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@article{184633,
        author = {DURAIARASU E and Ananthanarayanan B},
        title = {Human Activity Recognition and Biomechanical Analysis Using MPU6050 IMU Data for Anthropodic Movements},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3141-3146},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184633},
        abstract = {In this paper, we present a comprehensive study on human activity recognition (HAR) and biomechanical motion analysis using inertial measurement unit (IMU) data collected from a MPU6050 sensor placed on the human body. The dataset includes accelerometer and gyroscope signals corresponding to anthropodic activities such as standing, walking, flexion, ex- tension, and forward leg movements. We detail the dataset acquisition procedure, signal preprocessing, feature extraction techniques, and machine learning classification frameworks used to differentiate the activities. Additionally, biomechanical insights are derived from kinematic features extracted from raw IMU data. Experimental results using machine learning classifiers demonstrate promising recognition accuracy, which highlights the potential of this approach for applications in wearable rehabilitation systems and ergonomic assessments.},
        keywords = {Human activity recognition, IMU, MPU6050, ac- celerometer, gyroscope, biomechanical analysis, wearable sensors, anthropodic movements},
        month = {September},
        }