In these challenging times caused by the COVID-19, technology that leverages Artificial Intelligence potential can help people cope with the pandemic. For example, people looking to perform physical exercises while in quarantine. We also find another opportunity in the widespread adoption of mobile smart devices, making complex Artificial Intelligence (AI) models accessible to the average user.
Taking advantage of this situation, we propose a Smart Coaching experience on the Edge with our Digital Twin Coaching (DTC) architecture. Since the general population is advised to work from home, sedentarism has become prevalent. Coaching is a positive force in exercising, but keeping physical distance while exercising is a significant problem. Therefore, a Smart Coach can help in this scenario as it involves using smart devices instead of direct communication with another person. Some researchers have worked on Smart Coaching, but their systems often involve complex devices such as RGB-Depth cameras, making them cumbersome to use. Our approach is one of the firsts to focus on everyday smart devices, like smartphones, to solve this problem.
Digital Twin Coaching can be defined as a virtual system designed to help people improve in a specific field and is a powerful tool if combined with edge technology. The DTC architecture has six characteristics that we try to fulfill: adaptability, compatibility, flexibility, portability, security, and privacy.
We collected training data of 10 subjects using a 2D pose estimation model to train our models since there was no dataset of Coach-Trainee videos. To effectively use this information, the most critical pre-processing step was synchronization. This step synchronizes the coach and the trainee’s poses to overcome the trainee's action lag while performing the routine in real-time.
We trained a light neural network called “Pose Inference Neural Network” (PINN) to serve as a fine-tuning architecture mechanism. We improved the generalist 2D pose estimation model with this trained neural network while keeping the time complexity relatively unaffected. We also propose an Angular Pose Representation to compare the trainee and coach's stances that consider the differences in different people's body proportions.
For the PINN model, we use Random Search Optimization to come up with the best configuration. The configurations tested included using 1, 2, 3, 4, 5, and 10 layers. We chose the 2-Layer Neural Network (2-LNN) configuration because it was the fastest to train and predict while providing a fair tradeoff between performance and resource consumption. Using frame synchronization in pre-processing, we improved 76% on the test loss (Mean Squared Error) while training with the 2-LNN. The PINN improved the R2 score of the PoseNet model by at least 15% and at most 93% depending on the configuration. Our approach only added 4 seconds (roughly 2% of the total time) to the total processing time on average. Finally, the usability test results showed that our Proof of Concept application, DTCoach, was considered easy to learn and convenient to use. At the same time, some participants mentioned that they would like to have more features and improved clarity to be more invested in using the app frequently.
We hope DTCoach can help people stay more active, especially in quarantine, as the application can serve as a motivator. Since it can be run on modern smartphones, it can quickly be adopted by many people.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/42007 |
| Date | 15 April 2021 |
| Creators | Gámez Díaz, Rogelio |
| Contributors | El Saddik, Abdulmotaleb |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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