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      A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

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          Abstract

          Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

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          Most cited references96

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          The energy expenditure of normal and pathologic gait

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            Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.

            In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.
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              A strategy for identifying locomotion modes using surface electromyography.

              This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% +/- 5.0%, 6.0% +/- 4.7%, 7.5% +/- 5.1%, and 5.2% +/- 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                17 July 2020
                July 2020
                : 20
                : 14
                : 3972
                Affiliations
                [1 ]Robotics & Multibody Mechanics Research Group (R & MM), Vrije Universiteit Brussel and Flanders Make, 1050 Brussels, Belgium; Dianbiao.Dong@ 123456vub.be (D.D.); Hoang.Long.Cao@ 123456vub.be (H.-L.C.); Tom.Verstraten@ 123456vub.be (T.V.); dlefeber@ 123456vub.ac.be (D.L.); bram.vanderborght@ 123456vub.ac.be (B.V.); jgeeroms@ 123456vub.ac.be (J.G.)
                [2 ]Faculty of Electronics Engineering Technology, Hanoi University of Industry, Hanoi 100000, Vietnam
                [3 ]School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
                [4 ]College of Engineering Technology, Can Tho University, Can Tho 90000, Vietnam
                Author notes
                [* ]Correspondence: vu.huong@ 123456vub.be
                Author information
                https://orcid.org/0000-0001-8512-2784
                https://orcid.org/0000-0003-2851-5527
                https://orcid.org/0000-0001-7398-5398
                https://orcid.org/0000-0003-4881-9341
                Article
                sensors-20-03972
                10.3390/s20143972
                7411778
                32708924
                de44162b-6a1f-4331-af5c-2059ed3d1f06
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 11 June 2020
                : 15 July 2020
                Categories
                Review

                Biomedical engineering
                gait phase detection,event detection,lower limb prosthesis,gait phase classification,imu sensor,smart insole,wearable sensors,assistive devices

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