Human-Activity-Recognition-using-CNN
CNN model that classifies human activity with a test accuracy of 91.6%. The target labels are 'walking,' 'jogging', 'going upstairs', 'going downstairs', 'sitting' and 'standing'. Data was collected from UCI Machine Learning Repository and each activity is recorded in m/s^2 along 3-axis.
