Human-Robot Synchrony: Flexible Assistance Using Adaptive Oscillators

We propose a novel method for movement assistance that is based on adaptive oscillators, i. e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion-extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

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Auke Ijspeert, Renaud Ronsse, Jesse van den Kieboom
2011
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https://infoscience.epfl.ch/record/171588?ln=fr

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Concepts associés (16)

Concepts associés

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Publications associées (3)

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

Auke Ijspeert, Renaud Ronsse, Jesse van den Kieboom

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.

Springer-Verlag2011

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Adaptive Oscillators with Human-in-the-Loop: Proof of Concept for Assistance and Rehabilitation

Auke Ijspeert, Renaud Ronsse, Jesse van den Kieboom

Most recent findings in robot-assisted therapy suggest that the therapy is more successful if the patient actively participates to the movement (“assistance-as-needed”). In the present contribution, we propose a novel approach for designing highly flexible protocols based on this concept. This approach uses adaptive oscillators: a mathematical primitive having the capacity to learn the high-level features of a quasi-sinusoidal signal (amplitude, frequency, offset). Using a simple inverse model, we demonstrate that this method permits to synchronize with the torque produced by the user, such that the effort associated with the movement production is shared between the user and the assistance device, without specifying any arbitrary reference trajectory. Simulation results also establish the method relevance for helping patients with movement disorders. Since our method is specifically designed for rhythmic movements, the final target is the assistance/rehabilitation of locomotory tasks. As an initial proof of concept, this paper focuses on a simpler movement, i.e. rhythmic oscillations of the forearm about the elbow.

IEEE2010

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In this paper we compare three noninvasive control methods for a robotic knee exoskeleton and asses their kinematic influences on the repetitive squatting motions of able-bodied human subjects. The motion of the subjects wearing the knee exoskeleton was also compared to the motion of the subjects performing the same task without using the assistance of the knee exoskeleton. We chose the squatting motion because it approximates common movements with high metabolic cost, such as standing up from a chair and ascending or descending the stairs. Beside the two classical robotic control approaches, i.e. the position control and the gravity compensation, we propose a method that is based on a single adaptive frequency oscillator combined with an adaptive Fourier series in a feedback loop. The method can extract frequency and phase of an arbitrary periodic signal in real-time. This method is particularly appropriate for controlling novel robotic assisting devices since it does not require complex signal sensing or user calibration. The results show that the total knee torque was increased while using the exoskeleton device compared to the squatting without the assistance of the exoskeleton device. In effect, there were significant kinematic adaptations observed when the exoskleton device assisted the motion of the subjects. However, no significant kinematic differences were found between different control methods. We conclude that an assistive device can augment the abilities of the able-bodied humans in the targeted joints (i.e. the joints provided with additional mechanical power) but, on the other hand, significantly alters whole body kinematics.

2013

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