Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new state-of-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly.