A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

• Published in: Frontiers in neurorobotics Vol. 12; p. 18
• Main Authors: Torricelli, Diego, Cortés, Camilo, Lete, Nerea, Bertelsen, Álvaro, Gonzalez-Vargas, Jose E, Del-Ama, Antonio J, Dimbwadyo, Iris, Moreno, Juan C, Florez, Julian, Pons, Jose L
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 27.04.2018; Frontiers Media S.A
• Subjects: Ankle; benchmarking; Exoskeleton; Gait; Hip; Human subjects; Kinematics; Laboratories; lower limb; Metabolism; Neuroscience; Posture; rehabilitation; Robotics; Robots; skeletal modeling; Walking; wearable robot; Spain; rehabilitation; wearable robot; benchmarking; skeletal modeling; lower limb; walking
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2018.00018

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.