5 citations · 5 across the 5 of their papers we have counts for
5 papers
Machine Learning-Based Real-Time Detection of Compensatory Trunk Movements Using Trunk-Wrist Inertial Measurement Units
Jannis Gabler, Clément Lhoste, Max Quast +5
Compensatory trunk movements (CTMs) are commonly observed after stroke and can lead to maladaptive movement patterns, limiting targeted training of affected structures. Objective,…
Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input
Lorenzo Vianello, Clément Lhoste, Emek Barış Küçüktabak +3
Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, t…
Unidirectional Human-Robot-Human Physical Interaction for Gait Training
Lorenzo Amato, Lorenzo Vianello, Emek Baris Kucuktabak +6
This work presents a novel rehabilitation framework designed for a therapist, wearing an inertial measurement unit (IMU) suit, to virtually interact with a lower-limb exoskeleton w…
Deep-Learning Estimation of Weight Distribution Using Joint Kinematics for Lower-Limb Exoskeleton Control
Clément Lhoste, Emek Barış Küçüktabak, Lorenzo Vianello +4
In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be…
Exoskeleton-Mediated Physical Human-Human Interaction for a Sit-to-Stand Rehabilitation Task
Lorenzo Vianello, Emek Barış Küçüktabak, Matthew Short +4
Sit-to-Stand (StS) is a fundamental daily activity that can be challenging for stroke survivors due to strength, motor control, and proprioception deficits in their lower limbs. Ex…