Intelligent Assistive Knee Exoskeleton

Chandrapal, Mervin; Chen, Xiaoqi; Wang, Wenhui

doi:10.1002/9781118614549.ch7

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…According to former studies by Zhang et al [6] on the attachment position of all knee devices, approximately 38% of all devices is the lateral support layout (outside) type . The two-side support devices are used approximately 10% of all [3,[30][31][32][33][34]. The improved-lateral support type is used by 40% , while the remaining devices are anterior/posterior support type [6].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al also suggested that many studies of knee assistive devices use different actuation types [6]; around 73% of the research uses active actuation [3,[7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]30,[35][36][37][38][39][41][42][43][44][45][46][47][48][58][59][60][61][62][63][64][65][66][67], 10% uses quasi-active actuation [24][25][26][49][50][51][52]55], and 17% uses passive actuation...…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Knee Assistive Moment in a Gait Cycle Using Knee Angle and Knee Angular Velocity through Machine Learning and Artificial Stiffness Control Strategy (MLASCS)

Pornpipatsakul

Ajavakom

2023

Robotics

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Nowadays, many people around the world cannot walk perfectly because of their knee problems. A knee-assistive device is one option to support walking for those with low or not enough knee muscle forces. Many research studies have created knee devices with control systems implementing different techniques and sensors. This study proposes an alternative version of the knee device control system without using too many actuators and sensors. It applies the machine learning and artificial stiffness control strategy (MLASCS) that uses one actuator combined with an encoder for estimating the amount of assistive support in a walking gait from the recorded gait data. The study recorded several gait data and analyzed knee moments, and then trained a k-nearest neighbor model using the knee angle and the angular velocity to classify a state in a gait cycle. This control strategy also implements instantaneous artificial stiffness (IAS), a control system that requires only knee angle in each state to determine the amount of supporting moment. After validating the model via simulation, the accuracy of the machine learning model is around 99.9% with the speed of 165 observers/s, and the walking effort is reduced by up to 60% in a single gait cycle.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Knee Assistive Moment in a Gait Cycle Using Knee Angle and Knee Angular Velocity through Machine Learning and Artificial Stiffness Control Strategy (MLASCS)

Pornpipatsakul

Ajavakom

2023

Robotics

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A low-cost transradial prosthesis controlled by the intention of muscular contraction

Prakash

Sharma

2021

Phys Eng Sci Med

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Design of a Robotic Exo-Suit for Knee Lateral Support With an Interaction Control Based on Relative Motion Angle

Tanyıldızı

Taşar

Yakut

2022

J. Mech. Med. Biol.

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In this study, we focused on the design and control of a single DoF laterally supported knee exoskeleton robot designed to improve the load-carrying and strength capacity of healthy individuals, especially soldiers and workers. First, a nonlinear second-order differential model of the robotic knee orthosis was produced. Then, a single degree of freedom exoskeleton robot was designed and manufactured. An interactive motion control method based on the relative angle measurement principle between the knee joint of the user and the exo-suit knee joint was proposed. The user’s knee joint motion is detected via an IMU sensor, and a controller was provided to allow the exo-suit to track the human knee joint in synchrony. In this study, a conventional PID, SMC, and moving surface SMC controllers were designed, and the controllers’ performance were tested in real-time experiments. The maximum tracking errors in the PID, SMC, and MSMC controllers were 2.631∘, 1.578∘, and 1.289∘, respectively, and the average tracking time errors were 0.10, 0.08, and 0.06 s, respectively. In addition, the designed and produced knee orthosis weighs 2.5 kg, making it one of the lightest and most compact designs for use by healthy individuals. The knee orthosis was made of steel, and thus it is very durable for use in all kinds of terrain conditions (military, industrial, etc.). Experimental findings about the proposed design and control method are analyzed in the results and discussion section.

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Intelligent Assistive Knee Exoskeleton

Cited by 3 publications

References 47 publications

Estimation of Knee Assistive Moment in a Gait Cycle Using Knee Angle and Knee Angular Velocity through Machine Learning and Artificial Stiffness Control Strategy (MLASCS)

Estimation of Knee Assistive Moment in a Gait Cycle Using Knee Angle and Knee Angular Velocity through Machine Learning and Artificial Stiffness Control Strategy (MLASCS)

A low-cost transradial prosthesis controlled by the intention of muscular contraction

Design of a Robotic Exo-Suit for Knee Lateral Support With an Interaction Control Based on Relative Motion Angle

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