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@article{180711,
        author = {Rupkatha De and Aritro Saha},
        title = {KrishnaVision: A Multimodal Virtual Interface  Combining MediaPipe-Hands Optimization and Gemini  AI for Context- Aware HCI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2909-2917},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180711},
        abstract = {This work introduces Krishna Vision, an innovative virtual mouse system that is synergistically bringing together MediaPipe's hand tracking and Gemini's multimodal AI to develop a human-computer interface that can adapt. Our system introduces three primary innovations: [1] Velocity-damped cursor control with a 63% jitter reduction by derivative-based momentum modeling, [2] Gemini-driven contextual command resolution with environment-sensing gesture sensitivity control, and [3] Dynamic input modality prioritization through real-time confidence-scoring hybrid state machines. Results from benchmarks achieve 97.3% accuracy for gesture recognition at 22ms latency while surpassing ResNet-50 baselines by 15.2% with 41% reduced power usage. Gemini integration of the system provides new functionalities such as screenshot description (89.3% success) and inter-application memory, filling an important contextual awareness gap seen in current solutions. Large-scale user studies involving 45 users under varied lighting/noise conditions ensure the robustness of the approach, demonstrating 91.5% success on complicated hybrid commands.},
        keywords = {Adaptive HCI, MediaPipe Optimization, Multimodal Fusion, Gemini AI, Gesture-Voice Integration},
        month = {June},
        }