Corrigendum to “moment arms and musculotendon lengths estimation for a three-dimensional lower-limb model”

Survey/review study
Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function

Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,*


1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom
2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States
* Correspondence: Mengjie.Huang@liverpool.ac.uk
 
 
Received: 12 October 2022
Accepted: 28 November 2022
Published: 22 December 2022
 

Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.

/pdf/deep-common-spatial-pattern-based-motor-imagery-m1vdryma.pdf

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Biomechanics of the hip joint

Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.

/pdf/semi-supervised-transfer-learning-method-for-bearing-fault-te3wnqtc.pdf

Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data

In order to improve the quality of human walking, the present paper is analyzing a 3D virtual model for an orthotic device which is subjected to an optimization process based on dimensional, geometric and material criteria. For geometric modeling of the human knee joint were used the embedded applications: DesignModeler, SpaceClaim under AnsysWorkbench software package and for the orthotic device it was used ProEngineer application. The orthotic device is designed to rehabilitate people with knee problems, especially for the osteoarthritis affection. The geometric model of the joint shows a 50 tilt in varus, the joint beeing affected by osteoarthritis in the medial compartment. Using Ansys simulation environment, the virtual assembly joint orthois has been subjected to a nonlinear analysis based on the contacts and on the materials used. The proposed orthotic device seeks to improve the quality of walking by minimizing the loads from the knee joint, also on cartilage and on the menisci. Finally there are beeing compared the results obtained for normal knee, OA knee and OA knee-orthosis assemblies.

Modeling, Simulation and Optimization of a Human Knee Orthotic Device

Virtual reality (VR) has emerged as a powerful tool for rehabilitation. Many effective VR applications have been developed to support motor rehabilitation of people affected by motor issues. Movement reproduction, which transfers users’ movements from the physical world to the virtual environment, is commonly used in VR rehabilitation applications. Three major components are required for movement reproduction in VR: (1) movement input, (2) movement representation, and (3) movement modulation. Until now, movement reproduction in virtual rehabilitation has not yet been systematically studied. This article aims to provide a state-of-the-art review on this subject by focusing on existing literature on immersive motor rehabilitation using VR. In this review, we provided in-depth discussions on the rehabilitation goals and outcomes, technology issues behind virtual rehabilitation, and user experience regarding movement reproduction. Similarly, we present good practices and highlight challenges and opportunities that can form constructive suggestions for the design and development of fit-for-purpose VR rehabilitation applications and can help frame future research directions for this emerging area that combines VR and health.

Survey of Movement Reproduction in Immersive Virtual Rehabilitation

Head-mounted augmented reality (AR) technology is currently employed in upper limb motor rehabilitation, and the degrees of freedom (DoF) of virtual object translation modes become critical for rehabilitation tasks in AR settings. Since motor performance is the primary focus of motor rehabilitation, this study assessed it across different translation modes (1DoF and 3DoF) via task efficiency and accuracy analysis. In addition, eye movement characteristics were used to further illustrate motor performance. This research revealed 1DoF and 3DoF modes showing their own benefits in upper limb motor rehabilitation tasks. Finally, this study recommended selecting or integrating these two translation modes for future rehabilitation task design.

Exploring Virtual Object Translation in Head-Mounted Augmented Reality for Upper Limb Motor Rehabilitation with Motor Performance and Eye Movement Characteristics

Brain–computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain’s electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods.

/pdf/an-unsupervised-deep-transfer-learning-based-motor-imagery-3ijeezdo.pdf

An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain–Computer Interface

Virtual movement augmentation, which refers to the visual amplification of remapped movement, shows potential to be applied in motion-related virtual reality programs. Sense of agency (SoA), which measures the user's feeling of control in their action, has not been fully investigated for augmented movement. This study investigated the effect of augmented movement at three different levels (baseline, medium, and high) on users' SoA using both subjective responses and electroencephalography (EEG). Results show that SoA can be boosted slightly at medium augmentation level but drops at high level. The augmented virtual movement only helps to enhance SoA to a certain extent.

Meng Huang

Papers

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Survey of Movement Reproduction in Immersive Virtual Rehabilitation

Exploring Virtual Object Translation in Head-Mounted Augmented Reality for Upper Limb Motor Rehabilitation with Motor Performance and Eye Movement Characteristics

An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain–Computer Interface

Movement Augmentation in Virtual Reality: Impact on Sense of Agency Measured by Subjective Responses and Electroencephalography