Tag: status report

In this week, Sophia finished building the glove and Rachel was able to get our first set of real data. Since I have already performed validation tests on the models I have used with the generated fake data, I decided to use these tuned models on the real data after preprocessing them. Surprisingly, the results were overall better than that of the fake data. This shows that our fake data was not well generated. One possibility is that we included too much variance in generating sensor values. However, though the accuracy metrics were quite different, the trends remain the same. Random forest classifier achieved highest accuracy while perceptron had the lowest. I also did some extra tuning with neural net, but there wasn’t any significant improvement in accuracies, likely because our data isn’t high dimensional. One thing I would like to add is that this set of real data is only from Rachel, so there could be a possibility of overfitting which explains the high accuracy metrics.

In terms of schedule, we are actually ahead. We were able to get data from both type of sensors. We do need to work on getting consistent data and ensure the craftsmanship of the glove since Rachel mentioned some parts came undone. We will need to make sure that the sensors on the glove are stabilized before moving on to collect data from others.

Next week, I’ll be working on fixing the glove with the team and gathering more training data, starting from Sophia and me. If time and resources permit, we will try to find others who can sign for us. We will also working on finishing up the design report.

This week, my team and I worked collaboratively on the design review slides. Since we changed the number of gestures to recognize from 5 common signs to 21 ASL letters, we had to make sure to include our new scope. With the expansion of the scope, I worked on changing my data generation algorithm to include the ASL letters. I have also examined best performing models I have used in depth, such as using different parameters, to see if they can perform better than the default models given.

We are a bit behind schedule because our orders did not all arrive until Friday, hence putting us behind in making the glove and getting real data. We may have to speed up and do some more work next week to ensure we can get consistent data from both types of sensors. This setback is quite minor in my opinion since we have already gotten started on glove building and pre-determining ML models can save us time in the future.

Next week, we will have the glove built and we will be able to get real data. I will work on processing those data to ensure they are suitable for model training. We will also sign some gestures to obtain a preliminary set of data. Using this data, I’ll be testing the models that I have identified to have the best performances with generated data to find which one does well on the real data and perform further fine tuning to improve their accuracies.

This week Rachel and I worked together on generating fake data for gloves sensors to test out different machine learning models. We have decided to each generate a different set of data. Having data with some differences are good for making sure that models are generalizing well and not overfitting to a particular set of data.

Each sample of data consists of bend angles of each finger from flex sensors and nine values from each component of the IMU (accelerometer, gyroscope, and magnetometer). The bend angles for each finger are estimated by observing the finger’s pose for each gesture. The values will then be sampled from a normal distribution with some variance to account for variations in poses. There’s also a possibility of generating completely random numbers to consider for outliers. Similar procedures are done for IMU data generation. Data is generated by estimating a reasonable range based on the directions and orientations of the gestures. The estimations are based off an IMU data sample sheet that I have found online.

I have also done some preliminary model testing to check their accuracies. The models are trained and tested on the data that I generated, so there can be a possibility of overfitting. The models that I have tested include: SVM, perceptron, KNN, random forest, and neural networks. So far, most of these models meet our threshold for accuracy, but more testing should be done with different data sets and more model refinements are needed.

I believe we are on schedule. We have placed our order for the parts needed for the glove and started researching on models to use. Next week, I plan dive deeper into each model to find the optimal parameters to achieve best accuracy and latency and also get to test them different data sets.

During our mandatory lab meeting, the team collaboratively researched about project design and implementation and worked on the presentation slides. We will be meeting this Sunday to finish up the slides and finalize the type of sensors that we will be using. Since our project must be done sequentially for most part, the team will not be splitting up a lot of the work.

I took on the task of setting up the team’s website and writing the project overview. I also looked into some machine learning models that our project can use and established pros and cons for each.

The team is slightly behind schedule on ordering the glove and sensors, but we’ll be ordering them right away after this Sunday’s meeting.

In the coming week, I will be presenting our proposal to the class and start looking deeper into viable machine learning models and performing tests to see which model can give higher accuracy, and if possible, implementing a baseline code structure for classifying gestures.