An Adaptive Batch Size-Based-CNN-LSTM Framework for Human Activity Recognition in Uncontrolled Environment

Human activity recognition (HAR) is a process of identifying the daily living activities of an individual using a set of sensors and appropriate learning algorithms. Most of the works on HAR are done using a mix of sensor data that is collected in a simulated environment, and due to that, the real-time recognition suffers. This article proposes an efficient adaptive batch size-based-CNN-LSTM model for recognizing different human activities in an uncontrolled environment. It uses adaptive batch sizes from 128 to 1024 for iterative model training and validation. The proposed model can handle imbalanced classes and un-normalized data efficiently. A state-of-art HAR dataset is also generated in an open environment to get the activity data of an uncontrolled scenario. With minimal data preprocessing and data augmentation, the model is tested, and the proposed model managed to get the highest accuracy of 99.29% with an average loss of 0.08
$\pm$
0.136%. The presented model is also tested with two public datasets named- mHealth and MotionSense and achieved an accuracy of 99.5% and 99.8%, respectively. The proposed model outperforms all the previous benchmarks and approaches by a good margin.