  Using raw sensor data to model and train networks for Human Activity
Recognition can be used in many different applications, from fitness tracking
to safety monitoring applications. These models can be easily extended to be
trained with different data sources for increased accuracies or an extension of
classifications for different prediction classes. This paper goes into the
discussion on the available dataset provided by WISDM and the unique features
of each class for the different axes. Furthermore, the design of a Long Short
Term Memory (LSTM) architecture model is outlined for the application of human
activity recognition. An accuracy of above 94% and a loss of less than 30% has
been reached in the first 500 epochs of training.
