Human Motion Recognition Using UMIs
DOI:
https://doi.org/10.15662/IJEETR.2026.0802464Keywords:
Human Motion Recognition, Inertial Measurement Units (IMU), Activity Classification, Deep Learning, CNN-LSTM, IoT Integration, Wearable SensorsAbstract
Human Motion Recognition (HMR) has gained significant attention due to its wide applications in healthcare, sports analysis, rehabilitation, and human-computer interaction. This project focuses on recognizing and classifying human activities using data collected from Inertial Measurement Units (IMUs), which typically include accelerometers, gyroscopes, and sometimes magnetometers. These sensors capture motion dynamics such as acceleration, angular velocity, and orientation in real time.
The proposed system involves collecting raw sensor data from wearable IMU devices placed on different parts of the human body. The data is then preprocessed to remove noise and segmented into meaningful time windows. Feature extraction techniques are applied to derive statistical and frequency- domain features that effectively represent motion patterns. Machine learning algorithms such as Support Vector Machines (SVM), Random Forest, or deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are used for classification of activities such as walking, running, sitting, and standing.
The system aims to achieve high accuracy and real-time performance while maintaining low computational complexity, making it suitable for embedded and wearable applications. Experimental results demonstrate that IMU-based motion recognition provides a reliable and cost- effective solution compared to vision-based systems, especially in privacy-sensitive and indoor environments.
This project highlights the potential of IMU sensors in developing intelligent, portable, and scalable motion recognition systems for real-world applications.
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