Table 9 Steps for daily activity level classification

Step 1: Load activity datasets

Step 2: Encode predictive class values with one-hot encoding

Step 3: Split data into train, validation, and test with (a 60:20:20) ratio using the stratification technique

Step 4: Create classification model, M

Step 5: Compile M with value-set for optimization technique, k-fold, and metrics

Step 6: Fit model M with training data

Step 7: Improve the model with a grid-search technique

Step 8: Calculate accuracy and other classification metrics

Step 9: Select the best learning parameters as computed with the Grid Search technique

Step 10: Classify input data into respective output classes