Improving backdrivability in geared rehabilitation robots

Nef, Tobias; Lum, Peter S.

doi:10.1007/s11517-009-0437-0

Cited by 60 publications

(37 citation statements)

References 16 publications

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“…These results are however encouraging, and they have to be balanced with the literature illustrating the challenge related to reduce the metabolic cost of free walking with an assistive device [9,37,38,51]. Possible directions to improve this result would require to (i) make the LOPES more transparent [25,46], (ii) increase the level of assistance, (iii) give longer familiarization trials to the users (Fig. 8 already suggests a further improvement at the very end of the trial), and (iv) develop a more sensible assistance scheme.…”

Section: Discussionmentioning

confidence: 99%

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

Ronsse

Lenzi

Vitiello

et al. 2011

Med Biol Eng Comput

165

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In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot's own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.

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Section: Discussionmentioning

confidence: 99%

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

Ronsse

Lenzi

Vitiello

et al. 2011

Med Biol Eng Comput

165

View full text Add to dashboard Cite

show abstract

“…Friction of the motor gear combination is velocity dependent and identified by using a constant velocity controller [22]. We moved each joint with constant velocities from 0.1 to 60.0°/s over its entire range of motion.…”

Section: Friction Modelmentioning

confidence: 99%

“…Since static friction is not modeled, the prediction errors for joint velocities near zero are bigger (see Figure 5 at the beginning or end of the movement). The static friction parameters could also be extracted from the friction measurements and added to the model, but the nonlinearity of static friction makes it difficult to validate or use the model without direct measurement of the interaction forces with force or torque sensors [22].…”

Section: Inverse Dynamic Robot Modelmentioning

confidence: 99%

Estimating the patient�s contribution during robot-assisted therapy

Guidali¹,

Keller²,

Klamroth-Marganska³

et al. 2013

JRRD

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Abstract-Robot-assisted therapy has become increasingly common in neurorehabilitation. Sophisticated controllers have been developed for robots to assist and cooperate with the patient. It is difficult for the patient to judge to what extent the robot contributes to the execution of a movement. Therefore, methods to comprehensively quantify the patient's contribution and provide feedback are of key importance. We developed a method comprehensively to estimate the patient's contribution by combining kinematic measures and the motor assistance applied. Inverse dynamic models of the robot and the passive human arm calculate the required torques to move the robot and the arm and build, together with the recorded motor torque, a metric (in percentage) that represents the patient's contribution to the movement. To evaluate the developed metric, 12 nondisabled subjects and 7 patients with neurological problems simulated instructed movement contributions. The results are compared with a common performance metric. The estimation shows very satisfying results for both groups, even though the arm model used was strongly simplified. Displaying this metric to patients during therapy can potentially motivate them to actively participate in the training.

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“…Even though healthy subjects could easily move the robot, the inertia of the exoskeleton and the static friction of the actuators impeded full transparency of the device. By adding the recently developed static friction compensation method, we could further improve the transparency [28]. Extending the robot with acceleration sensors to the robot would be needed to also compensate for inertia.…”

Section: Patient-cooperative Controlmentioning

confidence: 99%

A robotic system to train activities of daily living in a virtual environment

Guidali

Duschau-Wicke

Broggi

et al. 2011

Med Biol Eng Comput

Self Cite

151

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In the past decade, several arm rehabilitation robots have been developed to assist neurological patients during therapy. Early devices were limited in their number of degrees of freedom and range of motion, whereas newer robots such as the ARMin robot can support the entire arm. Often, these devices are combined with virtual environments to integrate motivating game-like scenarios. Several studies have shown a positive effect of game-playing on therapy outcome by increasing motivation. In addition, we assume that practicing highly functional movements can further enhance therapy outcome by facilitating the transfer of motor abilities acquired in therapy to daily life. Therefore, we present a rehabilitation system that enables the training of activities of daily living (ADL) with the support of an assistive robot. Important ADL tasks have been identified and implemented in a virtual environment. A patient-cooperative control strategy with adaptable freedom in timing and space was developed to assist the patient during the task. The technical feasibility and usability of the system was evaluated with seven healthy subjects and three chronic stroke patients.

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Improving backdrivability in geared rehabilitation robots

Cited by 60 publications

References 16 publications

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

Estimating the patient�s contribution during robot-assisted therapy

A robotic system to train activities of daily living in a virtual environment

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