Automatic virtual impedance adaptation of a knee exoskeleton for personalized walking assistance

Chinimilli, Prudhvi Tej; Qiao, Zhi; Sorkhabadi, Seyed Mostafa Rezayat; Jhawar, Vaibhav; Fong, Iat Hou; Zhang, Wenlong

doi:10.1016/j.robot.2019.01.013

Cited by 27 publications

(16 citation statements)

References 27 publications

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“…The main advantage of this method is that it does not require any cognitive load or direct input from the user, making the interaction more intuitive and natural. For this method, generally joint sensors and IMU data (often from the upper body in persons with paraplegia) are processed by a machine learning or fuzzy logic algorithm to recognize the situation [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64], although simpler threshold-based methods have also been proposed [65]. Sometimes, other types of signals such as the ground reaction forces or electromyography (EMG) are also used to infer the movement or the intention of the user [66][67][68][69][70][71].…”

Section: Movements Recognition (Mov)mentioning

confidence: 99%

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

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

157

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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: Movements Recognition (Mov)mentioning

confidence: 99%

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

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

157

View full text Add to dashboard Cite

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“…Other related studies focused on the personalization of control systems. When considering gait rehabilitation, taking into account individual specificities is critical in order to provide relevant assistance [34], [35], [36]. The work of Chinimilli et al (2019) showed a reduced muscular activity (vastus medialis) with a personalized walking assistance compared to baseline conditions.…”

Section: Discussionmentioning

confidence: 99%

Personalizing the control law of an upper-limb exoskeleton using EMG signal

Treussart

Caron

Geffard

et al. 2021

Preprint

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Implementing an intuitive control law for an upperlimb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The goal of this study is to adapt an EMG-based control system to a user based on individual characteristics. To this aim, a method has been designed to tune the parameters of control using objective criteria, improving user's feedback. The user's response time is used as an objective value to adapt the gain of the controller. The proposed approach was tested on 10 participants during a lifting task. Two different conditions have been used to control the exoskeleton: with a generic gain and with a personalized gain. EMG signals was captured on five muscles to evaluate the efficiency of the conditions and the user's adaptation. Results showed a statistically significant reduction of mean muscle activity of the deltoid between the beginning and the end of each situation (28.6%, standard deviation (SD) 13.5% to 17.2%, SD 7.3%, of Relative Maximal Contraction for the generic gain and from 24.9%, SD 8.5%, to 18%, SD 6.8%, of Relative Maximal Contraction for the personalized gain). When focusing on the first assisted movements, the personalized gain induced a mean activity of the deltoid significantly lower (29%, SD 8.0%, of Relative Maximal Contraction and 37.4%, SD 9.5%, of Relative Maximal Contraction, respectively). Subjective evaluation showed that the system with a personalized gain was perceived as more intuitive, and required less concentration when compared to the system with a generic gain.

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“…A Fuzzy logic algorithm is implemented by Chinimilli et al [81] to detect four gait phases. Their goal was to create an adaptive virtual impedance control for a knee exoskeleton.…”

Section: ) Fuzzy Logicmentioning

confidence: 99%

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

2021

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This project has received funding from the Interreg 2 Seas programme 2014-2020 co-funded by the European Regional Development Fund under subsidy contract No 2S05-038 (M.O.T.I.O.N project). Rania Kolaghassi acknowledges the support of studentship through M.O.T.I.O.N, and Mohamad Kenan Al-Hares acknowledges the support of M.O.T.I.O.N. Data used in this work is stored in Kent Academic Repository (https://kar.kent.ac.uk/).

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Automatic virtual impedance adaptation of a knee exoskeleton for personalized walking assistance

Cited by 27 publications

References 27 publications

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

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

Personalizing the control law of an upper-limb exoskeleton using EMG signal

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

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