Detect Workout Pose for Virtual Gym using CNN
Approximately 80% of the people across globe do not use gym, yet they pay $30 to $125/month.Attrition from gym is linked with discouraging results and lack of engagement. Traditional gym users don’t know proper exercise regimen and users prefer workout regimens that are fun, customizable and social.
To combat above problem, we came up with idea to provide customized fitness solutions using Artificial Intelligence. In this talk, we showcase how we can leverage Deep Learning based Architectures like CNN to develop "Workout pose detection" that tracks user movement and classify it corresponding to specific trained workout and will determine whether the performed pose is correct or wrong.
Keywords: CNN, Deep Learning, Image classification Model, Computer Vision.
Outline/Structure of the Talk
1) What, why, Application
2) Dataset Discussion
3) Model Training
4) Model Comparison
6) Challenges and Future Scope
1) Candidate will get understanding of product development related to computer vision
2) End to end understanding of applied data Science
3) concepts related to computer vision
4) Comparision of Deep Learning Architectures with different hyper-parameters.
5) working example of usage of Deep learning architecture like CNN in real life.
Data Scientist, Deep Learning Enthusiast, Student, computer vision practicioner, Managers
Prerequisites for Attendees
1) Basic Idea of Neural Network
2) Interest in Deep Learning, computer vision
schedule Submitted 3 weeks ago
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Venkatraman J - Discovering hidden insights from Unstrucutred data using Semantic Knowledge graphVenkatraman JSr. Software engineerMetapack
schedule 1 month agoSold Out!
Knowledge representation has been a research for many years in AI world and its continuing further too. Once knowledge is represented, reasoning from that extracted knowledge is done by various inferencing techniques. Initial knowledge bases were built using rules from domain experts and different inferencing techniques like Fuzzy inference, Bayesian inference were applied to extract reasoning from those knowledge bases. Semantic networks is another form of knowledge representation which can represent structured data like WordNet, DBpedia which solves problems in a specific domain by storing entities and relations among entities using onotologies.
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