Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator

Chinimilli, Prudhvi Tej; Subramanian, Susheelkumar C.; Redkar, Sangram; Sugar, Thomas G.

doi:10.1109/wearracon.2019.8719628

Cited by 10 publications

(9 citation statements)

References 23 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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“…It is obvious that the convergence speed of the fundamental frequency ω( t ) of AO becomes slower and ω( t ) even becomes negative at about 10.7 s when the initial frequency and phase of AO are respectively changed as ω(0) = π and ϕ 0 ( t ) = π/2. Due to the length limitation of this paper, we only discussed the influence of ω(0) and ϕ 0 ( t ), but the results in Chinimilli et al ( 2019 ) have shown that the initial amplitude of each oscillator [α i (0)] will also has significant effect on the frequency estimation and trajectory prediction of AO. Above simulation results show that the initial values of AO have a significant effect on the performance of AO.…”

Section: Methodsmentioning

confidence: 99%

“…But the nominal pattern needs to be recorded by walking experiments in advance, and PSAO needs walking pattern classification algorithm to choose right nominal pattern function. To improve the convergence speed of AO when the walking amplitude suddenly changed, an amplitude omega adaptive oscillator ( AωAO ) was proposed in Chinimilli et al ( 2019 ). AωAO algorithm firstly calculates the amplitude and frequency of human motion, and then uses support vector machine (SVM) and discrete hidden Markov model (DHMM) to determine whether the movement pattern of the human body has changed.…”

Section: Related Workmentioning

confidence: 99%

“…The basic ideal of AO is establishing a pool of adaptive oscillators to learn the fundamental frequency of input signal and then constructing a supervised learning problem to learn the profile of input signal (Ronsse et al, 2011 ). But there are two drawbacks existed in AO: (1) the initial frequency of each oscillator will determine whether the AO can convergent to the fundamental frequency of input signal (Seo et al, 2018 ); (2) if the amplitude of input signal changes, AO will take a significant amount of time to converge to the new amplitude (Chinimilli et al, 2019 ). Therefore, to enhance the convergence of AO, a particularly-shaped adaptive oscillator (PSAO) (Seo et al, 2018 ) was proposed to make AO less dependent on the initial parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to enhance the convergence of AO, a particularly-shaped adaptive oscillator (PSAO) (Seo et al, 2018 ) was proposed to make AO less dependent on the initial parameters. And to make AO converge rapidly to the varying amplitude of input signal, an amplitude omega adaptive oscillator (AωAO) was proposed in Chinimilli et al ( 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

et al. 2021

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This paper introduces a novel exoskeleton active walking assistance control framework based on frequency adaptive dynamics movement primitives (FADMPs). The FADMPs proposed in this paper is an online learning and prediction algorithm which is able to online estimate the fundamental frequency of human joint trajectory, learn the shape of joint trajectory and predict the future joint trajectory during walking. The proposed active walking assistance control framework based on FADMPs is a model-based controller which relies on the human joint torque estimation. The assistance torque provided by exoskeleton is estimated by human lower limb inverse dynamics model which is sensitive to the noise in the joint motion trajectory. To estimate a smooth joint torque profile, the joint motion trajectory must be filtered first by a lowpass filter. However, lowpass filter will introduce an inevitable phase delay in the filtered trajectory. Both simulations and experiments in this paper show that the phase delay has a significant effect on the performance of exoskeleton active assistance. The active assistant control framework based on FADMPs aims at improving the performance of active assistance control by compensating the phase delay. Both simulations and experiments on active walking assistance control show that the performance of active assistance control can be further improved when the phase delay in the filtered trajectory is compensated by FADMPs.

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Artificial‐Intelligence‐Powered Lower Limb Assistive Devices: Future of Home Care Technologies

Mehr

Akbari

Faridi

et al. 2023

Advanced Intelligent Systems

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Healthcare systems are burdened by mobility impairments resulting from aging and neurological conditions. One of the recent advances in robotics is lower limb assistive/rehabilitative devices that can make independent living possible. Nonetheless, some limitations need to be addressed before robotics can be used in home‐based applications. This paper describes the current state of the art in intelligent motion planning and control of lower limb assistive devices, which have addressed some of these challenges. Adaptable central pattern generators and the divergent component of motion are introduced as methods for personalized motion planning based on physical human–robot interaction (pHRI). Uncertainty analysis for neural networks is introduced to increase safety in motion planning based on pHRI. For the case that a user cannot apply physical interaction, a reinforcement‐learning‐based approach is introduced to switch between different modes of walking based on the user's input via a push button embedded in a walker. Moreover, a smart walker is introduced as a device that can be synchronized with the lower limb exoskeleton to assist users with their daily activities. Also, a roadmap for future steps that can make lower limb assistive/rehabilitative devices a good fit for home use is introduced.

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Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator

Cited by 10 publications

References 23 publications

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

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

Exoskeleton Active Walking Assistance Control Framework Based on Frequency Adaptive Dynamics Movement Primitives

Artificial‐Intelligence‐Powered Lower Limb Assistive Devices: Future of Home Care Technologies

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