Abstract
A natural way for humans to connect with computers is through gestures, which have many applications in robotics and gaming. Despite this, problems with latency, precision, and adaptability in handling dynamic movements make real-time implementation difficult. In dynamic settings like gaming or robotic navigation, reactions are sometimes delayed, and current systems frequently have worse accuracy. The computational complexity of identifying diverse hand or body movements, noisy sensor data, and the requirement to adapt across different user behaviours are the root causes of these issues. This research suggests a method, GR-DTWHMM, a real-time system that uses Dynamic Time Warping (DTW) and Hidden Markov Models (HMM) to fix these problems. HMM offers strong sequence-based gesture recognition (GR) by capturing the temporal dynamics of hand movements. To compensate for differences in execution speed or timing, DTW guarantees that gesture sequences are aligned in real-time. A Kalman Filter also improves the quality of the incoming signal by reducing sensor noise. Robotics and gaming use cases, including controlling virtual characters and navigating drones, are used to assess the system. The results demonstrate a 25% decrease in latency and a 30% enhancement in recognition accuracy when contrasted with traditional methods. When HMM and DTW are used together, they improve performance in various contexts by being flexible in identifying complicated movements. This extensible framework raises the bar for sophisticated HCI systems in ever-changing contexts by providing an effective method for real-time gesture control.
Published Version
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