A Novel Active Rehabilitation Model for Stroke Patients Using Electroencephalography Signals and Deep Learning Technology

A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies

Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance the accuracy and effectiveness of clinical decision-making and treatment outcomes. Nevertheless, it is still a rather innovative field of application of these techniques and there is a need to find what aspects are highly developed and what possible job niches can be exploited. This systematic review aims to evaluate the current state of research on the use of a particular AI called deep learning models in physiotherapy and identify the key trends, challenges, and opportunities in this field. The findings of this review, conducted following the PRISMA guidelines, provide valuable insights for researchers and clinicians. The most relevant databases included were PubMed, Web of Science, Scopus, Astrophysics Data System, and Central Citation Export. Inclusion and exclusion criteria were established to determine which items would be considered for further review. These criteria were used to screen the items during the first and second peer review processes. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, of the 214 initial papers, 23 studies were selected. From our analysis of the selected articles, we can draw the following conclusions: Concerning deep learning models, innovation is primarily seen in the adoption of hybrid models, with convolutional models being extensively utilized. In terms of data, it's unsurprising that body signals and images are predominantly used. However, texts and structured data present promising avenues for groundbreaking work in the field. Additionally, medical tests that involve the collection of 3D images, Functional Movement Screening, or thermographies emerge as novel areas to explore applications within the scope of physiotherapy.

Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces

Introduction

Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.

Methods

Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.

Results and discussion

Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.

High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation

Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.

Construction of Digital Platform of Religious and Cultural Resources Using Deep Learning and Its Big Data Analysis

This article analyzes the difficulties associated with the preservation and transmission of religious cultural resources and the difficulties encountered in the new development environment and background. It does so in light of the current state of religious, cultural resources. The protection, growth, and use of religious and cultural resources against the backdrop of the digital era are elaborated upon and critically analyzed in this article. Based on the foregoing discussion, this article conducts a thorough analysis of the development of a digital platform for religious and cultural resources and its big data analysis, and it also suggests an image feature extraction algorithm based on DL. This article develops a clustering CNN based on the network with PCA vector as convolution kernel, which clusters small images and computes principal component vectors according to categories, generating multiple groups of convolution kernels to extract more features so that the input image can select feature extractors adaptively. Simulation and comparative analysis are used in this article to confirm the algorithm's effectiveness. Compared to the conventional NN algorithm, simulation results indicate that this algorithm is more accurate, with a maximum accuracy of about 95.14 percent. It has some reference value for the research that will be done in relation to the creation of the next digital platform for religious and cultural resources.

Prediction Model of Piano Collective Class Teaching and Learning Effect Based on Deep Learning

This paper proposes a prediction model of piano collective class teaching and learning effect based on DL network in order to realise the precise prediction and evaluation of piano collective class instructional effect and promote the improvement of piano collective class instructional quality. The idea of an instructional assessment index is quantified in this paper using specific data as its input and educational impact as its output. In parallel, several training networks are established to correspond to the first-level evaluation indexes, and the input samples are normalised. Finally, MATLAB performs the empirical research. According to the findings, this method’s prediction accuracy can reach 94.41 percent, which is about 10.22 percent higher than that of conventional methods. This prediction model is somewhat realistic and feasible. When used to predict and assess instructional quality, this method not only eliminates the subjectivity of experts in the evaluation process but also yields satisfactory evaluation outcomes and has a broad range of applications. According to the model’s predictions and evaluation findings in this paper, appropriate teachers can better understand the drawbacks of the collective class model, focus on some important aspects of teaching activities, and then enhance instructional methods and effects.

Construction of Bilingual Teaching Mode Based on Digital Twinning Technology

Based on the digital twin technology, a digital twin platform can be built to connect the real teaching space with the virtual teaching space and become the mainstream of online teaching space. All this has determined that the social demand for designers' education is undergoing fundamental changes. The so-called "scientific and technological progress, education first" bilingual education is undergoing comprehensive and profound changes in the digital age, which has a strong impact on the traditional bilingual teaching mode and concept. Traditional concepts, aging theoretical knowledge, and backward teaching methods will inevitably be eliminated and updated gradually in the contest with digitalization, which makes it necessary to transform traditional bilingual education into digital bilingual education. Through the comparative experimental analysis of the teaching effect, the independent sample t-test shows that the t-statistic is 3.634, and the corresponding significance level is 0.013, which is less than 0.05. It shows that there are significant differences between boys and girls in bilingual teaching in this class of digital twin technology experimental teaching. However, compared with the control class, the results of both boys and girls are higher, so to some extent, it shows that the application of digital twin technology experimental teaching in bilingual teaching will indeed produce certain results.

Optimization Model of Mathematics Instructional Mode Based on Deep Learning Algorithm

This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper's evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality.

Dance Fitness Action Recognition Method Based on Contour Image Spatial Frequency Domain Features and Few-Shot Learning

In recent years, the research work of artificial intelligence technology has progressed rapidly, and various classic Few-Shot learning models have achieved unprecedented success in many artificial intelligence application fields. These include face recognition, object classification detection and tracking, speech recognition, and natural language processing, which greatly facilitate our lives. This paper aims to identify dance fitness movements based on contour image spatial frequency domain features and Few-Shot learning technology. This paper proposes a Few-Shot learning method based on contrastive average loss for Few-Shot learning. This method makes the learned model more representative by improving the loss function and performing a normalization process, and it proposes a feature extraction algorithm that combines improved LBP and HOG for action recognition technology. The experimental results show that the recognition accuracy of the algorithm in this paper is 93.10%, 90.30%, and 92.70% for walking, opening hands, and running, respectively. This illustrates the effectiveness of the fusion feature algorithm.