Preprocess time series measurement windows into multidimensional tensors

The accelerometer data has four measurement columns and 160 time series measurement rows roughly captures a single motion. ax (m/s^2) ay (m/s^2) az (m/s^2) aT (m/s^2) Accelerometer column measurements. We will preproceess the data into tensors with 4 columns and 160 rows by the number of samples collected. See notebook. cols: [‘ax (m/s^2)’, ‘ay (m/s^2)’,

Check gesture measurements for normal motion and clean training data

Beginning and end measurements aren’t showing the standard gesture measurements because it takes time to start the recording then start moving the smartphone recording device. This again emphasizes the importance of visualizing training data as many data collection systems will have non-standard artifacts of the data collection. We want to remove these extreme low or

Ground truth label encoding

The ground truth label names of gestures ([‘rock’, ‘supination’, ‘scoop’, ‘shake’, ‘circle’]) are strings. The model training needs numeric labels so that the error of in-training models’ predictions can be calculated when comparing predictions to ground truths. The ground truth labels are converted into numbers (array([1, 4, 2, 3, 0])). Though a simple conversion from