Robotic knee exoskeleton helps reduce muscle effort during squatting
Design and Validation of a Robotic Knee Exoskeleton with Musculoskeletal Simulation-Derived Biological Torque Control
Robotics
Summary
Squatting can put a lot of strain on your knees and muscles. The authors designed a knee-supporting robot that helps reduce that effort by using a special control system based entirely on computer simulations. This means they didn’t have to collect data directly from the robot before using it. In tests with people, the robot lowered the muscle activity and knee forces during squatting, making the movement easier.
What this means in practice
- •For prosthetics designers: Create knee exoskeleton designs that reduce muscle strain without needing device-specific data collection.
- •For physical rehabilitation clinics: Use simulation-derived controllers in knee assistive devices to support patients during squatting exercises with less calibration effort.
Authors
Rajiv V. Joshi, Jimin An, Ilseung Park, Dawit Lee, Inseung Kang
Abstract
Data-driven controllers have enabled lower-limb robotic exoskeletons to adapt assistance to the user across many tasks. A leading example is biological torque control, which estimates the user's own joint moment from onboard sensors and assists in proportion to that estimate. However, its estimators are trained on data recorded with the target device, so deploying the controller on new hardware requires collecting new data. Existing open-source efforts release hardware, software, or controllers separately, so this data collection must still be repeated on the assembled device. Here, we present an open-source knee exoskeleton, built from off-the-shelf components and widely available fabrication tools, with a biological torque controller derived entirely from simulation. A muscle-driven imitation policy reproduces squatting motions from an open biomechanics dataset, and the resulting knee moments are distilled into an estimator that uses only onboard sensors, so no training data are recorded on the device. In seven able-bodied participants, assistance reduced peak biological knee extension moment by 21.5% and peak vastus medialis activation by 26.7% during squatting, relative to squatting without the exoskeleton (p<0.05). Our results demonstrate that a biological torque controller derived from simulation can transfer to physical hardware without device-specific data collection. Because the hardware and training pipeline are released together, research groups can train and compare knee exoskeleton controllers on shared hardware without additional data collection overhead.