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.