Assist-as-needed Impedance Control Strategy for a Wearable Ankle Robotic Orthosis

Lopes, João; Pinheiro, Cristiana; Figueiredo, Joana; Reis, Luís Paulo; Santos, Cristina P.

doi:10.1109/icarsc49921.2020.9096186

Cited by 10 publications

(24 citation statements)

References 16 publications

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“…Joint angles and angular velocities have also been used without the ground contact information to transition states [126,[178][179][180][181][182]. In [47], in addition to the angle and angular velocity of the knee joint, the moment at the joint and the angular velocity of the leg are involved in state transitioning.…”

Section: Machine Learning Phase (Mlp)mentioning

confidence: 99%

“…The challenge is then to generate a set of gait trajectories that are comfortable, stable and able to overcome obstacles. For partial assistance, it is associated with impedance control [13,145,182,[205][206][207]. These trajectories can be played back over time [89,205], or may be time-invariant (a tunnel or force field around the nominal path) [17,181,197,[208][209][210][211].…”

Section: Action Sublayermentioning

confidence: 99%

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Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

192

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Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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Section: Machine Learning Phase (Mlp)mentioning

confidence: 99%

Section: Action Sublayermentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

192

View full text Add to dashboard Cite

show abstract

“…Phase-based [10,[37][38][39][40][41]43,49, Impedance-based [74,75,102,136,[161][162][163][164][165]180] Metabolic-rate-based [70,96,132,181,182] Reflex-model-based [68,[183][184][185][186][187][188] Proportional-myoelectric-based [11,49,68,89,[189][190][191][192][193][194][195][196][197][198][199][200][201] Adaptive gain proportional-myoelectric-based [133][134][135][202]…”

Section: High-level Control Scheme Referencementioning

confidence: 99%

“…Electric actuators were the most popular actuators deployed in the reviewed PAEs. They were powered by on-board battery packs [37,41,56,57,61,[65][66][67][76][77][78][79][80][81][82][83][84]86,87,94,[100][101][102]112,113,115,[118][119][120]126,127,130,144,145,[156][157][158][159][160][172][173][174][175]177,184,187,189,234,235], DC off-board power supply units [10,…”

Section: Low-level Control and Machine-machine Sensorsmentioning

confidence: 99%

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Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

kian

Widanapathirana

Joseph

et al. 2022

Sensors

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Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

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Biomechanical Assessment of Adapting Trajectory and Human-Robot Interaction Stiffness in Impedance-Controlled Ankle Orthosis

Lopes

Figueiredo

Pinheiro

et al. 2021

J Intell Robot Syst

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Gait disabilities empowered intensive research on the field of human-robot interaction to promote effective gait rehabilitation. Assist-as-needed strategies are becoming prominent, appealing to the users' participation in their rehabilitation therapy. This study proposes and assesses the biomechanical effects of an adaptive impedance control strategy that innovatively allows adaptability in interaction-based stiffness and gait trajectory towards a fully assist-as-needed therapy. By modulating the interaction-based stiffness per gait phase, we hypothesize that the strategy appeals to a symbiotic human-orthotic cooperation, augmenting the user's muscular activity. The interaction stiffness was estimated by modelling the human-orthosis interaction torque vs angle curve with a linear regression model. The strategy also allows for real-time trajectory adaptations at different gait phases to fulfil the users' needs. The biomechanical assessment of the impedance-controlled ankle orthosis involved eight healthy volunteers walking at 1.0 and 1.6 km/h. The results revealed a stronger muscular activation regarding the non-assisted leg for the gastrocnemius lateralis (increment ratio ≥ 1.0 for both gait speeds) and for the tibialis anterior muscle (increment ratio ≥ 1.0 for 1.6 km/h). The strategy guided users successfully on a healthy gait pattern while allowing deviations (median error < 5.0°) given the users' intention weighted by interaction stiffness. Findings showed the relevance for adapting gait trajectory as users prefer higher trajectories as the speed increases. No significant temporal variations or neither knee angular compensations were observed (p value ≥0.11). Overall results support that this strategy may be applied for intensity-adapted gait training, allowing different human-robot compliant levels.

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Assist-as-needed Impedance Control Strategy for a Wearable Ankle Robotic Orthosis

Cited by 10 publications

References 16 publications

Review of control strategies for lower-limb exoskeletons to assist gait

Review of control strategies for lower-limb exoskeletons to assist gait

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

Biomechanical Assessment of Adapting Trajectory and Human-Robot Interaction Stiffness in Impedance-Controlled Ankle Orthosis

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