Robotic Knee Exoskeleton Prototype to Assist Patients in Gait Rehabilitation

Mora-Tola, Esteban; Loja-Duchi, Juan; Ordonez-Torres, Andres; Vazquez-Rodas, Andres; Astudillo‐Salinas, Fabián; Minchala, Luis I.

doi:10.1109/tla.2020.9381791

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Zhuang et al [42] Proved to be better than interaction-torque based method Zhang et al [43] Back Propagation (BP) neural network was used Xie et al [44] General regression neural network optimized by golden section algorithm was used Rabe et al [45] Anterior sonomyography sensor fusion with surface EMG Fougner et al [46] 3.8~18% average classification error due to muscle fatigue Mora-Tola et al [52] Artificial Neural Network (ANN) algorithms were used A robot dynamics model including the active force of human was established, and contact force was used to analyze intention Pinheiro et al [50] The interaction torque's direction and magnitude were both used Xu et al [51] A compliance control algorithm based on intent was proposed…”

Section: Intent Recognition Methods Study Characteristicmentioning

confidence: 99%

“…Xu et al [51] proposed a compliance control algorithm for walking-aid robots based on multi-sensor fusion, which allows the robot to obey human movement by recognizing user intentions. Esteban et al [52] also carried out related research, using EMG signals and Artificial Neural Network (ANN) algorithms to recognize human walking intention and proposed a robotic knee exoskeleton for assistance and rehabilitation. Wu et al [53] put forward a coordinated control strategy based on humanmachine interaction and the principle of minimum interference.…”

Section: Movement Intention Recognitionmentioning

confidence: 99%

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Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

et al. 2022

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Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

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Section: Intent Recognition Methods Study Characteristicmentioning

confidence: 99%

Section: Movement Intention Recognitionmentioning

confidence: 99%

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

et al. 2022

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“…This paper explores the development and evaluation of an IoT-based knee exoskeleton rehabilitation monitoring device [7], [8], [15]- [17]. The knee complex structure and susceptibility to injuries make it an essential focus area [1], [18].…”

Section: Development Of An Iot-based Knee Exoskeleton Device For Reha...mentioning

confidence: 99%

DEVELOPMENT OF AN IoT-BASED KNEE EXOSKELETON DEVICE FOR REHABILITATION THERAPY MONITORING

SAPIEE,

Thomas,

Marhaban

et al. 2023

AJMedTech

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The COVID-19 pandemic has presented a multitude of challenges pertaining to medical treatments and patient care, creating a pressing need for innovative solutions. Constrained outdoor activities have hindered patients' ability to access essential healthcare, while healthcare professionals encounter difficulties in remotely monitoring and administering treatments. This study aims to address these pressing issues by proposing an Internet of Things (IoT)-based exoskeleton system designed for the monitoring of knee rehabilitation therapy. Through the utilization of the IoT platform ThingSpeak, this developed system enables patients to remotely monitor the status of their knee recovery from the comfort of their homes. The collected data is securely stored and made accessible to healthcare professionals, facilitating remote analysis and the formulation of treatment recommendations. This pioneering solution amalgamates advancements in exoskeleton technology with seamless IoT integration, establishing a dependable and easily accessible framework for the monitoring of knee rehabilitation. By empowering patients to actively participate in their own recovery process and enabling remote monitoring by healthcare providers, this system effectively surmounts the limitations imposed by the pandemic and financial constraints. The proposed IoT-based exoskeleton system possesses the potential to revolutionize knee rehabilitation therapy, thereby enhancing patient engagement, optimizing treatment outcomes, and circumventing traditional healthcare barriers.

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“…sEMG signals are acquired by placing sEMG sensors on the muscle surface and recording electrical signals [3]. sEMG has been widely used in the field of action recognition and has achieved compelling results [4][5][6][7][8]. Since sEMG involves nonstationary bioelectric signals, wearers will experience muscle fatigue, unstable sEMG signals and other interference during the signal acquisition process.…”

Section: Introductionmentioning

confidence: 99%

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

Wang

Cao

Liu

2021

Sensors

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To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

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Robotic Knee Exoskeleton Prototype to Assist Patients in Gait Rehabilitation

Cited by 7 publications

References 22 publications

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

DEVELOPMENT OF AN IoT-BASED KNEE EXOSKELETON DEVICE FOR REHABILITATION THERAPY MONITORING

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

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