scholarly journals Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1909
Author(s):  
Guillermo Díaz-San Martín ◽  
Luis Reyes-González ◽  
Sergio Sainz-Ruiz ◽  
Luis Rodríguez-Cobo ◽  
José M. López-Higuera
Keyword(s):  
Data Extraction ◽  
Body Movement ◽  
Absolute Error ◽  
Angle Measurement ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Lower Limbs ◽  
Camera System ◽  
Kinect V2 ◽  
Ankle Angle

Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device’s design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle’s precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson’s coefficient, r = 0.89 ± 0.04, a Spearman’s coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson’s coefficient, r = 0.74, a Spearman’s coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals Asymmetry of Muscle Mass Distribution and Grip Strength in Professional Handball Players

2021 ◽  
Vol 18 (4) ◽  
pp. 1913
Author(s):  
Marcin Lijewski ◽  
Anna Burdukiewicz ◽  
Jadwiga Pietraszewska ◽  
Justyna Andrzejewska ◽  
Aleksandra Stachoń
Keyword(s):  
Grip Strength ◽  
Muscle Mass ◽  
Body Mass ◽  
Body Movement ◽  
The Body ◽  
Lower Limbs ◽  
Functional Changes ◽  
Morphological Asymmetry ◽  
Risk Of Injury ◽  
The Right

Handball is among the disciplines that impose a significant degree of asymmetry on the body movement. The aim of the study is to assess the influence of physical effort on the occurrence of asymmetry in body musculature and in isometric strength of handball players. The study examined 36 professional handball players. Players’ height and body mass were measured as to calculate their body mass indexes (BMIs). Segmental bioelectrical impedance analysis (SBIA) was used to assess: the percentage of fat mass, total muscle mass (MM), musculature of the right and left side of the body, and body segments (trunk, upper and lower limbs). Moreover, grip strength was also measured. The assessment confirmed the existence of discrepancies in the right and left sides of players’ bodies for the majority of the parameters. Cross-asymmetry and significant bilateral discrepancies in trunk musculature were also observed. Morphological asymmetry may impact performance in sports since it can cause unfavorable functional changes, which in turn increase the risk of injury and conditions caused by overexertion. Therefore, we believe it is important to emphasize the importance of individualized symmetrization during sports practice and consistent monitoring of the asymmetries occurring in different body parts; this should both improve one’s sports results and minimize the risk of injury.

Aspergillus Spondylodiscitis After Spinal Stenosis Surgery: A Case Report

2020 ◽  
Vol 5 (3 And 4) ◽  
pp. 155-160
Author(s):  
Mohsen Aghapoor ◽  
◽  
Babak Alijani Alijani ◽  
Mahsa Pakseresht-Mogharab ◽  
◽  
...  
Keyword(s):  
Antifungal Drugs ◽  
The Body ◽  
Lower Limbs ◽  
Lumbar Pain ◽  
Case Presentation ◽  
Delay In Diagnosis ◽  
Death Case ◽  
Therapeutic Results ◽  
History Of ◽  
Probable Diagnosis

Background and Importance: Spondylodiscitis is an inflammatory disease of the body of one or more vertebrae and intervertebral disc. The fungal etiology of this disease is rare, particularly in patients without immunodeficiency. Delay in diagnosis and treatment of this disease can lead to complications and even death. Case Presentation: A 63-year-old diabetic female patient, who had a history of spinal surgery and complaining radicular lumbar pain in both lower limbs with a probable diagnosis of spondylodiscitis, underwent partial L2 and complete L3 and L4 corpectomy and fusion. As a result of pathology from tissue biopsy specimen, Aspergillus fungi were observed. There was no evidence of immunodeficiency in the patient. The patient was treated with Itraconazole 100 mg twice a day for two months. Pain, neurological symptom, and laboratory tests improved. Conclusion: The debridement surgery coupled with antifungal drugs can lead to the best therapeutic results.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

Correction of Posture Deviations in the Sagittal Plane Using the Pilates Method

2019 ◽  
pp. 3-13
Author(s):  
Alexandru Cîtea ◽  
George-Sebastian Iacob
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Postural Control ◽  
Human Body ◽  
Sagittal Plane ◽  
The Body ◽  
Lower Limbs ◽  
Low Back ◽  
Pilates Method ◽  
The Relationship

Posture is commonly perceived as the relationship between the segments of the human body upright. Certain parts of the body such as the cephalic extremity, neck, torso, upper and lower limbs are involved in the final posture of the body. Musculoskeletal instabilities and reduced postural control lead to the installation of nonstructural posture deviations in all 3 anatomical planes. When we talk about the sagittal plane, it was concluded that there are 4 main types of posture deviation: hyperlordotic posture, kyphotic posture, rectitude and "sway-back" posture.Pilates method has become in the last decade a much more popular formof exercise used in rehabilitation. The Pilates method is frequently prescribed to people with low back pain due to their orientation on the stabilizing muscles of the pelvis. Pilates exercise is thus theorized to help reactivate the muscles and, by doingso, increases lumbar support, reduces pain, and improves body alignment.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals An interpretable prediction method for university student academic crisis warning

2021 ◽  
Author(s):  
Zhai Mingyu ◽  
Wang Sutong ◽  
Wang Yanzhang ◽  
Wang Dujuan
Keyword(s):  
Academic Achievement ◽  
Visual Analysis ◽  
Prediction Method ◽  
Absolute Error ◽  
University Student ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Achievement Prediction ◽  
Academic Achievement Prediction ◽  
Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

scholarly journals A Feasibility Study of Kinematic Characteristics on the Upper Body According to the Shooting of Elite Disabled Archery Athletes

2021 ◽  
Vol 18 (6) ◽  
pp. 2962
Author(s):  
Tae-Whan Kim ◽  
Jae-Won Lee ◽  
Seoung-Ki Kang ◽  
Kyu-Yeon Chae ◽  
Sang-Hyup Choi ◽  
...  
Keyword(s):  
The Body ◽  
Lower Limbs ◽  
Upper Body ◽  
Movement Trajectory ◽  
Phase 2 ◽  
Kinematic Characteristics ◽  
Significant Difference ◽  
Athletes With Disabilities ◽  
Body Center ◽  
Time Required

The purpose of this study is to compare and analyze the kinematic characteristics of the upper limb segments during the archery shooting of Paralympic Wheelchair Class archers (ARW2—second wheelchair class—paraplegia or comparable disability) and Paralympic Standing Class archers (ARST—standing archery class—loss of 25 points in the upper limbs or lower limbs), where archers are classified according to their disability grade among elite disabled archers. The participants of this study were selected as seven elite athletes with disabilities by the ARW2 (n = 4) and ARST (n = 3). The analysis variables were (1) the time required for each phase, (2) the angle of inclination of the body center, (3) the change of trajectory of body center, and (4) the change of the movement trajectory of the bow center by phase when performing six shots in total. The ARW2 group (drawing phase; M = 2.228 s, p < 0.05, holding phase; M = 4.414 s, p < 0.05) showed a longer time than the ARST group (drawing phase; M = 0.985 s, holding phase; M = 3.042 s), and the angle of the body did not show a significant difference between the two groups. Additionally, in the direction of the anteroposterior axis in the drawing phase, the change in the movement trajectory of the body center showed a more significant amount of change in the ARW2 group than in the ARST group, and the change in the movement trajectory of the bow center did not show a significant difference between the two groups.

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