A Comparative Study of CNN and RNN Architectures For Human Activity Recognition Using Wearable Sensors
Ch.G.V.N. Prasad, N Sravani, Bhavana T
This study presents a comparative analysis of Convolutional Neural Networks (CNNs) and Recurrent
Neural Networks (RNNs) for Human Activity Recognition (HAR) using data from wearable sensors. HAR
is crucial in various applications such as healthcare, fitness tracking, and smart environments, where
accurate recognition of human activities is essential. While traditional methods have been used in HAR,
the emergence of deep learning has significantly improved performance. In this work, we implemented
and evaluated both CNN and RNN models on a benchmark HAR dataset, focusing on key metrics such
as accuracy, precision, recall, and F1-score. The results demonstrate that CNNs outperform RNNs in
terms of overall accuracy and reliability, particularly in activities characterized by distinct spatial
patterns. However, RNNs remain competitive in recognizing activities with complex temporal dynamics.
These findings highlight the strengths and limitations of each architecture and provide insights into their
suitability for different types of HAR tasks.
