This week I worked with Stephanie on generating fake data. We both did research on the data that the sensors output and each generated a set of fake data using our own methods. We decided that rather than working together to generate one set of fake data together, it would be beneficial to do separate data generation methods, so that we have more data to work with and can be more sure that the model we select based on this fake data is likely to be the most fit for the task of gesture identification. Each data point has a bend degree for each finger as well as nine values outputted from the IMU.
For each finger as well as each component of the IMU, I randomly select an angle within a certain range for each gesture. This angle range was determined by us figuring out what a reasonable angle each finger should make for each gesture. The random selection from this range is meant to simulate the differences in how people may make each gesture. Based on the variance from the flex sensors, I add some noise to each datapoint by grabbing a random value from a normal distribution. This added noise is meant to simulate the variance from the sensors themselves.
Even though we have our fake data in terms of degrees, we hypothesize that we might get better results by feeding either voltage or resistance directly into the model (as opposed to translating it to degrees first). I read a paper researching flex sensors’ resistance values and it found that the relationship between angle and resistance actually isn’t linear for 0-30 degrees, so doing a direct translation may actually not be representative of the data collected. However, for the purposes of picking a model, our fake data in degrees is just easier to understand and will likely suffice. We might also consider collecting data at multiple points in time since one snapshot of the sensor’s data may not be enough to accurately determine the gesture the user is making.
I would say we are right on track with our timeline. We have requested all the parts we need to build our glove and are already working on figuring out the optimal ML model as well as began designing the PCB for the glove. Next week, I plan on working with my group on deciding on a few models to try out and training/testing them to figure out the best model for our use case. We will need to consider both accuracy and latency when deciding the model.