An Efficient and Lightweight Deep Learning Model for Human Activity Recognition on Raw Sensor Data in Uncontrolled Environment

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.