Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture
This work addresses activity recognition for applications like fitness tracking and safety monitoring, but it is incremental as it applies an existing LSTM method to a standard dataset.
The paper tackled human activity recognition using raw sensor data by designing an LSTM-RNN deep neural network, achieving an accuracy above 94% and loss below 30% within 500 epochs of training.
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.