A Multichannel CNN-LSTM Network for Daily Activity Recognition using Smartwatch Sensor Data

Recognition of human behavior is recently an active and stimulating study. The HAR can provide valuable information on human movement and the behavior of everyday life activities. In the last decade, a large range of HAR-based applications have been implemented, such as healthcare tracking, biometric user authentication, and so on. Previously, several deep learning approaches have been introduced to focus on the issue of conventional machine learning approaches with handcrafted features. So, a novel deep learning architecture to solve the HAR problem is proposed in this study. The introduced architecture is a hybrid model called a multichannel CNN-LSTM network. The model is evaluated by state-of-the-art evaluation metrics; accuracy, precision, recall and F1-score, with a public dataset of smartwatch's accelerometer data called DHA dataset. The proposed multichannel CNN-LSTM outperforms other deep learning methods in terms of accuracy, with a score of 96.87%.