Gesture Recognition and Human-Computer Interaction for User Control Applications Using Embedded Sensors

Gesture recognition and human-computer interaction (HCI) have garnered interest recently because they can provide intuitive and natural interfaces for several applications. This paper proposes real-time gesture detection and HCI technique using embedded sensors. Our method uses integrated sensors and machine learning techniques to recognize gestures without extra equipment or complicated setups. Real-time gesture detection is achieved by carefully placing integrated accelerometers and gyroscopes on the person or surroundings. These sensors send user motions to a central processing unit. The system employs deep neural networks to analyze sensor data and properly detect motions. It evaluates our system using numerous user gestures in trials. Our approach accurately recognizes several motions in real-time. Our system's accuracy and efficiency outperform existing gesture recognition algorithms. Our technology also controls virtual reality, interactive gaming, and smart home automation. Real-time gesture detection makes HCI smooth and immersive. This eliminates traditional input devices and makes interactions more natural. The suggested approach is portable, inexpensive, and requires little hardware. Embedded sensors may assist wearable devices, IoT systems, and smart surroundings without extra hardware. Finally, integrated sensors enable real-time gesture detection and HCI. Our framework perceives motions with accuracy, productivity, and adaptability. HCI integration allows intuitive and natural interactions in many disciplines. This work advances gesture detection and HCI technologies to improve user experiences.