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Evaluation of LSTM for predicting grip strength using electromyography: a comparison of setups and methods

Neural Computing and Applications 2025年

作者： Anam, Khairul Sudrajat, Ahmad Rizal, Naufal Ainur Intyanto, Gramandha Wega Muldayani, Wahyu Negara, Mohamad Agung Prawira Sumardi Bukhori, Saiful Gitakarma, Made Santo Castellini, Claudio Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Jawa Timur Jember68121 Indonesia Intelligent Systems and Robotics Lab CDAST Universitas Jember Jl. Kalimantan 37 Jawa Timur Jember68121 Indonesia Faculty of Computer Science CDAST Universitas Jember Jl. Kalimantan 37 Jawa Timur Jember68121 Indonesia Department of Electronic Systems Engineering Technology Universitas Pendidikan Ganesha Jl. Udayana 11 Bali Singaraja81116 Indonesia Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Nürnberger Str. 74 Bavaria Erlangen91052 Germany

Despite decades of research in prosthetics and myocontrol, using electromyography (EMG) to accurately predict the force a user grasps an object with is still a subject of investigation. Although the problem seems trivial, the optimal EMG setup, able to deliver high prediction accuracy at a minimal economic and computational cost needs to be found. In this work, we compare several EMG setups consisting of one to eight sensors and deep learning methods to find out which combination is most convenient. In particular, we compare long short-term memory (LSTM), together with a stacked autoencoder (LSTM–SAE) and an attention mechanism (LSTMATT). Our experimental results reveal that, while the best performance is attained by LSTM–SAE (coefficient of correlation 0.9867±0.0087, coefficient of determination 0.9676±0.0489, normalized root mean square error 0.048±0.0213), statistically significant differences can only be found when the number of sensors is drastically reduced, namely to 2 sensors, in which case, anyway, the performance is still close to optimal and even surpasses state-of-the-art methods. Further research will focus on testing the optimal approach and setup online on amputated users using prosthetic hardware in daily living activities. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Motion prediction with recurrent neural network dynamical models and trajectory optimization

arXiv

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arXiv 2019年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany

Predicting human motion in unstructured and dynamic environments is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose to encode the lower level aspects of human motion separately from the higher level geometrical aspects, which we believe will generalize better over environments. In contrast to our prior work [6], we encode the short-term behavior by using a state-of-the-art recurrent neural network structure instead of a Gaussian process. In order to perform longer term behavior predictions that account for variation in tasks and environments, we propose to make use of gradient based trajectory optimization. Preliminary experiments on real motion data demonstrate the efficacy of the approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Learning arbitration for shared autonomy by hindsight data aggregation

arXiv

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arXiv 2019年

作者： Oh, Yoojin Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems MPI-IS Tübingen Germany

In this paper we present a framework for the teleoperation of pick-and-place tasks. We define a shared control policy that allows to blend between direct user control and autonomous control based on user intent inference. One of the main challenges in shared autonomy systems is to define the arbitration function, which decides when to let the autonomous agent take over. In this work, we propose a model and training method to learn the arbitration function. Our model is based on a recurrent neural network that takes as input the state, intent prediction scores and user command to produce an arbitration between user and robot commands. This work extends our previous work on differentiable policies for shared autonomy [12]. Differentiability of the policy is desirable to further train the shared autonomy system end-to-end. In this work we propose training of the arbitration function by using data from user performing the task with shared control. We present initial results by teleoperating a gripper in a virtual environment using pre-trained motion generation and intent prediction. We compare our data aggregation training procedure to a handcrafted arbitration function. Our preliminary results show the efficacy of the approach and shed light on limitations that we believe demonstrate the need for user adaptability in shared autonomy systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous agents

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Prediction of human full-body movements with motion optimization and recurrent neural networks

arXiv

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arXiv 2019年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany

Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose a prediction framework that decouples short-Term prediction, linked to internal body dynamics, and long-Term prediction, linked to the environment and task constraints. In this work we investigate encoding short-Term dynamics in a recurrent neural network, while we account for environmental constraints, such as obstacle avoidance, using gradient-based trajectory optimization. Experiments on real motion data demonstrate that our framework improves the prediction with respect to state-of-The-Art motion prediction methods, as it accounts to beforehand unseen environmental structures. Moreover we demonstrate on an example, how this framework can be used to plan robot trajectories that are optimized to coordinate with a human partner. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion estimation

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A multi-task learning convolutional neural network for object pose estimation

A multi-task learning convolutional neural network for objec...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Wang, Yurui Jin, Shaokun Ou, Yongsheng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Beijing China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Semantic Segmentation

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Adaptive Unscented Kalman Filter-based Disturbance Rejection With Application to High Precision Hydraulic Robotic Control

Adaptive Unscented Kalman Filter-based Disturbance Rejection...

引用

2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Peng Lu Timothy Sandy Jonas Buchli Interdisciplinary Division of Aeronautical and Aviation Engineering Hong Kong Polytechnic University HKSAR China Agile & Dexterous Robotics Lab Institute of Robotics and Intelligent Systems ETH Zürich Switzerland

This paper presents a novel nonlinear disturbance rejection approach for high precision model-based control of hydraulic robots. While most disturbance rejection approaches make use of observers, we propose a novel adaptive Unscented Kalman Filter to estimate the disturbances in an unbiased minimum-variance sense. The filter is made adaptive such that there is no need to tune the covariance matrix for the disturbance estimation. Furthermore, whereas most model-based control approaches require the linearization of the system dynamics, our method is nonlinear which means that no linearization is required. Through extensive simulations as well as real hardware experiments, we demonstrate that our proposed approach can achieve high precision tracking and can be readily applied to most robotic systems even in the presence of uncertainties and external disturbances. The proposed approach is also compared to existing approaches which demonstrates its superior tracking performance.

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Fuzzy Adaptive Control-based Real-time Obstacle Avoidance under Uncertain Perturbations

Fuzzy Adaptive Control-based Real-time Obstacle Avoidance un...

引用

International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Yingbai Hu Guang Chen Longbin Zhang Hang Su Mengyao Li Yunus Schmirander Hu Cao Alois Knoll Chair of Robotics Artificial Intelligence and Realtime Systems Technical University of Munich Munich Germany College of Automotive Engineering Tongji University Shanghai China KTH MoveAbility Lab KTH Royal Institute of Technology Stockholm Sweden Politecnico di Milano Milano Italy Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Dynamic Movement Primitives (DMPs) framework is a powerful approach to imitate motor skills, which has outstanding characteristics, such as convergence to the goal position and good imitation performance. Considering complex motion scenes of manipulators, such as changing the goal position or adding obstacles, the original DMPs framework is not sufficient for the requirements. In this paper, we propose a learning control-based hierarchical control strategy to adapt to new goal positions and avoid obstacles: the high-level learning scheme is targeted at imitating the motor skill and generating the optimization trajectory for obstacle avoidance; the lower-level control scheme focuses on the safety and stability of the robot's movement with unknown disturbances. Firstly, the enhanced DMPs framework is presented to imitate the trajectory from human demonstrations, where the novel DMPs can adapt to new goal position with the changing goal, and avoid single or multiple obstacles. Then, the fuzzy adaptive control method is employed to control redundant manipulators, where the fuzzy logic system (FLS) is incorporated to approximate an unknown nonlinear function term of the unknown disturbance. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with simulation results. The results show that the developed hierarchical strategy has good performance for new goal adaptation and obstacle avoidance.

关键词： Collision avoidance Trajectory Manipulator dynamics Nonlinear dynamical systems Adaptive control

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Using robotics to train students for Industry 4.0

引用

IFAC-PapersOnLine 2019年第9期52卷 153-158页

作者： Elisa Tosello Nicola Castaman Emanuele Menegatti Intelligent Autonomous Systems Lab (IAS-Lab) Department of Information Engineering University of Padova Via Gradenigo 6/B 35131 Padova Italy IT+Robotics srl Contrà Valmerlara 21 36100 Vicenza Italy

This paper presents the master course on Autonomous robotics that we offer at the School of Engineering of the University of Padova (Italy). Its novelty is the assignment of a lab project carefully designed to train students on autonomous and industrial robotics in the framework of Industry 4.0: the “Industry 4.0 robotics Challenge”. Students have to program both a manipulator and a mobile robot, together with a 3D vision system, in order to collaborate in the fulfillment of a pick-place-transport industrial task. We adopt a constructionist approach: project-based learning and team-based learning are applied to robotics and Industry 4.0. The project is organized as a challenge to motivate students to propose innovative ideas. A survey on students’ satisfaction is reported at the end of the paper. We made the description of both the hardware and software setup, together with tutorials and wikis, publicly available to let other robotics instructors replicate our proposal and make it a point of reference for teaching robotics in the frame of Industry 4.0.

关键词： Education robotics ROS Industry 4.0 Robotic manipulation Robot navigation

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Stairs reconstruction with 3D point cloud for gait generation of lower limb exoskeleton robot

Stairs reconstruction with 3D point cloud for gait generatio...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Feng, Yachun Xia, Linqing He, Yong Wang, Can Yan, Zefeng Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

With the aging of the population, a considerable number of elderly persons suffer from walking disability. Lower limb exoskeleton robots have been proved to be effective in rehabilitation training and walking assistance. Though significant advancement has been made in the last decade, lacking the ability of adapting to complex terrain is one of the main challenges that restrict the wide application of exoskeleton robots. Thus we focus on the environmental perception and corresponding gait pattern generation of lower limb assistant exoskeleton. In this paper, we introduce a method to generate gait of exoskeleton walking on a stair, which applies the L-0 minimization-based acquisition and reconstruction of stairs information through 3D Point Cloud. The results of experiments show that our method can precisely measure the physical dimension information of stairs. © 2019 IEEE.

关键词： Stairs

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