Human Activity Recognition with MATLAB

In modern society, with the continuous advancement of technology, human activity recognition algorithms have become a fundamental technique. This technology finds applications across numerous domains including security systems, healthcare monitoring, sports analytics, and more. By analyzing human movement patterns and physiological indicators, these algorithms can identify individual identities and behavioral states. The implementation typically involves preprocessing sensor data (such as accelerometer/gyroscope readings), extracting temporal and spatial features through techniques like sliding window analysis, and employing machine learning classifiers (e.g., SVM or CNN) for pattern recognition. The application scope spans from large-scale public venues to personal wearable devices, significantly enhancing both security measures and user convenience. This article provides comprehensive MATLAB-based implementations with code annotations covering key functions for data preprocessing, feature extraction (including statistical moments and frequency-domain features), and model training procedures to assist developers in understanding and applying this technology effectively.