Body Joints Research Articles

“A CASE STUDY ON AGNIKARMA W.S.R. QUADRICEPS TENDON”

The knee joint is one of the most heavily stressed joints in the human body, exhibiting a unique combination of stability and mobility. Its stability primarily relies on the quadriceps femoris and collateral ligaments. Unfortunately, due to the rising incidents of accidents and sports-related injuries, knee tendon injuries are becoming increasingly prevalent, with quadriceps tendon injuries being particularly common. Surgical reconstruction, often pursued for such injuries, does not always ensure a full return to previous levels of activity. The quadriceps tendon, composed of tendinous portions from the rectus femoris, vastus intermedius, vastus lateralis, and vastus medialis muscles, plays a crucial role in knee function. These muscle fibers blend with the tendinous portion approximately 3 cm proximal to the superior border of the patella. In this single case study, an attempt was made to address a knee tendon injury using Ayurvedic treatment methods. A 38-year-old female suffering from a quadriceps tendon injury received treatment involving Agni Karma (therapeutic cauterization) and internal medicine. The treatment spanned over a period of 15 days, during which significant relief from pain was achieved, accompanied by noticeable improvement in knee joint mobility

Fabrication of a high performance flexible capacitive porous GO/PDMS pressure sensor based on droplet microfluidic technology.

Porous structures are an effective way to improve the performance of flexible capacitive sensors, but the pore size uniformity of porous structures is not easily controlled by current methods, which may affect the inconsistent performance of different batches of sensors. In this paper, a high performance capacitive flexible porous GO/PDMS pressure sensor was prepared based on droplet microfluidic technology. By testing the performance of the sensor, we found that the sensor with a flow rate ratio of 1 : 3 has relatively good performance, with a degree of hysteresis (DH) of 8.64% and a coefficient of variation (CV) of 5.2%. Therefore, we studied the sensor performance based on this process. The result shows that the sensitivity of the flexible capacitive porous GO/PDMS pressure sensor reached 0.627 kPa-1 at low pressure (0-3 kPa), which is significantly higher than that of the pure PDMS thin film sensor (about 0.031 kPa-1) and the porous PDMS pressure sensor (0.263 kPa-1). At the same time, the sensor has a large range with a fast response time of 240 ms and a relaxation time of 300 ms at 30 kPa and an ultra-low detection limit (70 Pa). It can maintain stable operation under continuous force loading/unloading cycles and can respond well to different pressure step changes, so the sensor can be used to detect the movement process of each finger, knee, foot and other joints of the human body. In conclusion, the droplet microfluidic technology can effectively prepare high-performance capacitive flexible porous GO/PDMS pressure sensors.

A study on health issues of leather industry workers

Leather manufacturing company workers faced so many health issues. This study explores to investigate the health issues faced by workers in a leather manufacturing company located in Vaniyambadi District. Through a comprehensive assessment of the workers' health, including surveys, interviews, and analysis of medical records, the study identifies the prevalent health concerns and explores potential contributing factors. The findings reveal that workers in the leather manufacturing company experience a range of health issues, with body ache, joint pain, and headache being the most common problems reported. Other health issues identified include teeth problems, irritation in feet, urinary tract discomfort, giddiness, eye irritation, chest irritation, stomach discomfort, and various other symptoms. The study assesses the work environment and job tasks to identify potential occupational health risks that may contribute to the observed health issues. Factors such as long working hours, exposure to hazardous substances, ergonomic challenges, and inadequate safety measures are examined. Based on the study findings, recommendations are provided to address the identified health issues. These include implementing ergonomic improvements, enhancing health and safety training programs, providing personal protective equipment, and fostering a culture of health and well-being within the organization. The results of this study contribute to the understanding of health issues among workers in the leather manufacturing industry, providing valuable insights for the company and relevant stakeholders to enhance occupational health and improve worker well-being.

Self-Adaptive Graph With Nonlocal Attention Network for Skeleton-Based Action Recognition.

Graph convolutional networks (GCNs) have achieved encouraging progress in modeling human body skeletons as spatial-temporal graphs. However, existing methods still suffer from two inherent drawbacks. Firstly, these models process the input data based on the physical structure of the human body, which leads to some latent correlations among joints being ignored. Furthermore, the key temporal relationships between nonadjacent frames are overlooked, preventing to fully learn the changes of the body joints along the temporal dimension. To address these issues, we propose an innovative spatial-temporal model by introducing a self-adaptive GCN (SAGCN) with global attention network, collectively termed SAGGAN. Specifically, the SAGCN module is proposed to construct two additional dynamic topological graphs to learn the common characteristics of all data and represent a unique pattern for each sample, respectively. Meanwhile, the global attention module (spatial attention (SA) and temporal attention (TA) modules) is designed to extract the global connections between different joints in a single frame and model temporal relationships between adjacent and nonadjacent frames in temporal sequences. In this manner, our network can capture richer features of actions for accurate action recognition and overcome the defect of the standard graph convolution. Extensive experiments on three benchmark datasets (NTU-60, NTU-120, and Kinetics) have demonstrated the superiority of our proposed method.

GUESS: GradUally Enriching SyntheSis for Text-Driven Human Motion Generation.

In this paper, we propose a novel cascaded diffusion-based generative framework for text-driven human motion synthesis, which exploits a strategy named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy sets up generation objectives by grouping body joints of detailed skeletons in close semantic proximity together and then replacing each of such joint group with a single body-part node. Such an operation recursively abstracts a human pose to coarser and coarser skeletons at multiple granularity levels. Notably, we further integrate GUESS with the proposed dynamic multi-condition fusion mechanism to dynamically balance the cooperative effects of the given textual condition and synthesized coarse motion prompt in different generation stages. Extensive experiments on large-scale datasets verify that GUESS outperforms existing state-of-the-art methods by large margins in terms of accuracy, realisticness, and diversity. Please refer to the supplemental demo video for more visualizations.

Detection of mild cognitive impairment using various types of gait tests and machine learning.

Alzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration. This study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions. The study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis-a key behavioral pattern-with machine learning, offering a practical tool for MCI screening in both clinical and home environments.

Concurrent Validity of Motion Parameters Measured With an RGB-D Camera-Based Markerless 3D Motion Tracking Method in Children and Young Adults.

Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults. Twenty-three typically developing children and healthy young adults (5-29 years, 110-189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson's correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness. Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm. Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis. The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard.

Multi-View Time-Series Hypergraph Neural Network for Action Recognition.

Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series Hypergraph Neural Network (MV-TSHGNN) method. The framework is composed of two main parts: the construction of a multi-view time-series hypergraph structure and the learning process of multi-view time-series hypergraph convolutions. Specifically, given the multi-view video sequence frames, we first extract the joint features of actions from different views. Then, limb components and adjacent joints spatial hypergraphs based on the joints of different views at the same time are constructed respectively, temporal hypergraphs are constructed joints of the same view at continuous times, which are established high-order semantic relationships and cooperatively generate complementary action features. After that, we design a multi-view time-series hypergraph neural network to efficiently learn the features of spatial and temporal hypergraphs, and effectively improve the accuracy of skeleton-based action recognition. To evaluate the effectiveness and efficiency of MV-TSHGNN, we conduct experiments on NTU RGB+D, NTU RGB+D 120 and imitating traffic police gestures datasets. The experimental results indicate that our proposed method model achieves the new state-of-the-art performance.

Comparison between patients with COPD and healthy subjects on spatiotemporal, moment and kinematic parameters: A quasi-experimental study.

Chronic obstructive respiratory diseases (COPD) not only cause damage to the respiratory system as well as the heart and blood vessels of the patient but also have a direct effect on the condition of the musculoskeletal system. The risk of falling is increasing due to dysfunction of the joints as well as aging, which occurs frequently in this population. Gait deficits are known as an important risk factor for falls. This research aimed to investigate the gait of COPD patients compared to healthy people to gain a better understanding of the reasons for falls. Twenty patients with COPD and 20 age and BMI-matched healthy individuals were included in this study. Sixteen markers were applied to the lower body of the subjects. Spatio-temporal, kinematic, and maximum moment parameters were measured in different phases in three lower body joints, including the hip, knee, and ankle. The results showed that all spatio-temporal parameters in patients were significantly lower than in healthy people. The ankle angle in the sagittal plane at initial contact was significantly difference (p = 0.03). As well as, in the frontal plane the hip angle in the mid-stance showed a significant difference (p = 0.02). There was also a significant difference in maximum hip moment in the sagittal plane between the two groups (p = 0.01). The larger hip angle of the patients can be related to the balance problems in the mediolateral direction. The moment showed a significant difference in the hip joint. Since the hip muscles are directly in a synergistic relationship with the trunk muscles, it seems the performance of these muscles is likely to be seriously damaged due to respiratory diseases.

Multi-Person Pose Tracking With Sparse Key-Point Flow Estimation and Hierarchical Graph Distance Minimization.

In this paper, we propose a novel framework for multi-person pose estimation and tracking on challenging scenarios. In view of occlusions and motion blurs which hinder the performance of pose tracking, we proposed to model humans as graphs and perform pose estimation and tracking by concentrating on the visible parts of human bodies which are informative about complete skeletons under incomplete observations. Specifically, the proposed framework involves three parts: (i) A Sparse Key-point Flow Estimating Module (SKFEM) and a Hierarchical Graph Distance Minimizing Module (HGMM) for estimating pixel-level and human-level motion, respectively; (ii) Pixel-level appearance consistency and human-level structural consistency are combined in measuring the visibility scores of body joints. The scores guide the pose estimator to predict complete skeletons by observing high-visibility parts, under the assumption that visible and invisible parts are inherently correlated in human part graphs. The pose estimator is iteratively fine-tuned to achieve this capability; (iii) Multiple historical frames are combined to benefit tracking which is implemented using HGMM. The proposed approach not only achieves state-of-the-art performance on PoseTrack datasets but also contributes to significant improvements in other tasks such as human-related anomaly detection.

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

Female Body's in Advertisements: A Foucauldian Analysis

This research study examines the articulation of the female body in advertisements through the intersecting lenses of Foucauldian perspectives. In the context of contemporary consumer culture where advertisements play a pivotal role in shaping social perceptions of gender and beauty, this research seeks to unravel the complex web of power, discourse, and commodification regarding the portrayal of women's bodies. The analysis delves into how capitalism commodifies the female body, turning it into a product to circulate in the market. The study investigates the economic relations and labor conditions involved in the production of these images, shedding light on the exploitative mechanisms within the advertising industry. It also explores how consumerism and the pursuit of profit contribute to the perpetuation of idealized beauty standards. Simultaneously, drawing from Foucault's theories, the research investigates the ways in which power operates within the discourse of advertisements. It uncovers how advertising normalizes specific beauty ideals, imposes disciplinary domains regimes, and invites viewers to engage in a surveillance-based gaze that judges and categorizes female bodies. This study revealed that advertisements commodify the female body, reducing women to objects that can be bought and sold, perpetuating unrealistic beauty standards and creating a culture where women feel alienated from their own bodies, all driven by the profit motive of corporations and reinforcing the capitalist notion that everything, including beauty, has a price. By elucidating these dynamics, the research contributes to a critical discourse on the role of advertising in reinforcing or challenging gender norms and the broader socio-economic system.

An analysis of the relationship between dietary pattern changes and temporomandibular joint inflammation in diabetic rats.

The temporomandibular joint (TMJ) is the most commonly used joint in the human body. Recent studies have shown pathologic relationships between inflammation, diabetes, and musculoskeletal disorders (MSDs). Chewing disorder is a significant sign of dysfunction in the masticatory system. This study investigated dietary pattern changes in response to TMJ inflammation in diabetic rats. This experimental study was carried out on 30 male rats. The rats were fed concentrated 20-mg dietary tablets. Complete Freund's adjuvant (CFA) was used to induce TMJ inflammation and streptozotocin (STZ) was used to induce diabetes. The animals were randomly divided into three groups (n=10), including group I (CFA+STZ), group II (healthy rats+CFA), and group III (healthy rats, no injection). Parameters such as overall food intake, food intake duration, food intake frequency, and the interval between meals were recorded in a checklist and analyzed by Mann-Whitney and Kruskal-Wallis tests (P<0.05). The results showed no significant difference between groups in overall food intake and food intake frequency on days 0 and 1, but this difference was significant from day 2 to day 7. Regarding the time and end of food intake, there was a significant difference between the three groups from day 1 to day 7, but this difference was not significant on day zero. Dietary pattern changes were similar in the diabetic TMJ inflammation and TMJ inflammation groups. These changes can be used as a behavioral marker for TMJ inflammation in rats.

Multi-supervision transformer combining bounding box and mask for data-limited pose estimation

In scenarios where acquiring accurate annotated pose data is difficult, obtaining sufficient training samples becomes a challenge. Given these data constraints, it is challenging to provide optimal end-to-end supervision with limited pose information. To address this limitation, we propose a novel approach, named MSPose, which combines diverse data sources from human instances to provide multi-level supervision in human pose estimation. This approach mitigates the impact of scarce training data and enhances pose estimation precision by synergistically incorporating human bounding box and mask information. Our proposed method achieves multi-level model activation for the human body region, structure, and joints. This hierarchical pixel activation is facilitated by integrating human bounding box and mask signals as supplementary supervision during training. Importantly, we exclude auxiliary branches to ensure complete freedom during inference. Our experimental validation on a quarter of the COCO and MPII datasets demonstrates the competitiveness of our method compared to state-of-the-art methods. Compared to TokenPose-T, our MSPose-T achieves a 6.1 points improvement in average precision and a corresponding 3.1 points increase on the full COCO validation set. On the MPII dataset, MSPose achieves the highest Mean score of 1.9 points. Our approach achieves these improvements while maintaining consistent parameters and GFLOPs, highlighting the advantage of our approach.

Usage of human diatomaceous earth

Diatomaceous earth is naturally formed sedimentary mineral rock resulting from the remains of diatoms, or oceanic unicellular algae. It is extracted from diatoms mined from deposits of diatomite and manufactured into a powder that is diatomaceous earth for human use, as a natural compound. It is gaining popularity as having an outstanding list of health benefits and non-toxic, no-known side effect origins. In this paper, a comprehensive review is presented to highlight the availability, benefits, mechanism of operation, and effects on the use of Human Diatomaceous Earth. The review revealed that diatomaceous earth has an outstanding list of health benefits and non-toxic, no-known side effects. It has the ability to lower the concentration of cholesterol in the blood. It is a very essential mineral for tendons, cartilage, blood vessels, and bones. Diatomaceous Earth is also considered to greatly help the body in getting rid of toxic substances such as heavy metals. It improves body joints bones, and ligaments through silica, which is the highest-occurring compound in diatomaceous earth that aids in the metabolism and formation of the joints.

Research on the Innovation of Physical Education and Physical Education Teaching Based on Big Data Analysis

Abstract In this paper, Openpose is used to process the sports teaching video to get the coordinates data of the human body joint point positions in each frame of the video, and Kalman filter data fusion is used to establish the human skeleton model. According to the results of the division of the five major parts of the human body, after establishing the limb vectors of the human body’s torso, left arm, right arm, left leg, and right leg in three-dimensional spatial coordinates, the distances between the joints of the five human body skeletons based on the DTW posture matching algorithm were used to extract the characteristics of the sports error technical movements. From the demand of sports digital teaching, the design and implementation of sports basic movement teaching evaluation system based on the DTW posture matching algorithm, and the research and analysis of sports teaching under the background of big data. The results show that the IoU values of batting action localization in 6 segments of physical education teaching are 85.6%, 91.6%, 77.7%, 75.1%, 87.4% and 77.7%, respectively, and the average reach 82.5%, i.e., it shows that the research on action localization and recognition based on the DTW posture matching algorithm has a good performance. In the assessment of movement standardization in physical education, the maximum moment of stretching angle corresponds to the moment of hitting the ball, and its value reaches 3.79, i.e., it reflects that the evaluation system of physical education basic movement teaching can accurately determine whether the students’ movements are accurate or not, and make timely feedbacks to carry out the corrections of physical education movements. This study has the potential to enhance students’ interest and performance in sports and contribute to the advancement of digital sports teaching.

SOCA-PRNet: Spatially Oriented Attention-Infused Structured-Feature-Enabled PoseResNet for 2D Human Pose Estimation.

In the recent era, 2D human pose estimation (HPE) has become an integral part of advanced computer vision (CV) applications, particularly in understanding human behaviors. Despite challenges such as occlusion, unfavorable lighting, and motion blur, advancements in deep learning have significantly enhanced the performance of 2D HPE by enabling automatic feature learning from data and improving model generalization. Given the crucial role of 2D HPE in accurately identifying and classifying human body joints, optimization is imperative. In response, we introduce the Spatially Oriented Attention-Infused Structured-Feature-enabled PoseResNet (SOCA-PRNet) for enhanced 2D HPE. This model incorporates a novel element, Spatially Oriented Attention (SOCA), designed to enhance accuracy without significantly increasing the parameter count. Leveraging the strength of ResNet34 and integrating Global Context Blocks (GCBs), SOCA-PRNet precisely captures detailed human poses. Empirical evaluations demonstrate that our model outperforms existing state-of-the-art approaches, achieving a Percentage of Correct Keypoints at 0.5 (PCKh@0.5) of 90.877 at a 50% threshold and a Mean Precision (Mean@0.1) score of 41.137. These results underscore the potential of SOCA-PRNet in real-world applications such as robotics, gaming, and human-computer interaction, where precise and efficient 2D HPE is paramount.

PENGARUH PENYULUHAN BABY MASSAGE TERHADAP MINAT IBU DALAM MELAKSANAKAN BABY MASSAGE PADA BAYI USIA 0-36 BULAN

Growth and development in babies can be achieved optimally if the result of the interaction of various interrelated factors, for example genetic factors, environmental factors, behavioral factors and useful stimulus factors. Babies' gross motor and fine motor skills should be stimulated as early as possible, for example by doing baby massage. Baby Massage is very effective for eliminating fatigue and boredom in babies, swimming will stimulate motor movements in babies because the baby's muscles will develop very well, the body's joints will work optimally, body growth will increase and the body will become flexible. The aim of this research is to determine whether there is an influence of baby massage education on mothers' interest in implementing baby massage for babies aged 0-36 months. The method used is a quasi-experiment methodology with a one group pre and post test design. The results of the research on mothers' interest in implementing Baby Massage showed that most of the interest was high, namely High 20 (64.4%), medium 5 (16.17%), low 6 (19.4%). And mothers' interest in implementing Baby Massage after counseling. overall high 31 (100%). The Wilcoxon test obtained a p-value of 0.000 &lt;α0.05, so Hi was accepted. In conclusion, there is an influence of Baby Massage counseling on mothers' interest in implementing Baby Massage in Murukan Village, Mojoagung District, Jombang Regency. So from these results, it is important to do baby massage for babies aged 0-36 months

Development of automated region of interest selection algorithms for surface-guided radiation therapy of breast cancer.

To investigate automation of the preparation of the region of interest (ROI) for surface-guided radiotherapy (SGRT) of the whole breast with two algorithms based on contour anatomies: using the body contour, and using the breast contour. The patient dataset used for modeling consisted of 39 breast cancer patients previously treated with SGRT. The patient's anatomical structures (body and ipsilateral breast) were retrieved from the planning system, and the clinical ROI (cROI) drawn by the planners was retrieved from the SGRT system for comparison. For the body-contour-based algorithm, a convolutional neural network (MobileNet-v2) was utilized to train a synthetic human model dataset to predict body joint locations. With the body joint location knowledge, an automated ROI (aROIbody ) can be created based on: (1) the superior-inferior (S-I) borders defined by the joint locations, (2) the left-right (L-R) borders defined with 3/4 of chest width, and (3) a curation of the ROI to avoid the ipsilateral armpit. For the breast-contour-based algorithm, an aROIbreast was created by first defining the ROI in the S-I direction with the ipsilateral breast boundaries. Other steps are the same as with the body-contour-based algorithm. Among the 39 patients, 24 patients were used to fine-tune the algorithm parameters, and the remaining 15 patients were used to evaluate the quality of the aROIs against the cROIs. A blinded evaluation was performed by three SGRT expert physicists to rate the acceptability and the quality (1-10 scale) of the aROIs and cROIs, and the dice similarity coefficient (DSC) was also calculated to compare the similarity between the aROIs and cROIs. The results showed that the average acceptability was 14/15 (range: 13/15-15/15) for cROIs, 13.3/15 (range: 13/15-14/15) for aROIbody , and 14.6/15 (range: 14/15-15/15) for aROIbreast . The average quality was 7.4 ± 0.8 for cROIs, 8.1 ± 1.2 for aROIbody , and 8.2 ± 0.9 for aROIbreast . The DSC with cROIs was 0.81 ± 0.06 for aROIbody , and 0.83 ± 0.04 for aROIbreast . The ROI creation time was ∼120s for clinical, 1.3s for aROIbody , and 1.2s for aROIbreast . The proposed automated algorithms can improve the ROI compliance with the SGRT protocol, with a shortened preparation time. It is ready to be integrated into the clinical workflow for automated ROI preparation.

Depth-aware pose estimation using deep learning for exoskeleton gait analysis

In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human–machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients’ walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.