Human activity recognition by combining external features with accelerometer sensor data using deep learning network model

Abstract

Various Human Activities are classified through time-series data generated by the sensors of wearable devices. Many real-time scenarios such as Healthcare Surveillance, Smart Cities and Intelligent surveillance etc. are based upon Human Activity Recognition. Despite the popularity of local features-based approaches�and machine learning approaches, it fails to capture adequate temporal information. In this paper, the deep convolutional neural model has been proposed by combining external features, i.e. orientation invariant (

) and consecutive point trajectory information (

) with tri-axis data of the accelerometer. The proposed external features based approach experimented on three different deep learning architecture, namely Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN) and Convolution Long-Short Term Memory (ConvLSTM). Accuracy of the algorithms radically improve with the additional input feature

and

along with tri-axis data of accelerometer. The results show that the performance of all three LSTM, CNN and ConvLSTM models is better to compare with the state of art methods on WISDOM dataset and Activity dataset also the performance of ConvLSTM is 98.41% for WISDOM dataset and 98.04 for activity dataset, which is higher than that of CNN and LSTM model used in this paper.