Using Technology to Classify Yoga Poses

Yoga has become really important for our health and well-being. Many folks are trying to keep fit while juggling work and home life, and the gym often takes a backseat. One cool way to help with this is by figuring out yoga poses through technology. But wait, did you know that identifying those poses can be tricky? Yep, it's about pinpointing where our body joints are. There's a dataset called Yoga-82 with a whopping 82 different poses, and let's just say some of them are harder to label than a cat in a bath!

We’ve experimented with a few well-known computer models like VGG-16, ResNet-50, ResNet-101, and DenseNet-121 to help us figure these poses out. After some tinkering, DenseNet-121 really stood out with an impressive accuracy of 85%. That's like hitting the bullseye in darts!

#The What of Human Activity Recognition (HAR)

So what is Human Activity Recognition (HAR) anyway? It’s basically a fancy way of saying that we are trying to figure out what people are doing, either from video or sensors. Think of it like a supercharged detective that uses algorithms to spot actions.

Yoga poses count as a specific activity, and HAR can help recognize them. How? By analyzing videos or data from sensors as someone flows through their yoga routine. This can be super helpful for yoga teachers or even just to keep people safe from doing a downward dog incorrectly and pulling a muscle.

#The Rise of Virtual Personal Trainers

Machine learning is stepping in to help people work out smarter, not harder. Some systems are now able to offer exercise tips based on how you’re doing. Imagine a virtual trainer that knows your fitness level and can provide personalized workouts. They even adapt on the fly based on your heart rate, calories burned, and other metrics. It's like having a gym buddy who knows exactly when you're slacking off!

Wearable tech like fitness trackers are jumping on this train too. They’re using data to give feedback on your fitness journey. So if you’ve ever wondered if that new smartwatch is really keeping track of your yoga poses, the answer is: maybe!

#Yoga and Stress Relief

During the pandemic, yoga has become even more popular. People have been turning to it to help manage stress. But to really benefit, you’ve got to nail those poses. The problem? Not everyone can afford a yoga instructor.

Here’s where technology can pitch in. If we can create an application that acts as your personal yoga teacher, we could help a lot of people who want to practice but can’t find a trainer. Regular pose-checking methods often struggle due to the vast variety of human body types and poses. So, we thought, why not focus on the overall pose instead of pinpointing each joint?

#Our Big Idea

We set out to create a classification system that looks at the similarities between poses. By doing so, we can help more people access yoga even if they can’t get real-time feedback from a trainer.

Here’s what we did:

1. Image Preprocessing: We tried out various techniques to improve the images before we analyzed them.
2. Transfer Learning: We borrowed knowledge from pre-trained models to save time and resources when training our model.
3. Network Search: We used Random Search to find the best structure for our model.

And voilà! We had a system that could classify yoga poses without needing to pinpoint every little joint.

#What Others Have Done

Let’s see what’s been done in the world of yoga pose recognition so far. Some researchers have used deep learning to recognize joints from images successfully, making it possible to identify poses. But others pointed out that with so many different ways a human body can move, traditional methods often miss the mark.

In the yoga world, people started to notice a spike in interest during COVID-19. Remote yoga classes sprouted up everywhere, helping folks reduce stress and keep fit. Some researchers even built datasets of thousands of yoga pose images, testing various machine-learning models to see which worked best.

A bunch of clever minds decided to combine traditional and deep learning methods to boost their results. Who doesn’t love a good hybrid approach?

#Getting to Work on Yoga-82

We concentrated on the Yoga-82 dataset, which contains over 21,000 training images and about 7,500 test images, all showcasing those 82 different poses. The dataset divides poses into five main classes: standing, sitting, balancing, inverted, and reclining. Each of these has several subclasses, which make figuring out poses easier.

Before showing the images to our model, we prepped them. We enhanced the images to make the body parts easier to see. Contrast can highlight certain features, helping the model get a clearer picture of what’s going on.

#The Preprocessing Magic

So, how did we enhance our images? Here are the steps we took:

1. Contrast Enhancement: This step made light areas lighter and dark parts darker, making it easier for our model to see what it needs to pay attention to-the parts of the body that matter.

2. Median Filtering: Once we cranked up the contrast, noise became a problem. We employed a median filter to smooth things out without losing too much detail.

3. Image Sharpening: After filtering, some images ended up a bit blurry. We used a sharpening technique to make those edges crisper, reducing any fuzziness that had crept in.

#Transfer Learning to the Rescue

Now, let’s chat about transfer learning. This is a method that uses knowledge from a pre-trained model to speed up the learning process for new tasks. It’s like wanting to bake a cake but realizing you already have a great recipe from your aunt-it would save time, right?

We took some well-known models like VGG-16, ResNet-50, and DenseNet-121 and tweaked them to fit our yoga needs.

#VGG-16

This model is known for its straightforward structure. It has been a go-to for many beginners in the deep learning world. It’s often used as a base model because it’s easy to adapt.

#ResNet-50

ResNet-50 handles deeper networks like a pro, thanks to its clever use of skip connections that allow the model to overcome the so-called 'vanishing gradient' problem. It has layers that pick up on low-level features in the image, perfect for our yoga poses.

#DenseNet-121

DenseNet-121 is a modern approach with a twist. It connects layers in a way that promotes feature sharing, helping the model learn more efficiently. We found it to be the best fit for our yoga pose classification.

#Our Findings

We put our models through their paces with various configurations, discovering that DenseNet-121 performed the best. However, VGG-16 had its moments too, especially when we only fine-tuned the last few layers. On the other hand, ResNet-50 did not fare as well when we froze most of the layers.

All in all, using DenseNet-121 helped us outperform existing results!

#Classifying Yoga Poses

The hard part of classifying yoga poses is that many of them look similar. It’s like trying to tell apart identical twins who are wearing the same outfits! That's why some researchers have shifted from focusing on key point detection to addressing the image classification challenge directly. With machine learning on our side, these classification problems have become much easier to tackle.

We’ve ultimately seen promising results, thanks to transfer learning and our tweaks to the models. But the adventure doesn't stop here!

#What’s Next?

We are excited to keep pushing the boundaries! Our next steps include trying out different approaches, like combining multiple learning models. We also want to dive deeper into understanding how our model makes decisions-which means checking out tools like GradCam.

As a bonus, exploring new processing techniques can also help our current methods. Plus, tackling any biases that pop up in our models could make our yoga pose classification even better.

So here we have it-a journey into the world of yoga poses, technology, and a sprinkle of humor along the way. Who knew identifying yoga poses could be so much fun? And the best part? Everyone gets access to yoga, whether or not they have a trainer in their back pocket!