EMG-based Wrong Posture Detection System using Raspberry Pi and Cloud-based Visualization

Human-computer interaction experts investigate posture detection with cutting-edge methodologies and electromyography (EMG) signals. This study presents an innovative system that provides users with real-time feedback via cloud visualization, electromyography (EMG) sensors, and a Raspberry Pi. To obtain more comprehensive data, sensor fusion methods integrate gyroscopes and accelerometers, while state-of-the-art machine learning algorithms are employed to improve the precision of posture detection. The proposed approach provides a precise and nonintrusive substitute for incorrect posture recognition to avoid the shortcomings of current technologies. The report emphasizes sensor integration, pattern recognition, and classification algorithms as critical elements in enhancing the system's dependability. In addition to strategically placed accelerometers, the methodology incorporates a deep learning framework of LSTM. Subsequent investigations should concentrate on developing real-time processing algorithms and exploring alternative sensor modalities to obtain more comprehensive data, given the promising practical applications of posture detection systems.