A novel human effort estimation method for knee assistive exoskeletons

Saccares, Lorenzo; Brygo, Anais; Sarakoglou, Ioannis; Tsagarakis, Nikos G.

doi:10.1109/icorr.2017.8009423

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…In [249] the authors have used inverse dynamics (4 sets of equations depending on the contact point(s) with the ground) to estimate the joints torque. Another method that does not rely on an accurate model, is using ground reaction forces, shank angle, and shank length [250,251]. It has also been proposed to use a spring-loaded inverted pendulum (SLIP) model to estimate the required biological hip and knee torques [252].…”

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

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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: 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

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“…With information from the environment and the user, captured by sensors, a program (control mode) converts the information to commands for the actuators. There are some studies that implemented different type of control strategies, based on segment angles, interaction forces, muscle forces and combinations of these variables (Fleischer and Hommel, 2008;Giovacchini et al, 2015;Grazi et al, 2018;Hara and Sankai, 2010;Hayashi et al, 2005;Saccares et al, 2017). However, none of these studies used L5/S1 compression forces as outcome measure and some only quantified the effects via questionnaires.…”

Section: Introductionmentioning

confidence: 99%

The effect of control strategies for an active back-support exoskeleton on spine loading and kinematics during lifting

Koopman

Toxiri

Power

et al. 2019

Journal of Biomechanics

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With mechanical loading as the main risk factor for LBP, exoskeletons (EXO) are designed to reduce the load on the back by taking over part of the moment normally generated by back muscles. The present study investigated the effect of an active exoskeleton, controlled using three different control modes (INCLINATION, EMG & HYBRID), on spinal compression forces during lifting with various techniques. Ten healthy male subjects lifted a 15kg box, with three lifting techniques (free, squat & stoop), each of which was performed four times, once without EXO and once each with the three different control modes. Using inverse dynamics, we calculated L5/S1 joint moments. Subsequently, we estimated spine forces using an EMG-assisted trunk model. Peak compression forces substantially decreased by 17.8% when wearing the EXO compared to NO EXO. However, this reduction was partly, by about one third, attributable to a reduction of 25% in peak lifting speed when wearing the EXO. While subtle differences in back load patterns were seen between the three control modes, no differences in peak compression forces were found. In part, this may be related to limitations in the torque generating capacity of the EXO. Therefore, with the current limitations of the motors it was impossible to determine which of the control modes was best. Despite these limitations, the EXO still reduced both peak and cumulative compression forces by about 18%. The effect of control strategies for an active back-support exoskeleton on spine loading and kinematics during lifting Manuscript Click here to view linked References

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“…A similar approach is taken on a different and somewhat unique device, the Honda Walking Assist Device Ikeuchi et al (2009) . With focus on lifting objects rather than walking, GRF is used to generate commands for a wearable knee exoskeleton Saccares et al (2017) and for a ground-based robotic arm Kim et al (2018) . One of the issues typically associated to these measurements is the obtrusiveness of the sensors that measure the GRF.…”

Section: Introductionmentioning

confidence: 99%

Rationale, Implementation and Evaluation of Assistive Strategies for an Active Back-Support Exoskeleton

et al. 2018

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Active exoskeletons are potentially more effective and versatile than passive ones, but designing them poses a number of additional challenges. An important open challenge in the field is associated to the assistive strategy, by which the actuation forces are modulated to the user's needs during the physical activity. This paper addresses this challenge on an active exoskeleton prototype aimed at reducing compressive low-back loads, associated to risk of musculoskeletal injury during manual material handling (i.e., repeatedly lifting objects). An analysis of the biomechanics of the physical task reveals two key factors that determine low-back loads. For each factor, a suitable control strategy for the exoskeleton is implemented. The first strategy is based on user posture and modulates the assistance to support the wearer's own upper body. The second one adapts to the mass of the lifted object and is a practical implementation of electromyographic control. A third strategy is devised as a generalized combination of the first two. With these strategies, the proposed exoskeleton can quickly adjust to different task conditions (which makes it versatile compared to using multiple, task-specific, devices) as well as to individual preference (which promotes user acceptance). Additionally, the presented implementation is potentially applicable to more powerful exoskeletons, capable of generating larger forces. The different strategies are implemented on the exoskeleton and tested on 11 participants in an experiment reproducing the lifting task. The resulting data highlights that the strategies modulate the assistance as intended by design, i.e., they effectively adjust the commanded assistive torque during operation based on user posture and external mass. The experiment also provides evidence of significant reduction in muscular activity at the lumbar spine (around 30%) associated to using the exoskeleton. The reduction is well in line with previous literature and may be associated to lower risk of injury.

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A novel human effort estimation method for knee assistive exoskeletons

Cited by 16 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

The effect of control strategies for an active back-support exoskeleton on spine loading and kinematics during lifting

Rationale, Implementation and Evaluation of Assistive Strategies for an Active Back-Support Exoskeleton

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