Development and testing of a passive Walking Assist Exoskeleton

Lovrenovic, Zlatko; Doumit, Marc

doi:10.1016/j.bbe.2019.01.002

Cited by 38 publications

(23 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, the arithmetic mean of the absolute value is calculated for several discrete points of the power curves of joint energy metabolism, and the results are shown in Figure 14. Based on the calculation method, the ultimate walking assistance efficiency of the KESM is as follows: the result in this article is 66.72%, which is theoretically much higher than that of other exoskeleton designs (van den Bogert, 2003;Banala et al, 2006;Rahman et al, 2007;van Dijk et al, 2011;Wiggin et al, 2011;van Dijk and Van der Kooij, 2014;Collins et al, 2015;Lee and Wang, 2015;Hung et al, 2017;Alamdari et al, 2019;Lovrenovic and Doumit, 2019;Yandell et al, 2019;Zhou et al, 2020), and the simulation result is still less than the theoretical value. The extreme value of the principle of mutual offset between joint positive and joint negative work in the whole gait range will be the focus for future improvement.…”

Section: The Functional Verification Of the Kesmmentioning

confidence: 81%

“…The reason is the power curves of the hip joint are composed of a series of discrete points. Based on the calculation method of the ultimate walking assistance efficiency of the HESM shown in the following formula, the result in this article is 52.36%, which is theoretically much higher than that of other exoskeleton designs (van den Bogert, 2003;Banala et al, 2006;Rahman et al, 2007;van Dijk et al, 2011;Wiggin et al, 2011;van Dijk and Van der Kooij, 2014;Collins et al, 2015;Lee and Wang, 2015;Hung et al, 2017;Alamdari et al, 2019;Lovrenovic and Doumit, 2019;Yandell et al, 2019;Zhou et al, 2020), and the existing simulation results are much smaller than it. The reason for these huge differences includes not only the two unexpected situations mentioned before but also a great difficulty in the transformation degree and identification cycle between the joint positive and negative works.…”

Section: The Functional Verification Of the Hesmmentioning

confidence: 81%

See 1 more Smart Citation

Hip–Knee Coupling Exoskeleton With Offset Theory for Walking Assistance

Sun

Chen³

2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

To alleviate the influence of aging on the elderly, a hip–knee coupling exoskeleton with offset theory is designed in this article to improve people’s athletic ability. With the novel design, the unique application strategy of the offset principle for hip and knee joints is developed, and a hip–knee coupling mechanism is proposed to solve the discrete power assistance problem for the knee joint. To acquire the success of the design, the mathematical model of the coupling mechanism is established to optimize the load environment of the exoskeleton system, and furthermore, an algorithm adapted to human movement is proposed to determine the monotonicity of the cam profiles. For the selection of the elastic parameters in the coupling mechanism, the sensitivity condition is proposed, and the human–machine interaction model of the KESM is further established. A man–machine coupling model was used to verify the scientificity of the exoskeleton design, and the comparison between the joint powers with or without exoskeleton indicated that the exoskeleton theoretically saved at least 20% of the human body’s energy.

show abstract

Section: The Functional Verification Of the Kesmmentioning

confidence: 81%

Section: The Functional Verification Of the Hesmmentioning

confidence: 81%

Hip–Knee Coupling Exoskeleton With Offset Theory for Walking Assistance

Sun

Chen³

2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Target devices for the controllers in this review do not need to provide an improvement of the user's health. Although the devices are typically anthropomorphic, exceptions also exist (such as [10][11][12][13]). The so-called "soft exoskeletons" (exosuits) are included too, even if these are not really stiff "skeletons", but closer to "tendons and muscles".…”

Section: Scope and Methodological Stepsmentioning

confidence: 99%

“…In [ 164 ], the spring is linked to a ratchet and a clutch to disconnect the spring from the ankle during the swing phase. A similar approach was described in [ 11 ]. In [ 171 ], the clutch is active, but the rest (a spring) is passive.…”

Section: Assistive Strategiesmentioning

confidence: 99%

“…The same idea can also be implemented using wearable lower-limb devices. Instead of providing assistance at the joints to move the legs, the idea is to relieve the user from a part of his/her weight, by having the exoskeleton pushing the trunk upward [11,12]. This mainly works for the knee joint, because in stance, the partial gravity compensation consists simply in applying an extension torque.…”

Section: Action Sublayermentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Automated knee osteoarthritis severity classification using three‐stage preprocessing method andVGG16architecture

Saini

Khosla

Chand

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

As a nutshell, an early diagnosis of Knee Osteoarthritis (KOA) enhances the likelihood of an individual receiving treatment at its onset and prevents joint replacement surgery. Computer-Aided-Diagnosis (CAD) from the radiograph images have gained a wide-spread attention in automated grading of KOA severity. But radiograph images have certain limitations, such as presence of too much noise and uneven contrast distribution across the image that impacts the accuracy and reliability of CAD systems. Therefore, a novel threestage pre-processing method has been proposed using a combination of different techniques applied at sequential stages such as noise-reduction using gaussian-filter, normalization using pixel-centering method, and balanced contrast enhancement technique. A transfer-learning based VGG16 architecture has been used for severity classification. Our classification framework outperforms the existing state-of-the-art methods achieving an excellent accuracy of 89.95\%. Therefore, our system can be used by the radiologists and physicians as a decision-support tool in their daily diagnosis procedure.

show abstract

Development and testing of a passive Walking Assist Exoskeleton

Cited by 38 publications

References 14 publications

Hip–Knee Coupling Exoskeleton With Offset Theory for Walking Assistance

Hip–Knee Coupling Exoskeleton With Offset Theory for Walking Assistance

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

Automated knee osteoarthritis severity classification using three‐stage preprocessing method andVGG16architecture

Contact Info

Product

Resources

About