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About

Mohammad Shushtari is a robotics researcher whose work sits at the intersection of energy-efficient actuation and human–robot interaction, with a particular focus on lower-limb exoskeletons. His early research pioneered methods for online natural dynamics modification in multijoint robots, introducing a compliance adaptation framework that minimizes actuation forces during cyclic tasks—a contribution that has garnered 35 citations and laid the groundwork for energy-saving robotic systems. Shushtari’s “Adaptive Natural Oscillator” further advanced this line of inquiry, demonstrating how robots can exploit natural dynamics to improve efficiency. More recently, he has made significant strides in exoskeleton technology, developing novel approaches for system identification and interaction force estimation in the Indego exoskeleton (2023, 13 citations). His 2024 work on IMU-based real-time gait phase estimation using multi-resolution neural networks (11 citations) offers a practical, sensor-driven solution for adaptive exoskeleton control. Shushtari has also introduced the concept of a “human–exoskeleton interaction portrait,” a method for evaluating co-adaptation by analyzing muscle activity and interaction forces. His research bridges theoretical advances in adaptive control with tangible improvements in assistive robotics, making him a notable figure in the field of rehabilitation and wearable robotics.