Interacting with a “Transparent” Upper-Limb Exoskeleton: A Human Motor Control Approach

Bastide, Simon; Vignais, Nicolas; Geffard, Franck; Berret, Bastien

doi:10.1109/iros.2018.8593991

Cited by 19 publications

(38 citation statements)

References 27 publications

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“…19,31 Nevertheless, this method is not often used because few exoskeletons include force sensors as standard equipment. More often, the robot's control is based on a closed-loop position control 1,9,19,21 as most robotic devices use incremental encoders as on-board sensors. Thus, the most direct solution for a better transparency appears to be controlling the robot so that it compensates its own dynamics.…”

Section: Assess and Improve Transparencymentioning

confidence: 99%

“…52 It has been shown that the wearing of a "transparent" exoskeleton tends to slow down the movement whatever the amplitude. 9 Therefore, we assume that simple indices such has movement duration and maximal velocity are relevant for a macroscopic approach in order to assess the transparency of the tested control laws and their effects on human movement kinematics. We also assume that the conservation of the "isochrony" principle (i.e., approximately affine relationships between amplitude and duration or velocity) is necessary to deem a control law as "transparent".…”

Section: Transparency Assessmentmentioning

confidence: 99%

“…Furthermore, the possible industrial applications may be challenged by the evidence of an average 30% movement slowdown when wearing a robotic "transparent" exoskeleton (see below) of the upper limb, 9,10 which could result in a critical decrease in productivity. Besides hardware and technical issues, their lack of concrete usability is mainly due to the fact that only few exoskeleton devices allow a symbiotic interaction with humans.…”

Section: Introductionmentioning

confidence: 99%

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An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

et al. 2021

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SUMMARY Over the past decade, research on human–robot collaboration has grown exponentially, motivated by appealing applications to improve the daily life of patients/operators. A primary requirement in many applications is to implement highly “transparent” control laws to reduce the robot impact on human movement. This impact may be quantified through relevant motor control indices. In this paper, we show that control laws based on careful identification procedures improve transparency compared to classical closed-loop position control laws. A new performance index based on the ratio between electromyographic activity and limb acceleration is also introduced to assess the quality of human exoskeleton interaction.

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Section: Assess and Improve Transparencymentioning

confidence: 99%

Section: Transparency Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

et al. 2021

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“…During the interaction, human and robotic forearm dynamics are coupled, which can be modeled as follows [17]:…”

Section: System Specifications a Exoskeletonmentioning

confidence: 99%

Controlling an Exoskeleton with EMG Signal to Assist Load Carrying: A Personalized Calibration

Treussart

Geffard

Vignais

et al. 2019

2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE)

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Implementing an intuitive control law for an upperlimb exoskeleton to perform force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to calibrate electromyography (EMG) data in order to detect the intention to lift or put down a charge while wearing an upper-limb exoskeleton. Based on a low-cost EMG sensor bracelet placed around the arm (Myo armband, Thalmics Lab, Ontario), a subject-specific mapping procedure is implemented to discriminate motion intentions during lifting tasks with a 1-DoF upper-limb exoskeleton. The processing is divided into two main parts: (i) direction estimation with an artificial neural network, and (ii) a model-based intensity prediction. The mapping procedure has been tested on 7 healthy participants with a precision of 96.9 ± 3.1% for the classification and a RMS Error of 3.8 ± 0.8N at the end effector. This study opens up the way for fast-deployment applications involving exoskeletons or cobots.

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“…All these applications are conditioned by several critical functionalities including transparency [7] and the ability to compensate for the user's weight [8]. Transparency is the ability of an exoskeleton to inuence human movement as little as possible when worn [9] and has already been extensively studied [10,11,12,13,14]. It is for instance critical at the end of a rehabilitation process to ensure that the robot will not lead the user to adopt abnormal motor patterns.…”

Section: Introductionmentioning

confidence: 99%

A New Weight Compensation Model Considering Joint Misalignments Monitored by a Feedforward Impedance Control Method Applied To an Active Upper-Limb Exoskeleton

Verdel

Bastide

Vignais

et al. 2021

Preprint

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Background Active exoskeletons are promising devices for improving rehabilitation procedures in patients. In particular, exoskeletons implementing human limb's weight support (WS) are interesting to restore some mobility in patients with muscle weakness. Using active exoskeletons should result in accurate and generic WS but its effect on human motor control will critically depend on the position of the user within the exoskeleton and the characteristics of the control law. Methods The present study aims at improving WS 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 are respectively conducted on 29, 17 and 19 participants who performed posture maintenance and pointing movements with the forearm in the sagittal plane. The first two experiments were used to build an accurate WS control law and the third experiment was conducted to compare the effects of different WS control laws on human movement and assess their quality. During these three experiments, kinematic data and eletromyographic activity of elbow flexors and extensors were measured. Interaction forces were measured with a force/torque sensor placed between the human segment and the robot link. Results The introduction of joint misalignments in the WS model allowed to drastically reduce the model errors in terms of weight estimation. The use of a feedforward architecture based on model and errors learned during experiments, coupled to a force feedback, allowed to reduce model tracking errors in both static and dynamic conditions during vertical movements, which induce substantial variations of gravitational torques. Overall, WS did not affect the general kinematic motion parameters of the participants and decreased the activity of antigravity muscles (flexors). However, WS increased the activation of extensors because weight is usually exploited by humans to accelerate a limb downward. Conclusion A new weight compensation model considering joint misalignments was introduced and data showed their prominent role on WS accuracy and homogeneity. Three WS control laws were compared and results indicated that classical control methods were not sufficient to provide an accurate tracking of the weight model during dynamic vertical movements but that introducing simple feedforward terms learned from previous measures could significantly improve WS accuracy. Accordingly, WS reduced significantly activity in flexors in both static and dynamic conditions. Nevertheless, WS tended to increase the activity of extensors, which might be an important factor in a rehabilitation perspective. Indeed, if the present WS control law will be very helpful to allow patients accelerating the arm upward despite some muscle weakness, it may have an opposite effect when accelerating the arm downward. A partial WS controller could thus be more appropriate in rehabilitation applications.

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Interacting with a “Transparent” Upper-Limb Exoskeleton: A Human Motor Control Approach

Cited by 19 publications

References 27 publications

An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

Controlling an Exoskeleton with EMG Signal to Assist Load Carrying: A Personalized Calibration

A New Weight Compensation Model Considering Joint Misalignments Monitored by a Feedforward Impedance Control Method Applied To an Active Upper-Limb Exoskeleton

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