Sensor-Based Human Activity Recognition in Smart Homes Using Depthwise Separable Convolutions

The recent enhancement of computerized electronic gadgets has led to the acceptance of smart home sensing applications, stimulating a need for related services and products. As a result, the ever-increasing volume of data necessitates the application of advanced deep learning to the automated identification of human activity. Over the years, several deep learning models that learn to categorize human activities have been proposed, and several experts have used convolutional neural networks. To tackle the human activity recognition (HAR) problem in smart homes, we suggest employing a depthwise separable convolution neural network (DS-CNN). Instead of standard 2D convolution layers, the network uses depth-wise separable convolution layers. DS-CNN is a fantastic performer, particularly with limited datasets. DS-CNN also minimizes the number of trainable parameters while improving learning efficiency by using a compact network. We tested our technique on benchmark HAR-based smart home datasets, and the findings reveal that it outperforms the current state of the art. This study shows that using depthwise separable convolutions significantly improves performance (accuracy=92.960, precision=91.6, recall=90, F-score=93) compared to classical CNN and baseline methods.