Wearable IoT and Artificial Intelligence Techniques for Leveraging the Human Activity Analysis

The integration of wearable Internet of Things (IoT) devices with advanced Artificial Intelligence (AI) techniques has significantly transformed human activity analysis (HAA), It enables precise, real-time monitoring of diverse physical behaviors. This research delves into the design of an efficient and accurate human activity recognition (HAR) system utilizing the WISDM dataset. The dataset comprises accelerometer and gyroscope data collected from wearable devices during various activities, offering a robust foundation for HAR. Despite advancements, existing systems face challenges in accurately capturing the spatial and temporal dependencies in time-series data, which often leads to suboptimal classification performance. To address this, a hybrid deep learning framework is proposed, combining Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. This combination ensures better learning by using CNNs for spatial features and LSTMs for time-based patterns, making activity analysis more accurate. The proposed model attains a remarkable classification accuracy of 99.4%, underscoring its efficacy in distinguishing between various activities in real-time scenarios. Key performance metrics, including Precision (99%), Recall (98.6%), Specificity (99.3%), and F1-Score (99.2%), are systematically evaluated, validating the model’s robustness, reliability, and practical applicability. Experimental results reveal the model’s consistent and exceptional performance, highlighting its superiority over existing state-of-the-art approaches. The findings underscore the practical viability of deploying such intelligent systems in real-world environments, representing a notable progression in the evolution of human activity analysis.