scholarly journals Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

2020 ◽  
Vol 11 (1) ◽  
pp. 313
Author(s):  
Alessandro Silacci ◽  
Redha Taiar ◽  
Maurizio Caon
Keyword(s):  
Artificial Intelligence ◽  
Training Data ◽  
Central Piece ◽  
Road Cycling ◽  
Training Plan ◽  
Resting Time ◽  
Time Distance ◽  
Road Cyclists ◽  
Cycling Training

In a world where the data is a central piece, we provide a novel technique to design training plans for road cyclists. This study exposes an in-depth review of a virtual coach based on state-of-the-art artificial intelligence techniques to schedule road cycling training sessions. Together with a dozen of road cycling participants’ training data, we were able to create and verify an e-coach dedicated to any level of road cyclists. The system can provide near-human coaching advice on the training of cycling athletes based on their past capabilities. In this case study, we extend the tests of our empirical research project and analyze the results provided by experts. Results of the conducted experiments show that the computational intelligence of our system can compete with human coaches at training planification. In this case study, we evaluate the system we previously developed and provide new insights and paths of amelioration for systems based on artificial intelligence for athletes. We observe that our system performs equal or better than the control training plans in 14 and 24 week training periods where it was evaluated as better in 4 of our 5 test components. We also report a higher statistical difference in the results of the experts’ evaluations between the control and virtual coach training plan (24 weeks; training load: X2 = 4.751; resting time quantity: X2 = 3.040; resting time distance: X2 = 2.550; efficiency: X2 = 2.142).

scholarly journals Use of artificial intelligence to estimate population health indicators in France

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R Haneef ◽  
S Fuentes ◽  
R Hrzic ◽  
S Fosse-Edorh ◽  
S Kab ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Data Set ◽  
Selection Of

Abstract Background The use of artificial intelligence is increasing to estimate and predict health outcomes from large data sets. The main objectives were to develop two algorithms using machine learning techniques to identify new cases of diabetes (case study I) and to classify type 1 and type 2 (case study II) in France. Methods We selected the training data set from a cohort study linked with French national Health database (i.e., SNDS). Two final datasets were used to achieve each objective. A supervised machine learning method including eight following steps was developed: the selection of the data set, case definition, coding and standardization of variables, split data into training and test data sets, variable selection, training, validation and selection of the model. We planned to apply the trained models on the SNDS to estimate the incidence of diabetes and the prevalence of type 1/2 diabetes. Results For the case study I, 23/3468 and for case study II, 14/3481 SNDS variables were selected based on an optimal balance between variance explained and using the ReliefExp algorithm. We trained four models using different classification algorithms on the training data set. The Linear Discriminant Analysis model performed best in both case studies. The models were assessed on the test datasets and achieved a specificity of 67% and a sensitivity of 62% in case study I, and a specificity of 97 % and sensitivity of 100% in case study II. The case study II model was applied to the SNDS and estimated the prevalence of type 1 diabetes in 2016 in France of 0.3% and for type 2, 4.4%. The case study model I was not applied to the SNDS. Conclusions The case study II model to estimate the prevalence of type 1/2 diabetes has good performance and will be used in routine surveillance. The case study I model to identify new cases of diabetes showed a poor performance due to missing necessary information on determinants of diabetes and will need to be improved for further research.

Body balance in a free-standing position in road and off-road cyclists

2014 ◽  
Vol 6 (4) ◽  
Author(s):  
Paulina Hebisz ◽  
Rafal Hebisz ◽  
Marek Zaton
Keyword(s):  
Sagittal Plane ◽  
Standing Position ◽  
The Body ◽  
Free Standing ◽  
Body Balance ◽  
Eyes Closed ◽  
Road Cycling ◽  
Progressive Exercise ◽  
Before And After ◽  
Road Cyclists

AbstractBackground: The purpose of this study was to compare body balance in road and off-road cyclists, immediately before and after the racing season.Material/Methods: Twenty individuals participated in the study and they were divided into two groups: specialists in road-cycling (n = 10) and in off-road cycling (n = 10). Immediately before and after the five-month racing season stabilographic trials were carried out (at rest and after progressive exercise). In assessing body balance the distance and velocity of the centre shifts (in the anterior-posterior and left-right direction) were analysed. The tests were performed with the cyclists’ eyes open, eyes closed, and in feedback.Results: After the racing season, in the off-road cyclists’ group, distance and velocity of the centre of pressure shifts increased after a progressive exercise.Conclusions: In the off-road cyclists’ group the balance of the body in the sagittal plane deteriorated after the racing season. Moreover, after the racing season off-road cyclists were characterized by a worse balance of the body, compared to road cyclists

Sprint Track Cyclists and Road Cycling Training

2020 ◽  
Vol 29 (3) ◽  
pp. 1129-1137
Author(s):  
Jae-Ho Park ◽  
Hee-Jae Kim ◽  
Sok Park
Keyword(s):  
Road Cycling ◽  
Cycling Training

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

scholarly journals UNDERSTANDING SPECTRUM BIAS IN ALGORITHMS DERIVED BY ARTIFICIAL INTELLIGENCE A CASE STUDY IN DETECTING AORTIC STENOSIS USING ELECTROCARDIOGRAMS

2021 ◽  
Vol 77 (18) ◽  
pp. 3241
Author(s):  
Andrew S. Tseng ◽  
Michal Shelly-Cohen ◽  
Zachi Itzhak Attia ◽  
Peter Noseworthy ◽  
Paul Friedman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Aortic Stenosis
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