Karur Krishna Sahithi

Papers

Total Citations

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About

Karur Krishna Sahithi is a rising researcher at the forefront of intelligent human-robot interaction, specializing in the development of proactive control systems for industrial exoskeletons. Her work directly addresses a critical bottleneck in exoskeleton adoption: the control delay that hinders seamless human-machine collaboration. Sahithi’s major contributions lie in applying advanced machine learning and deep learning architectures—specifically CNN-LSTM models—to decode bio-signals, such as electromyography (EMG), for real-time intention prediction. Her most cited work, "Continuous Intention Prediction of Lifting Motions Using EMG-Based CNN-LSTM" (2024, 14 citations), demonstrates a novel method to anticipate user movements before they occur, enabling exoskeletons to provide timely, proactive assistance during physically demanding tasks like lifting. Expanding on this, her 2023 paper (6 citations) focuses on recognizing lower-limb movement sequences, further refining the proactive control loop. Sahithi’s research is pivotal for enhancing worker safety and productivity in industrial settings, bridging the gap between advanced AI and practical, wearable robotics. Her recent exploration into Quality of Experience (QoE) features (2025) signals a broadening interest in user-centered design, ensuring that technological advancements align with dynamic human needs.