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Article
Computer Science, Theory & Methods
Jared Willard et al.
Summary: There is a growing consensus that solving complex science and engineering problems requires innovative methods that integrate traditional physics-based modeling with advanced machine learning techniques. This article offers a comprehensive overview of such techniques, summarizing their application areas and describing the methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks. Additionally, a taxonomy of existing techniques is provided, revealing knowledge gaps and potential crossovers between disciplines that can inspire future research.
ACM COMPUTING SURVEYS
(2023)
Article
Automation & Control Systems
Yihui Zhao et al.
Summary: This article proposes an adaptive cooperative control strategy for a wrist exoskeleton based on a real-time joint impedance estimation approach. By interpreting the underlying transformation in the muscular and skeletal systems, the proposed approach estimates the motion intention and the joint impedance of a human subject simultaneously without additional calibration procedures. Results indicate the proposed method outperforms other training protocols and enhances the training effectiveness and the interaction safety.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Biomedical
Moon Ki Jung et al.
Summary: This study compared the performance of surface EMG and intramuscular EMG in estimating the required joint torques for assistive devices. The results showed that there was a similar correlation between the experimental and predicted joint torques when using either surface or intramuscular EMG as input signals in both healthy individuals and SCI patients.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Article
Engineering, Biomedical
Kieran J. Bennett et al.
Summary: This study used a musculoskeletal modelling framework to compare different methods for estimating knee joint loading, and found that using EMG-informed modelling allows for better estimation of knee joint loading.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Sifan Wang et al.
Summary: This work investigates the Neural Tangent Kernel (NTK) of Physics-informed neural networks (PINNs) and demonstrates that it can converge to a deterministic kernel that remains constant during training under appropriate conditions. A novel gradient descent algorithm is proposed to adaptively calibrate the convergence rate of total training error using the eigenvalues of NTK. A series of numerical experiments are conducted to validate the theory and practical effectiveness of the proposed algorithms.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Statistics & Probability
Cynthia Rudin et al.
Summary: This work highlights the fundamental principles of interpretable machine learning and identifies 10 technical challenge areas in this field, including optimizing sparse models, scoring systems, and adding constraints for better interpretability. It serves as a useful starting point for statisticians and computer scientists interested in interpretable machine learning.
STATISTICS SURVEYS
(2022)
Article
Engineering, Biomedical
Phairot Autthasan et al.
Summary: Advances in motor imagery-based brain-computer interfaces (BCIs) allow control of multiple applications using electroencephalography (EEG) recordings. However, changes in EEG rhythms pose challenges to classification performance in a subject-independent manner. To tackle this problem, the researchers propose MIN2Net, a method that combines deep metric learning and multi-task autoencoder to improve classification performance.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Article
Engineering, Biomedical
Lahiru N. Wimalasena et al.
Summary: This study proposes a machine learning method to estimate the underlying neural command signal for muscle activation, and successfully applies it to experimental data from rats and monkeys. The method, called AutoLFADS, dynamically adjusts its frequency response and provides single-trial estimates that improve the prediction of joint kinematics and uncover new muscle oscillations. This approach has important implications for studying multi-muscle coordination, brain control, and improving myoelectric-based brain-machine interfaces.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Automation & Control Systems
Punnawish Thuwajit et al.
Summary: This study introduces a deep learning approach called EEGWaveNet for detecting seizures in epileptic patients via Electroencephalography (EEG). The proposed method utilizes a multiscale convolutional neural network to achieve high performance, speed, and subject-independence in seizure detection.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Tianzhe Bao et al.
Summary: This article provides a systematic review of recent achievements in the field of multi-functional human-machine interfaces. By exploring the fusion of multi-modal sensors, transfer learning methods, and post-processing approaches, the aim is to enhance the robustness, adaptability, and reliability of the models. Additionally, research challenges and emerging opportunities in hardware development, public resources, and decoding strategies are analyzed to provide perspectives for future developments.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Engineering, Biomedical
Jiacheng Weng et al.
Summary: An efficient inverse optimal control method called Adaptive Reference IOC is introduced for studying natural walking using musculoskeletal models. The method achieves about 7 times faster convergence compared to existing derivative-free methods while maintaining similar outcomes in terms of gait trajectory matching. The proposed method successfully reconstructs reference data when applied to experimental walking data and can provide guidance for personalized cost function optimization and reference trajectory design for assistive robotic systems.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Qingqing Li et al.
Summary: This article investigates the use of multichannel sEMG signals of hand gestures for user authentication. A new deep anomaly detection-based method is proposed, which employs sEMG images generated from multichannel sEMG signals. Among different sEMG image generation methods, the root mean square (rms) map achieves the best performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Souvik Chakraborty
Summary: This paper introduces a novel multi-fidelity physics informed deep neural network (MF-PIDNN) to address the challenge of analyses and design for systems where the governing differential equation is either not known or known in an approximate sense. MF-PIDNN blends physics informed and data-driven deep learning techniques by using transfer learning, providing accurate predictions even in zones with no data. The approach updates a low-fidelity model with available high-fidelity data, showcasing the effectiveness of transfer learning in solving multi-fidelity problems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Review
Automation & Control Systems
Dezhen Xiong et al.
Summary: This paper examines the role of deep learning in decoding EMG signals for human-machine interaction applications. It reviews recent advancements in network structures, processing schemes, and tasks like movement classification and joint angle prediction. The discussion also includes new challenges, such as multimodal sensing and robustness towards disturbances, and presents potential future research directions.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Information Systems
Xiang Chen et al.
Summary: This paper introduces an effective transfer learning strategy to improve gesture recognition accuracy and decrease training burden. The strategy performs well on different datasets, significantly enhancing recognition accuracy and greatly reducing training time.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Biophysics
William S. Burton et al.
Summary: The study developed and compared machine learning techniques for rapid, data-driven estimation of musculoskeletal metrics, accurately predicting joint and muscle forces of patients during daily activities using four different algorithms. This technology has the potential to broaden the impact of musculoskeletal modeling by enabling faster assessment in both clinical and research settings.
JOURNAL OF BIOMECHANICS
(2021)
Article
Multidisciplinary Sciences
Patrick A. K. Reinbold et al.
Summary: The study demonstrates that combining data-driven methods with physical principles can lead to the discovery of accurate models of non-equilibrium spatially extended systems from high-dimensional, noisy, and incomplete experimental data. This hybrid approach also allows for the reconstruction of inaccessible variables successfully.
NATURE COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Nannapas Banluesombatkul et al.
Summary: This study proposes a novel transfer learning framework MetaSleepLearner based on Meta-Learning (MAML), achieving a range of 5.4% to 17.7% improvement in comparison to the conventional approach. The model interpretation after adaptation to each subject confirms a reasonable learning performance, showcasing potential for human-machine collaboration in sleep stage classification and reducing clinicians' workload.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Wenjie Bao et al.
Summary: The article introduces two methods, synchrosqueezing transform (SST) and second-order synchroextracting transform (SET2), for improving the TF representation of nonstationary signals. While SST uses the GMLC signal model to accurately represent time-varying nonstationary signals, SET2 focuses on characterizing TF distribution using energy at the IF to reduce the impact of noise on the real signal.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Navaneethakrishna Makaram et al.
Summary: The measurement and analysis of electrical activity of muscle aid in controlling assistive devices. Muscle fatigue is associated with loss of muscle function and detection via sEMG is challenging due to nonlinear signal variations. This study aims to develop a reliable fatigue index based on dynamic nonlinear variations in signal characteristics, achieving an accuracy of 90% using machine learning algorithms.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Biomedical
Xiao Tang et al.
Summary: This study introduces a novel framework for interpreting motor unit (MU) activities and applying it to muscle force decoding. By characterizing the spatially distributed firing waveforms of MUs and utilizing a twitch force model, a deep network is designed to predict normalized force. Examination of MU category distribution to calibrate actual force levels shows the effectiveness of this framework in muscle force estimation.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Article
Engineering, Biomedical
Tianzhe Bao et al.
Summary: A novel regression scheme for supervised domain adaptation (SDA) was proposed to enhance inter-subject performances of CNN in wrist kinematics estimation by effectively reducing domain shift effects. The approach outperformed fine-tuning in both single-single and multiple-single scenarios of kinematics estimation, maintaining better performances in original domains and increasing model reusability among multiple subjects.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Review
Physics, Applied
George Em Karniadakis et al.
Summary: Physics-informed learning seamlessly integrates data and mathematical models through neural networks or kernel-based regression networks for accurate inference of realistic and high-dimensional multiphysics problems. Challenges remain in incorporating noisy data seamlessly, complex mesh generation, and addressing high-dimensional problems.
NATURE REVIEWS PHYSICS
(2021)
Article
Engineering, Biomedical
Keun-Tae Kim et al.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2020)
Article
Computer Science, Interdisciplinary Applications
Yuta Hiasa et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2020)
Article
Engineering, Mechanical
Somdatta Goswami et al.
THEORETICAL AND APPLIED FRACTURE MECHANICS
(2020)
Article
Biotechnology & Applied Microbiology
Arne De Brabandere et al.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2020)
Article
Biotechnology & Applied Microbiology
Claudio Pizzolato et al.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2020)
Article
Automation & Control Systems
Jie Zhang et al.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2020)
Article
Automation & Control Systems
Jie Zhang et al.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2020)
Article
Biotechnology & Applied Microbiology
Marleny M. Arones et al.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2020)
Article
Engineering, Biomedical
Yihui Zhao et al.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2020)
Article
Computer Science, Interdisciplinary Applications
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Yibo Yang et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Engineering, Biomedical
Lance Rane et al.
ANNALS OF BIOMEDICAL ENGINEERING
(2019)
Article
Computer Science, Interdisciplinary Applications
Tien Tuan Dao
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2019)
Article
Engineering, Biomedical
Weiming Wang et al.
Proceedings Paper
Neurosciences
Massimo Sartori et al.
CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION II, VOLS 1 AND 2
(2017)
Article
Engineering, Biomedical
Massimo Sartori et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2016)
Article
Multidisciplinary Sciences
Giordano Valente et al.
