Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Download Full-text

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Download Full-text

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽

10.3390/s21030863 ◽

2021 ◽

Vol 21 (3) ◽

pp. 863

Author(s):

Vidas Raudonis ◽

Agne Paulauskaite-Taraseviciene ◽

Kristina Sutiene

Keyword(s):

Deep Learning ◽

Image Fusion ◽

Early Stage ◽

Image Data ◽

Cell Detection ◽

Processing Times ◽

Fused Image ◽

Stage Embryo ◽

Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

Download Full-text

Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽

10.3390/s21093279 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3279

Author(s):

Maria Habib ◽

Mohammad Faris ◽

Raneem Qaddoura ◽

Manal Alomari ◽

Alaa Alomari ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Quality Assessment ◽

Transcript Level ◽

Assessment Process ◽

Classification Model ◽

Systematic Evaluation ◽

Transcript Levels ◽

Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

Download Full-text

Rehabilitation training based on virtual reality for patients with Parkinson’s disease in improving balance, quality of life, activities of daily living, and depressive symptoms: A systematic review and meta-regression analysis

Clinical Rehabilitation ◽

10.1177/0269215521995179 ◽

2021 ◽

pp. 026921552199517

Author(s):

Runze Li ◽

Yanran Zhang ◽

Yunxia Jiang ◽

Mengyao Wang ◽

Wei How Darryl Ang ◽

...

Keyword(s):

Quality Of Life ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Virtual Reality ◽

Depressive Symptoms ◽

Activities Of Daily Living ◽

Daily Living ◽

Meta Analysis ◽

Rehabilitation Training

Objective: To examine the effectiveness of rehabilitation training based on virtual reality in improving balance, quality of life, activities of daily living, and depressive symptoms of patients with Parkinson’s disease. Data sources: PubMed, EMBASE, CINAHL, Scopus, Cochrane Library, PsycINFO, ProQuest, Physiotherapy Evidence Database, IEEE Xplore, China National Knowledge Infrastructure, Wanfang, and VIP Information databases were searched from their inception to October 15, 2020. Trial registries, gray literature, and target journals were also searched. Methods: Eligible randomized controlled trials included studies with patients with Parkinson’s disease in rehabilitation training based on virtual reality. Comprehensive Meta-Analysis 3.0 software was used. Physiotherapy Evidence Database Scale and the Grading of Recommendation, Assessment, Development, and Evaluation system were used to assess the methodological quality of individual trials and the overall quality of the evidence, respectively. Results: A total of 22 randomized controlled trials with 836 patients were included. Meta-analysis revealed that training significantly improved balance ( g = 0.66, P < 0.001), quality of life ( g = 0.28, P = 0.015), activities of daily living ( g = 0.62, P < 0.001), and depressive symptoms ( g = 0.67, P = 0.021) compared to the control group. Subgroup analysis indicated that training should utilize video game consoles. Meta-regression analyses showed that age, sessions, and frequency of training had statistically significant impacts on balance scores. Quality of individual trials was high and overall evidence ranged from very low to low. Conclusion: Virtual rehabilitation training could be adopted in healthcare institutions as supplementary training for patients with Parkinson’s disease.

Download Full-text

Improving Image Quality of Single Plane Wave Ultrasound via Deep Learning Based Channel Compounding

2020 IEEE International Ultrasonics Symposium (IUS) ◽

10.1109/ius46767.2020.9251322 ◽

2020 ◽

Author(s):

Sven Rothlubbers ◽

Hannah Strohm ◽

Klaus Eickel ◽

Jurgen Jenne ◽

Vincent Kuhlen ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Plane Wave ◽

Single Plane

Download Full-text

Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine

Oral Radiology ◽

10.1007/s11282-021-00538-2 ◽

2021 ◽

Author(s):

Mizuho Mori ◽

Yoshiko Ariji ◽

Motoki Fukuda ◽

Tomoya Kitano ◽

Takuma Funakoshi ◽

...

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Classification Performance ◽

Learning Systems ◽

Learning Technology ◽

System 2 ◽

Potential Benefits ◽

System 1 ◽

Periapical Radiography

Abstract Objectives The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. Methods We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems. Results The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927). Conclusions The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.

Download Full-text

A novel two-stage method of plant seedlings classification based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211507 ◽

2021 ◽

pp. 1-11

Author(s):

Tianhong Dai ◽

Shijie Cong ◽

Jianping Huang ◽

Yanwen Zhang ◽

Xinwang Huang ◽

...

Keyword(s):

Deep Learning ◽

Learning Technology ◽

Two Stage ◽

Second Stage ◽

Stage Classification ◽

Different Types ◽

Two Stages ◽

Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

Download Full-text