scholarly journals IART: Inertial Assistant Referee and Trainer for Race Walking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 783 ◽  
Author(s):  
Teodorico Caporaso ◽  
Stanislao Grazioso
Keyword(s):  
Elite Athletes ◽  
Step Length ◽  
Ground Contact ◽  
Average Accuracy ◽  
Race Walking ◽  
Single Sensor ◽  
Ground Contact Time ◽  
Paved Road ◽  
Inertial Measurements

This paper presents IART, a novel inertial wearable system for automatic detection of infringements and analysis of sports performance in race walking. IART algorithms are developed from raw inertial measurements collected by a single sensor located at the bottom of the vertebral column (L5–S1). Two novel parameters are developed to estimate infringements: loss of ground contact time and loss of ground contact step classification; three classic parameters are indeed used to estimate performance: step length ratio, step cadence, and smoothness. From these parameters, five biomechanical indices customized for elite athletes are derived. The experimental protocol consists of four repetitions of a straight path of 300 m on a long-paved road, performed by nine elite athletes. Over a total of 1620 steps (54 sequences of 30 steps each), the average accuracy of correct detection of loss of ground contact events is equal to 99%, whereas the correct classification of the infringement is equal to 87% for each step sequence, with a 92% of acceptable classifications. A great emphasis is dedicated on the user-centered development of IART: an intuitive radar chart representation is indeed developed to provide practical usability and interpretation of IART indices from the athletes, coaches, and referees perspectives. The results of IART, in terms of accuracy of its indices and usability from end-users, are encouraging for its usage as tool to support athletes and coaches in training and referees in real competitions.

scholarly journals Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

2021 ◽  
Vol 11 (5) ◽  
pp. 2093
Author(s):  
Noé Perrotin ◽  
Nicolas Gardan ◽  
Arnaud Lesprillier ◽  
Clément Le Goff ◽  
Jean-Marc Seigneur ◽  
...  
Keyword(s):  
Heart Rate ◽  
Low Cost ◽  
Step Length ◽  
Measurement Unit ◽  
Ecological Conditions ◽  
Sports Science ◽  
Ground Contact Time ◽  
Spatio Temporal ◽  
Performance Results ◽  
Trail Running

The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10km. The three sensors used were (i) a Stryd sensor (Stryd Inc. Boulder CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

scholarly journals Associations between Well-Being State and Match External and Internal Load in Amateur Referees

2021 ◽  
Vol 18 (6) ◽  
pp. 3322
Author(s):  
Eñaut Ozaeta ◽  
Javier Yanci ◽  
Carlo Castagna ◽  
Estibaliz Romaratezabala ◽  
Daniel Castillo
Keyword(s):  
External Load ◽  
Contact Time ◽  
Well Being ◽  
Ground Contact ◽  
Internal Load ◽  
Match Play ◽  
Power Meter ◽  
Ground Contact Time ◽  
Training Impulse ◽  
External Loads

The main aim of this paper was to examine the association between prematch well-being status with match internal and external load in field (FR) and assistant (AR) soccer referees. Twenty-three FR and 46 AR participated in this study. The well-being state was assessed using the Hooper Scale and the match external and internal loads were monitored with Stryd Power Meter and heart monitors. While no significant differences were found in Hooper indices between match officials, FR registered higher external loads (p < 0.01; ES: 0.75 to 5.78), spent more time in zone 4 and zone 5, and recorded a greater training impulse (TRIMP) value (p < 0.01; ES: 1.35 to 1.62) than AR. Generally, no associations were found between the well-being variables and external loads for FR and AR. Additionally, no associations were found between the Hooper indices and internal loads for FR and AR. However, several relationships with different magnitudes were found between internal and external match loads, for FR, between power and speed with time spent in zone 2 (p < 0.05; r = −0.43), ground contact time with zone 2 and zone 3 (p < 0.05; r = 0.50 to 0.60) and power, speed, cadence and ground contact time correlated with time spent in zone 5 and TRIMP (p < 0.05 to 0.01; r = 0.42 to 0.64). Additionally, for AR, a relationship between speed and time in zone 1 was found (p < 0.05; r = −0.30; CL = 0.22). These results suggest that initial well-being state is not related to match officials’ performances during match play. In addition, the Stryd Power Meter can be a useful device to calculate the external load on soccer match officials.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Online EEG Classification of Covert Speech for Brain–Computer Interfacing

2017 ◽  
Vol 27 (08) ◽  
pp. 1750033 ◽  
Author(s):  
Alborz Rezazadeh Sereshkeh ◽  
Robert Trott ◽  
Aurélien Bricout ◽  
Tom Chau
Keyword(s):  
Chance Level ◽  
Support Vector ◽  
Eeg Classification ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Online Classification ◽  
Average Accuracy ◽  
Gamma Power ◽  
Brain Computer Interfacing

Brain–computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

scholarly journals The Effects of Muscle Energy on Low Back Pain: A 3D Analysis of running biomechanics

2021 ◽  
Vol 9 (1) ◽  
pp. 51
Author(s):  
Alisa Drapeaux ◽  
Jon Hurdelbrink
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Reaction Force ◽  
Low Back ◽  
Ground Contact ◽  
Peak Acceleration ◽  
Clinical Exam ◽  
Ground Contact Time ◽  
Muscle Energy ◽  
Initial Peak

Background: Muscle energy technique (MET) is asn osteopathic treatment technique that is utilized frequently in the clinical setting, yet the overall effectiveness is minimally supported within literature. MET is an osteopathic technique that involves an isometric contract relax technique intended to improve alignment and enhance neuromuscular education. Objective: The purpose of this study was to determine the effectiveness of MET on running kinetics on subjects with low back pain. Method: A quasi-experimental research design was implemented and subjects, all of whom either had a history of or currently experience low back pain, underwent pre-intervention data collection of: anthropometric measurements, medical history, dorsaVi 3D running analysis, and a musculoskeletal and neurological clinical exam. Subjects underwent 6 weeks of isolated lumbo-pelvic MET at a frequency of twice a week, and were instructed to avoid all other treatment. Post-intervention data collected included a clinical exam and another dorsaVI running analysis. Results: Data was analyzed including: pre and post-treatment initial peak acceleration, ground contact time, and ground reaction force. A paired t-test comparing pre and post mean kinetic changes demonstrated the following p values: initial peak acceleration p = .80, ground contact time p = .96, and ground reaction force p = .68. Conclusion: This study demonstrated that isolated MET treatment is not statistically significant for changing 3D kinetic running variable in subjects with low back pain. Clinical Implications: Recommend healthcare providers to use a multi-treatment approach for low back pain. Future research should include a control group and larger sample size.

scholarly journals Neither Beetroot Juice Supplementation nor Increased Carbohydrate Oxidation Enhance Economy of Prolonged Exercise in Elite Race Walkers

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2767
Author(s):  
Louise M. Burke ◽  
Rebecca Hall ◽  
Ida A. Heikura ◽  
Megan L. Ross ◽  
Nicolin Tee ◽  
...  
Keyword(s):  
Elite Athletes ◽  
Endurance Performance ◽  
Beetroot Juice ◽  
Future Studies ◽  
Exercise Economy ◽  
Parallel Group ◽  
Performance Effects ◽  
Race Walking ◽  
The Impact ◽  
Current Guidelines

Given the importance of exercise economy to endurance performance, we implemented two strategies purported to reduce the oxygen cost of exercise within a 4 week training camp in 21 elite male race walkers. Fourteen athletes undertook a crossover investigation with beetroot juice (BRJ) or placebo (PLA) [2 d preload, 2 h pre-exercise + 35 min during exercise] during a 26 km race walking at speeds simulating competitive events. Separately, 19 athletes undertook a parallel group investigation of a multi-pronged strategy (MAX; n = 9) involving chronic (2 w high carbohydrate [CHO] diet + gut training) and acute (CHO loading + 90 g/h CHO during exercise) strategies to promote endogenous and exogenous CHO availability, compared with strategies reflecting lower ranges of current guidelines (CON; n = 10). There were no differences between BRJ and PLA trials for rates of CHO (p = 0.203) or fat (p = 0.818) oxidation or oxygen consumption (p = 0.090). Compared with CON, MAX was associated with higher rates of CHO oxidation during exercise, with increased exogenous CHO use (CON; peak = ~0.45 g/min; MAX: peak = ~1.45 g/min, p < 0.001). High rates of exogenous CHO use were achieved prior to gut training, without further improvement, suggesting that elite athletes already optimise intestinal CHO absorption via habitual practices. No differences in exercise economy were detected despite small differences in substrate use. Future studies should investigate the impact of these strategies on sub-elite athletes’ economy as well as the performance effects in elite groups.

Voltammetric e-Tongue Based on a Single Sensor and Variable Selection for the Classification of Teas

2018 ◽  
Vol 11 (7) ◽  
pp. 1958-1968 ◽  
Author(s):  
Dayvison R. Rodrigues ◽  
Diana S. M. de Oliveira ◽  
Marcio J. C. Pontes ◽  
Sherlan G. Lemos
Keyword(s):  
Variable Selection ◽  
Single Sensor ◽  
Selection For

Single-Leg Jump Performance Before and After Exercise in Healthy and Anterior Cruciate Ligament Reconstructed Individuals

2020 ◽  
Vol 29 (7) ◽  
pp. 879-885
Author(s):  
Haley Bookbinder ◽  
Lindsay V. Slater ◽  
Austin Simpson ◽  
Jay Hertel ◽  
Joseph M. Hart
Keyword(s):  
Anterior Cruciate Ligament ◽  
Repeated Measures ◽  
Contact Time ◽  
Cruciate Ligament ◽  
Ground Contact ◽  
Jump Height ◽  
Before And After ◽  
Healthy Control ◽  
Ground Contact Time ◽  
Anterior Cruciate

Context: Many clinicians measure lower-extremity symmetry after anterior cruciate ligament reconstruction (ACLR); however, testing is completed in a rested state rather than postexercise. Testing postexercise may better model conditions under which injury occurs. Objective: To compare changes in single-leg performance in healthy and individuals with history of ACLR before and after exercise. Design: Repeated-measures case-control. Setting: Laboratory. Patients: Fifty-two subjects (25 control and 27 ACLR). Intervention: Thirty minutes of exercise. Main Outcome Measures: Limb symmetry and involved limb performance (nondominant for healthy) for single-leg hop, ground contact time, and jump height during the 4-jump test. Cohen d effect sizes were calculated for all differences identified using a repeated-measures analysis of variance. Results: Healthy controls hopped farther than ACLR before (d = 0.65; confidence interval [CI], 0.09 to 1.20) and after exercise (d = 0.60; CI, 0.04 to 1.15). Those with ACLR had longer ground contact time on the reconstructed limb compared with the uninvolved limb after exercise (d = 0.53; CI, −0.02 to 1.09), and the reconstructed limb had greater ground contact time compared with the healthy control limb after exercise (d = 0.38; CI, −0.21 to 0.73). ACLR were less symmetrical than healthy before (d = 0.38; CI, 0.17 to 0.93) and after exercise (d = 0.84; CI, 0.28 to 1.41), and the reconstructed limb demonstrated decreased jump height compared with the healthy control limbs before (d = 0.75; CI, 0.19 to 1.31) and after exercise (d = 0.79; CI, 0.23 to 1.36). Conclusions: ACLR became more symmetric, which may be from adaptations of the reconstructed limb after exercise. Changes in performance and symmetry may provide additional information regarding adaptations to exercise after reconstruction.

scholarly journals Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rana Zia Ur Rehman ◽  
Silvia Del Din ◽  
Yu Guan ◽  
Alison J. Yarnall ◽  
Jian Qing Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Decision Making ◽  
Information Gain ◽  
Step Length ◽  
Support Vector ◽  
Step Width ◽  
Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

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