Yves Zimmermann

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Zimmermann

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Yves

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0000-0003-2139-2181

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Publications 1 - 10 of 15

Performing Activities of Daily Living with a Fully Actuated 9-DOF Shoulder and Arm Exoskeleton
Zimmermann, Yves; Hutter, Marco (2022)
We developed a fully actuated exoskeleton that covered all nine relevant degrees of freedom of the human arm while providing enough range of motion, speed, strength, and haptic-rendering function to provide meaningful neurorehabilitation therapy to severely and mildly affected patients. The unique kinematic structure of the robot and the large impedance bandwidth of haptic rendering allowed training for most activities of daily living with real or virtual objects. Thus, next to neurorehabilitation, this robot could find an application in testing different actuation and control methods (e.g., actuation synergies) for evaluation of portable assistive robot concepts. Further, potential application fields are in telemanipulation, and as a haptic-device for entertainment applications.
Towards Dynamic Transparency: Robust Interaction Force Tracking Using Multi-Sensory Control on an Arm Exoskeleton
Zimmermann, Yves; Kücüktabak, Emek Baris; Farshidian, Farbod; et al. (2020)
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
A high-quality free-motion rendering is one of the most vital traits to achieve an immersive human-robot interaction. Rendering free-motion is notably challenging for rehabilitation exoskeletons due to their relatively high weight and powerful actuators required for strength training and support. In the presence of dynamic human movements, accurate feedback linearization of the robot’s dynamics is necessary to allow for a linear synthesis of interaction wrench controllers. Hence, we introduce a virtual model controller that uses two 6-DoF force sensors to control the interaction wrenches of a multiDoF torque-controlled exoskeleton over the joint accelerations and inverse dynamics. Furthermore, we propose a disturbance observer for controlling the joint acceleration to diminish the influence of modeling errors on the inverse dynamics. To provide a high-bandwidth, low-bias estimation of the system’s acceleration, we introduce a bias-observer which fuses the information from joint encoders and seven low priced IMUs. We have validated the performance of our proposed control structure on the shoulder and arm exoskeleton ANYexo. The experimental comparison of the controllers shows a reduction of the felt inertia and maximum reflected joint torque by a factor of more than three compared to state of the art. The controllers’ robustness w.r.t. a model mismatch is validated. The experiments show that the closed-loop acceleration control improves the tracking, particularly at joints with low inertia. The proposed controllers’ performance sets a new benchmark in haptic transparency for comparable devices and should be transferable to other applications.
Polymorphic Control Framework for Automated and Individualized Robot-Assisted Rehabilitation
Sommerhalder, Michael; Zimmermann, Yves; Song, Jaeyong; et al. (2024)
IEEE Transactions on Robotics
Robots were introduced in the field of upper-limb neuro-rehabilitation to relieve the therapist from physical labor, and to provide high-intensity therapy to the patient. A variety of control methods were developed that incorporate patients' physiological and biomechanical states to adapt the provided assistance automatically. Higher level states such as selected type of assistance, chosen task characteristics, defined session goals, and given patient impairments are often neglected or modeled into tight requirements, low-dimensional study designs, and narrow inclusion criteria so that presented solutions cannot be transferred to other tasks, robotic devices or target groups. In this work, we present the design of a modular high-level control framework based on invariant states covering all decision layers in therapy. We verified the functionality of our framework on the assistance and task layer by outlaying the invariant states based on the characteristics of twenty examined state-of-the-art controllers. Then, we integrated four controllers on each layer and designed two algorithms that automatically selected suitable controllers. The framework was deployed on an arm rehabilitation robot and tested on one participant acting as a patient. We observed plausible system reactions to external changes by a second operator representing a therapist. We believe that this work will boost the development of novel controllers and selection algorithms in cooperative decision-making on layers other than assistance, and eases transferability and integration of existing solutions on lower layers into arbitrary robotic systems.
Orthosis and Method for Stabilizing the Position of the Scapula, System for Moving an Arm and Method for Lifting a Load
Georgarakis, Anna-Maria; Zimmermann, Yves; Riener, Robert (2021)
Human-Robot Attachment System for Exoskeletons: Design and Performance Analysis
Zimmermann, Yves; Song, Jaeyong; Deguelle, Cédric; et al. (2023)
IEEE Transactions on Robotics
Exoskeleton robots found applications in neurorehabilitation, telemanipulation, and power augmentation. The human-robot attachment system of an exoskeleton should transmit all interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to insufficient stiffness for high-performance force control, time-consuming adaption processes, and/or bulkiness. Therefore, we developed an augmented attachment system for a recent fully actuated 9-DOF upper limb exoskeleton. The proposed system was compared to a conventional solution in a case study with four participants. The proposed attachment system lowered the relative motion between human and robot under static loads for all defined landmarks by 45% on average. The occurrence of undesired contacts in the trials was mitigated by 74%, thus, improving conditions for closed-loop force control. Further, the proposed system adapted better to the user’s anatomy facilitating more accurate alignment and less obstruction. On average, self-attachment took 43(8.3) s to don(doff). Thereby, the alignment of anatomic landmarks typically had less than 15mm offset to a thorough expert alignment, making self-attachment eligible. The augmented attachment system and the insights gained by the case study are expected to enable improvement of the physical human-robot interaction of exoskeletons.
Supporting and Stabilizing the Scapulohumeral Rhythm with a Body- or Robot-Powered Orthosis
Georgarakis, Anna-Maria; Zimmermann, Yves; Wolf, Peter; et al. (2022)
IEEE Transactions on Medical Robotics and Bionics
The versatile functionality of the human upper limb is owed to the coordinated rotation of the scapula and humerus, a pattern called the scapulohumeral rhythm (SHR). Various medical conditions can alter the SHR, frequently leading to limitations in activities of daily living. However, to date, supporting the SHR in practice is often not feasible. We present a textile orthosis that assists the SHR both in stand-alone use and in combination with the ANYexo, a therapy exoskeleton, or the Myoshirt, an assistive exomuscle. The SHR Orthosis comprised a textile harness and a scapula interface that was coupled with the upper arm to promote scapular upward rotation. In a technical evaluation including four participants without impairments and one with a partial hemiparesis, the SHR Orthosis followed the desired scapular rotation with an average deviation of less than 5 %, thus providing accurate support and guidance towards the physiological SHR. The SHR Orthosis substituted for <=42 % of the normal forces, and <=19.6 % of the tangential forces required for scapular stabilization and rotation, providing sufficient support for patients with remaining muscular function. At last, the SHR Orthosis provides practicable scapula support in daily life, during conventional therapy, and in combination with assistive and therapy robots.
Trajectory Optimization Framework for Rehabilitation Robots with Multi-Workspace Objectives and Constraints
Sommerhalder, Michael; Zimmermann, Yves; Simovic, Leonardo; et al. (2023)
IEEE Robotics and Automation Letters
Robot-assisted neurorehabilitation requires trajectories between arbitrary poses in the patient's range of motion. Data-driven optimization methods, such as Learning by Demonstration, are well suited to replicate complex multi-joint movements. However, these methods lack individualization to patient-, robot- and exercise-specific constraints. We propose a hybrid optimization framework that combines cost-based objectives, such as minimizing jerk, with the data-driven optimization of a reference trajectory. The objectives can be individually weighted in a sequential quadratic program with application-related constraints represented in intuitive workspaces. We demonstrated that trajectories recorded from an existing upper-limb activity dataset could be adapted to the personal needs of a healthy participant with simulated impairments, the hardware-specific robot topology, and changes in the exercise setup. Furthermore, we showed how redundancies in the degrees of freedom of the arm can be exploited: For example, an elbow angle movement of 30.4${^\circ }$ was compensated entirely through increased wrist movement in a reach-goal task. In addition to making sequential quadratic programming more accessible to the field of rehabilitation robotics, our framework improves the variability and individualizability of generated trajectories for patients, provides more adaptation possibilities to the therapist, and enables sharing of recorded movement data between robotic platforms, patients, and exercises.
ANYexo 2.0: A Fully-Actuated Upper-Limb Exoskeleton for Manipulation and Joint-Oriented Training in all Stages of Rehabilitation
Zimmermann, Yves; Sommerhalder, Michael; Wolf, Peter; et al. (2023)
IEEE Transactions on Robotics
We developed an exoskeleton for neurorehabilitation that covered all relevant degrees of freedom of the human arm while providing enough range of motion, speed, strength, and haptic-rendering function for therapy of severely affected (e.g., mobilization) and mildly affected patients (e.g., strength and speed). The ANYexo 2.0, uniting these capabilities, could be the vanguard for highly versatile therapeutic robotics applicable to a broad target group and an extensive range of exercises. Thus, supporting the practical adoption of these devices in clinics. The unique kinematic structure of the robot and the bio-inspired controlled shoulder coupling allowed training for most activities of daily living. We demonstrated this capability with 15 sample activities, including interaction with real objects and the own body with the robot in transparent mode. The robot’s joints can reach 200%, 398%, and 354% of the speed required during activities of daily living at the shoulder, elbow, and wrist, respectively. Further, the robot can provide isometric strength training. We present a detailed analysis of the kinematic properties and propose algorithms for intuitive control implementation.
Cooperative Goal Generation for Reaching Tasks in Robot-Assisted Rehabilitation
Sommerhalder, Michael; Zimmermann, Yves; Riener, Robert; et al. (2023)
2023 International Conference on Rehabilitation Robotics (ICORR)
Robot-assisted neurorehabilitation requires automated generation of goal positions for reaching tasks in functional movement therapy. In state-of-the-art solutions, these positions are determined by a motivational therapy game either through constraints on the end-effector (2D or 3D games), or individual arm joints (1D games). Consequently, these positions cannot be adapted to the patients' specific needs by the therapist, and the effectiveness of the training is reduced. We solve this issue by generating goal positions using Gaussian Mixture Models and probability density maps based on the active range of motion of the patient and desired activities, while being compliant with existing game constraints. Therapists can modify the goal generation via an intuitive difficulty and activity parameter. The pipeline was tested on the upper-limb exoskeleton ANYexo 2.0. We have shown that the range of motion exploration rate could be altered from 0.39% to 5.9% per task and that our method successfully generated a sequence of reaching tasks that matched the range of motion of the selected activity, up to an inlier accuracy of 78.9%. Results demonstrate that the responsibilities of the therapy game (i.e., motivating the patient) and the therapists (i.e., individualizing the training) could be distributed properly. We believe that with our pipeline, effective cooperation between the involved agents is achieved, and the provided therapy can be improved.
Score rectification for online assessments in robot-assisted arm rehabilitation
Sommerhalder, Michael; Zimmermann, Yves; Knecht, Manuel; et al. (2022)
at - Automatisierungstechnik ~ Special issue: AUTOMED 2021: Automation in Medical Technology
Relative comparison of clinical scores to measure the effectiveness of neuro-rehabilitation therapy is possible through a series of discrete measurements during the rehabilitation period within specifically designed task environments. Robots allow quantitative, continuous measurement of data. Resulting robotic scores are also only comparable within similar context, e.g. type of task. We propose a method to decouple these scores from their respective context through functional orthogonalization and compensation of the compounding factors based on a data-driven sensitivity analysis of the user performance. The method was validated for the established accuracy score with variable arm weight support, provoked muscle fatigue and different task directions on 6 participants of our arm exoskeleton group on the ANYexo robot. In the best case, the standard deviation of the assessed score in changing context could be reduced by a factor of 3.2. Therewith, we paved the way to context-independent, quantitative online assessments, recorded autonomously with robots.

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