Basic Activity Recognition from Wearable Sensors Using a Lightweight Deep Neural Network

The field of human activity recognition has undergone a great development, making its presence felt in various sectors such as healthcare and supervision. The identification of fundamental behaviours that occur regularly in our everyday lives can be extremely useful in the development of systems that aid the elderly, as well as opening the door to the detection of more complicated activities in a Smart home environment. Recently, the use of deep learning techniques allowed the extraction of features from sensor's readings automatically, in a hierarchical way through non-linear transformations. In this study, we propose a deep learning model that can work with raw data without any pre-processing. Several human activities can be recognized by our stacked LSTM network. We demonstrate that our outcomes are comparable to or better than those obtained by traditional feature engineering approaches. Furthermore, our model is lightweight and can be applied on edge devices. Based on our expertise with two datasets, we obtained an accuracy of 97.15% on the UCI HAR dataset and 99% on WISDM dataset.