Stacked Lstm Network for Human Activity Recognition Using Smartphone Data

• Main Authors: Ullah, Mohib, Ullah, Habib, Khan, Sultan Daud, Cheikh, Faouzi Alaya
• Format: Conference Proceeding
• Language: English
• Subjects: Activity recognition; Cost function; Feature extraction; Hidden Markov models; human activity recognition; L2 regularizer; sensor-data; Sports player; stacked LSTM; Training; Visualization
• ISSN: 2471-8963
• DOI: 10.1109/EUVIP47703.2019.8946180

Sensor-based human activity recognition is an essential task for automatic behavior analysis for sports player, senior citizens, and IoT applications. The traditional approaches are based on hand-crafted features which use fixed mathematical rules to extract the features from the input data and are not capable of incremental learning. In this paper, we proposed a stacked long Short-term memory (LSTM) network for recognizing six human behaviors from the smartphone data. The network consists of a five LSTM cell that is trained end-to-end on the sensor data. The network is preceded by a single layer neural network that pre-processes the data for the stacked LSTM network. An L 2 regularizer is used in the cost function which helps the network in generalization. The network is evaluated on public domain UCI dataset and quantitative results are compared against six state-of-the-art methods. The performance is calculated in terms of precision-recall and the average accuracy. The proposed network improves the average accuracy by 0.93% as compared to the closest state-of-the-art method without any manual feature engineering.