Classify different physical activities from motion sensors data (accelerometers and gyroscope) colleted in smartphones and smartwatches by using a multilayer perceptron (implemented from scratch). Activities : ‘Biking’, ‘Sitting’, ‘Standing’, ‘Walking’, ‘Stair Up’ and ‘Stair down’.
Average_error% = 18% (k-fold cross-validation)
Link: https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition
561 columns ; 7352 lines
There two main files : one contains the data without classes and one contains classes only.
This personal project was intended as an exercise to understand the concept of Neural Network in Machine Learning. That is the reason why I chose to implement everything from scratch.