Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation

Abstract

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

Progress and prospects of the human–robot collaboration

“…A solution to this issue can be obtained by the introduction of shared communication modalities [Green et al, 2008,Lackey et al, 2011,Lackey et al, 2011. Alternatively, robotic learning techniques such as: gradual mutual adaptation [Ikemoto et al, 2012, Peternel et al, 2016a, reinforcement learning [Palunko et al, 2014] or learning from demonstration , Lawitzky et al, 2012a, Rozo et al, 2015 can be exploited to weaken the communication loops' demands (e.g. bandwidth, number of feedback modalities) due to an increased level of robot autonomy.…”

Потоковая Обработка Многоканальных Сигналов Электроэнцефалограммы С Использованием Технологии Параллельных Вычислений На Графических Процессорах NVIDIA CUDA

“…В частности, последнее необходимо для разработки интерфейсов мозг−компьютер (ИМК) [6]. В настоящее время ИМК активно развиваются и применяются, на-пример, для управления простыми движениями [7], экзоскелетами и роботами [8], предсказания приступов абсанс-эпилепсии [9, 10] и т. д. Для дальнейшего развития ИМК должны использоваться продвинутые методы анализа электроэнцефалограмм (ЭЭГ), основанные на анализе большого числа входных данных.…”

Adaptive Natural Oscillator to exploit natural dynamics for energy efficiency

“…Due to the fundamental role of frequency in energy consumption, locomotion speed, and stability, the frequency-adaptation is extensively studied and applied in robotic systems [32,33]; especially in gait assistance and rehabilitation devices [34][35][36]. As another example, [37] shows that a simple frequency adaptation, through phaseresetting, can improve the gait-stability.…”