Adaptive control of an actuated ankle foot orthosis for paretic patients

Arnez-Paniagua, V.; Rifaï, Hala; Amirat, Yacine; Ghédira, Mouna; Graciès, Jean Michel; Mohammed, Samer

doi:10.1016/j.conengprac.2019.06.003

Cited by 22 publications

(25 citation statements)

References 37 publications

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“…Most commonly, the ground contact state of the feet, or equivalently the ground reaction force (GRF), is used either for the entire foot to only distinguish between stance/swing [164][165][166][167][168][169] or considering local components (e.g. at the heel and under the toes) to further differentiate between stance subphases [48,67,86,117,148,162,163,[170][171][172][173][174]. The gait state can also be determined by computing the center of pressure (CoP) position of the stance leg with four load cells per foot, then applying a threshold to identify four states [175,176].…”

Section: Machine Learning Phase (Mlp)mentioning

confidence: 99%

“…For some state machines, the ground contact status has been used as the only factor for transitioning the states [67, 117, 162-166, 171, 172], but it has also been used in combination with joint angle(s) [116,167,170], joint angular velocities [173], segment angles and angular velocities [48,168], or the relative position of the feet [177]. In [148], the linear acceleration of the shank is also used in addition to ground contact data to improve the accuracy of heelstrike detection. The amount of time elapsed since the onset of swing has also been used in addition to ground reaction force (GRF) data to further detect subphases of swing [169].…”

Section: Machine Learning Phase (Mlp)mentioning

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: Machine Learning Phase (Mlp)mentioning

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

“…Gurney et al, 2008 [209] integrated a set of FSR sensors into shoe insoles to measure the plantar pressure from the main pressure distribution regions of the foot sole: forefoot, toe, and heel, to identify gait events based on the pressure profile of these regions during different states of a normal gait cycle. Gyroscope [172] Linear displacement sensor [97][98][99] IMU [64,127,152,211] Goniometer [180,183] Attitude sensor [116] Custom strain sensor combined with IMU [174,175] Strain sensor [87] Knee joint angle IMU [206,207] Strain sensor [87] Absolute shank angle IMU [40,85,87,117,118,159,160,177] Orientation of shank, thigh, and trunk IMU [42] Inclinometer [82] Angular velocity Gyroscope [123][124][125][126]128,129,150,151,157,172] IMU [114,119,120] Translational acceleration of wearer IMU [40,[117][118]…”

Section: Phase-based Control and Physical Sensorsmentioning

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%

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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“…Therefore, the analysis of the human gait represents an important instrument for the early and timely identification of pathologies, and a component of follow-up rehabilitation programs [5] [6] [7] [8] [9] . In addition, gait analysis is also important to the design of equipment, devices, rehabilitation systems, prostheses, orthoses, and humanoid robots [10] [11] [12] [13] [14] .…”

Section: Introductionmentioning

confidence: 99%

A Practical Review of the Biomechanical Parameters Commonly Used in the Assessment of Human Gait

González

Medellín-Castillo

Cervantes-Sánchez

et al.

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The analysis of human gait is a potential diagnostic instrument for the early and timely identification of pathologies and disorders. It can also supply valuable data for the development of biomedical devices such as prostheses, orthoses, and rehabilitation systems. Although various research papers in the literature have used human gait analyses, few studies have focused on the biomechanical parameters used. This paper presents an extensive review and analysis of the main biomechanical parameters commonly used in the human gait study. The aim is to provide a practical guide to support and understand of the choices and selection of the most appropriate biomechanical parameters for gait analysis. A comprehensive search in scientific databases was conducted to identify, review and analyze the academic work related to human gait analysis. From this search, the main biomechanical parameters used in healthy and pathological gait studies were identified and analyzed. The results have revealed that the spatiotemporal and angular gait parameters are the most used in the assessment of healthy and pathological human gait.

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Adaptive control of an actuated ankle foot orthosis for paretic patients

Cited by 22 publications

References 37 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

A Practical Review of the Biomechanical Parameters Commonly Used in the Assessment of Human Gait

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