Shingo Okamoto
Papers
15
Total Citations
132
H-Index
5
About
Shingo Okamoto is a robotics and human-computer interaction researcher whose work bridges intelligent sensing, humanoid locomotion, and assistive technology. His research spans three interconnected domains: human activity recognition, biped robot development, and human-robot interaction, with a sustained focus on making machines more responsive to human behavior. Okamoto's most influential contribution, "New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition" (2021, 50 citations), introduces novel data structuring techniques that significantly enhance real-time activity recognition — a critical capability for healthcare robotics and collaborative human-robot systems. His earlier work on gesture-based human-robot interaction (2014, 26 citations) laid important groundwork for intuitive robot control interfaces. His biped robotics research is notably comprehensive, progressing from passive walking theory and CPG-based locomotion control to antagonistic actuation and, most recently, deep learning frameworks that enable robot motion synthesis from a single inertial sensor. This trajectory reflects a sophisticated integration of biomechanical insight and modern machine learning. His LocoESIS system further demonstrates a commitment to accessible, low-cost gait analysis with broad healthcare and rehabilitation applications. Collectively, Okamoto's work represents a coherent vision: enabling smarter, more human-aware robots through advances in sensing, learning, and locomotion engineering.
Research Focus
Key Achievements
Top Papers
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- 3Development of a Biped Walking Robot with Antagonistic Actuation11 citations · 2016
- 43D Map Building Using Mobile Robot with Scan Device6 citations · 2016
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Key Collaborators
Related papers
- A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor
- Imitation Control for Biped Robot Using Wearable Motion Sensor
- Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot
- Design and Control of a Lower Limb Rehabilitation Robot Based on Human Motion Intention Recognition with Multi-Source Sensor Information
- Robust Walking Control of Humanoids on Unknown Rough Terrain via Short Cycle Motion Generation Using Absolute Position Estimates
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