸本)9781509023974

A human walking has some general characteristics such as general timing of walking cycle, foot state at each timing, relationship with the stride per stature and walking speed, joint angle patterns of leg, pelvis movement, foot clearance during swing phase. So we collected and analysed the normal walking characteristics to generating natural walking pattern for general biped rigid-body configuration And we explained how to generate the walking pattern in sagittal plane possessing human normal walking characteristics. Finally, we demonstrated that our walking pattern properly have the normal walking characteristics.

关键词： Biped robot Walking pattern generation Normal walking characteristics Natural walking

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