Wrap models in prediction interface to use in application

We need to conveniently call AI models from software applications to bring them to users and get their full benefit. Even though AI is making headlines it’s impact in many fields of business, industry, and medicine have been low relative to the investment leading to low Return on Investment (ROI). Part of the reason is

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)’,

Multiple measures and windows

Multiple measurements are recorded for each gesture. Test code. Function call. The measures captured here are acceleration in different directions: ax (m/s^2) ay (m/s^2) az (m/s^2) aT (m/s^2) Accelerometer measurements. Looking at a single measure like x, we can see a single measure may not differentiate gestures. The multi measure plots above look much more

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