Human Weight Compensation With a Backdrivable Upper-Limb Exoskeleton: Identification and Control

Abstract

Active exoskeletons are promising devices for improving rehabilitation procedures in patients and preventing musculoskeletal disorders in workers. In particular, exoskeletons implementing human limb’s weight support are interesting to restore some mobility in patients with muscle weakness and help in occupational load carrying tasks. The present study aims at improving weight support of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments, for design and validation purposes, are conducted on a total of 65 participants who performed posture maintenance and elbow flexion/extension movements. The introduction of joint misalignments in the weight support model significantly reduced the model errors, in terms of weight estimation, and enhanced the estimation reliability. The introduced control architecture reduced model tracking errors regardless of the condition. Weight support significantly decreased the activity of antigravity muscles, as expected, but increased the activity of elbow extensors because gravity is usually exploited by humans to accelerate a limb downwards. These findings suggest that an adaptive weight support controller could be envisioned to further minimize human effort in certain applications.

A Trade-Off between Complexity and Interaction Quality for Upper Limb Exoskeleton Interfaces

“…Indeed, although this design did not allow for clearly reducing the interaction forces at the arm level, it implied limited additional inertia and was, overall, well-graded by the participants. Furthermore, it mainly introduces one large JM that can be efficiently identified following a simple procedure [50]. Finally, the proposed design seems to be suitable for clinical interventions or work tasks and for the burgeoning field of human movement studies involving exoskeletons [51,58,68].…”

“…The successive positions of the index reflective marker were low-pass filtered (Butterworth, fifth order, 5 Hz cut-off frequency) before numerical differentiation, which allowed for the computation of the velocity and acceleration profiles. Movement onset and end were defined with a threshold fixed at 5% of the peak velocity [36,[48][49][50]. The movement duration (MD) and amplitude were defined as the elapsed time and the traveled distance between these bounds, respectively.…”

Human movement modifications induced by different levels of transparency of an active upper limb exoskeleton

“…Robot dynamics are generally formulated as in Eq. 1 ( Gong et al, 2000 ; Siciliano et al, 2009 ; Moberg, 2010 ; Verdel et al, 2021b ; 2022a ), where q , and are respectively the joints position, velocity and acceleration vectors, is the inertia matrix, is the vector of centrifugal and Coriolis terms, is the gravity vector, μ C and μ v are respectively the Coulomb and viscous friction coefficients vectors and, finally, τ m is the motor torques vector. Usually, the different parameters, or more accurately their combinations into base parameters, can be identified with different exciting trajectories and nonlinear least square methods ( Verdel et al, 2021b ).…”

“…This control mode consists in following the human movement without altering it ( Pirondini et al, 2016 ; Just et al, 2018 ; Verdel et al, 2021b ), which in an ideal case would result in null interaction efforts. Such a control mode can serve as a baseline to design other common controllers, typically weight support for rehabilitation applications ( Just et al, 2017 ; 2020 ; Verdel et al, 2022a ).…”

The value of time in the invigoration of human movements when interacting with a robotic exoskeleton

“…Position data from the other markers was used as control to monitor residual motions. Position data were filtered (low-pass Butterworth, 5 Hz cutoff, fifth-order, zero-phase distortion, butter function from the scipy package) as in previous studies [55, 72, 77]. Then, velocity and acceleration were obtained by numerical differentiation.…”