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Qiming Chen

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7 papers

2 author rows

JBHI Journal 2025 Journal Article

Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm

Siyu Liu
Mengzhen Liu
Deyu Zhang
Zhiyuan Ming
Ziyu Liu
Qiming Chen
Lingfei Ma
Jiawei Luo

Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16. 27 s, an average output latency of 1. 54 s, and an average correlation instantaneous rate (CIR) of 0. 0321. The proposed system shows 35. 37% better efficiency, 8. 03% reduced system delay, and 35. 28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.

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ICRA Conference 2019 Conference Paper

Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons in Slope Scenarios

Chaobin Zou
Rui Huang 0008
Hong Cheng 0002
Qiming Chen
Jing Qiu 0004

Lower-limb exoskeleton has gained considerable interests in walking assistance applications for paraplegic patients. In walking assistance of paraplegic patients, the exoskeleton should have the ability to help patients to walk over different terrains in the daily life, such as slope terrains. One critical issue is how to plan the stepping locations on slopes with different gradients, and generate stable and human-like gaits for patients. This paper proposed an adaptive gait planning approach which can generate gait trajectories adapt to slopes with different gradients for lower-limb walking assistance exoskeletons. We modeled the human-exoskeleton system as a 2D Linear Inverted Pendulum Model (2D-LIPM) with an external force in the two-dimensional sagittal plane, and proposed a Dynamic Gait Generator (DGG) based on an extension of the conventional Capture Point (CP) theory and Dynamic Movement Primitives (DMPs). The proposed approach can dynamically generate reference foot locations for each step on slopes, and human-like adaptive gait trajectories can be reproduced after the learning from demonstrated trajectories that sampled from level ground walking of normal healthy human. We demonstrated the efficiency of the proposed approach on both the Gazebo simulation platform and an exoskeleton named AIDER. Experimental results indicate that the proposed approach is able to provide the ability for exoskeletons to generate appropriate gaits adapt to slopes with different gradients.

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IROS Conference 2018 Conference Paper

Learning-based Walking Assistance Control Strategy for a Lower Limb Exoskeleton with Hemiplegia Patients

Rui Huang 0008
Zhinan Peng
Hong Cheng 0002
Jiangping Hu
Jing Qiu 0004
Chaobin Zou
Qiming Chen

Lower exoskeleton has gained considerable interests in walking assistance applications for both paraplegia and hemiplegia patients. In walking assistance of hemiplegia patients, the exoskeleton should have the ability to control the affected leg to follow the unaffected leg's motion naturally. One critical issue of walking assistance for hemiplegia patients is how to adapt the controller of both lower limbs with different patients. This paper presents a novel learning-based walking assistance control strategy for lower exoskeleton with hemiplegia patients. In the proposed control strategy, we modeled the control system of lower exoskeleton with hemiplegia patient as a Leader-Follower Multi-Agent System (LF -MAS). In order to adapt different patients with different conditions, reinforcement learning framework is utilized to adapt controllers online. In reinforcement learning framework with LF-MAS, we employed a Policy Iteration Adaptive Dynamic Programming (PI-ADP) algorithm, which aims to achieve better tracking control performance for lower exoskeleton with hemiplegia patient. We demonstrate the efficiency of proposed learning-based walking assistance control strategy in an exoskeleton system with healthy subjects who simulate hemiplegia patients. Experimental results indicate that the proposed control strategy can adapt different pilots with good tracking performance.

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ICRA Conference 2016 Conference Paper

Hierarchical Interactive Learning for a HUman-Powered Augmentation Lower EXoskeleton

Rui Huang 0008
Hong Cheng 0002
Hongliang Guo 0001
Qiming Chen
XiChuan Lin

Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for one pilot in different walking patterns. This characteristic requires that the exoskeletons should have the ability to learn and adapt the motion trajectories as well as controllers online. This paper presents a novel Hierarchical Interactive Learning (HIL) strategy which reduces the complexity of the exoskeleton sensory system and is able to handle varying interaction dynamics. The proposed HIL strategy is composed of two learning hierarchies, namely, high-level motion learning and low-level controller learning. The Dynamic Movement Primitives (DMPs) combined with Locally Weighted Regression (LWR) are employed to model and learn the motion trajectories, while reinforcement learning (RL) is used to learn the model-based controller. We demonstrate the efficiency of proposed HIL strategy on a single degree-of-freedom (DOF) platform as well as a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HIL strategy is able to handle the varying interaction dynamics with less interaction force between the pilot and the exoskeleton when compared to traditional model-based control algorithms.

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IROS Conference 2016 Conference Paper

Learning Cooperative Primitives with physical Human-Robot Interaction for a HUman-powered Lower EXoskeleton

Rui Huang 0008
Hong Cheng 0002
Hongliang Guo 0001
XiChuan Lin
Qiming Chen
Fuchun Sun 0001

Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical Human-Robot Interaction (pHRI). As a powerful representation tool for motion trajectories, Dynamic Movement Primitive (DMP) has been used extensively to model human movement trajectories. However, canonical DMPs only offers a general offline representation of human movement trajectory and neglects the real-time interaction term, therefore it cannot be directly applied to lower exoskeletons which need to model human motion trajectories online since different pilots have different trajectories and even one pilot might change his/her intended trajectory during walking. This paper presents a novel Coupled Cooperative Primitives (CCPs) scheme, which models the motion trajectories online. Besides maintaining canonical motion primitives, we also model the interaction term between the pilot and exoskeletons through impedance models and apply a reinforcement learning method based on Policy Improvement and Path Integrals (PI 2 ) to learn the parameters online. Experimental results on both a single Degree-Of-Freedom (DOF) platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme.

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IROS Conference 2015 Conference Paper

Interactive learning for sensitivity factors of a human-powered augmentation lower exoskeleton

Rui Huang 0008
Hong Cheng 0002
Qiming Chen
Huu-Toan Tran
XiChuan Lin

Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation applications of Berkeley Lower Extremity Exoskeleton (BLEEX). The SAC algorithm is widely used in human augmentation applications since it just need the information from the exoskeleton robot, so that the complexity of exoskeleton system can be reduced greatly. However, the SAC algorithm has two main drawbacks: 1) requiring accurate dynamic models of the exoskeleton, 2) can not manage the variation of interaction dynamics from different walking speed. This paper presents a novel developed learning control strategy based on SAC algorithm. In the proposed Adaptive Sensitivity Amplification Control (ASAC) strategy, the reinforcement learning method is utilized to learn the sensitivity factors online for the sake of handling the variation of interaction dynamics. We demonstrate the control efficiency of ASAC on an one degree-of-freedom (DOF) platform with swing movements first, and then extend it into a HUman-powered Augmentation Lower EXoskeleton (HUALEX). The experimental results show that the proposed ASAC strategy can handle the changing interaction dynamics with less interaction force between the pilot and the exoskeleton as compared with traditional SAC algorithm.

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AAAI Conference 2014 Conference Paper

Machine Translation with Real-Time Web Search

Lei Cui
Ming Zhou
Qiming Chen
Dongdong Zhang
Mu Li

Contemporary machine translation systems usually rely on offline data retrieved from the web for individual model training, such as translation models and language models. In contrast to existing methods, we propose a novel approach that treats machine translation as a web search task and utilizes the web on the fly to acquire translation knowledge. This end-to-end approach takes advantage of fresh web search results that are capable of leveraging tremendous web knowledge to obtain phrase-level candidates on demand and then compose sentence-level translations. Experimental results show that our web-based machine translation method demonstrates very promising performance in leveraging fresh translation knowledge and making translation decisions. Furthermore, when combined with offline models, it significantly outperforms a state-of-theart phrase-based statistical machine translation system.

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