An Efficient and Lightweight Deep Learning Model for Human Activity Recognition on Raw Sensor Data in Uncontrolled Environment
Nurul Amin Choudhury, Badal Soni
Abstract
Human activity recognition (HAR) is the process of identifying daily living activities using a set of sensors and optimal learning algorithms. It is a convoluted process, as there is no straightforward way to associate human action with the induced sensor data. Most of the work on HAR is done on highly augmented and pre-processed data. It optimizes performance but introduces intense data pre-processing and feature engineering overhead in real-time activity recognition. This article proposes an efficient and lightweight CNN-long short term memory (LSTM) model for enhanced activity classification on raw sensor data in an uncontrolled environment. It used convolution cum memory functionalities of the CNN-LSTM layer to extract spatial and temporal features for distinguished feature analysis and also classifies different human activities using dense classification layers. A state-of-the-art inbuilt smartphone sensor-based HAR dataset is also generated for six daily living activities in an uncontrolled environment for getting real-world activity data. With zero data pre-processing and augmentation, our proposed 1-D convolution (Conv1D)-based CNN-LSTM model out-completed all the incorporated conventional models and achieved the highest accuracy of 98% in optimized computational time. Also, the loss on real-world test data was minimum, with the loss perimeter advantage of 68% and 158% with LSTM and artificial neural network (ANN) models, respectively.