Individualized Gait Pattern Generation for Sharing Lower Limb Exoskeleton Robot

Wang, Xinyu; Liu, Du-Xin; Liu, Ming; Chen, Chunjie; Guo, Heng

doi:10.1109/tase.2018.2841358

Cited by 87 publications

(41 citation statements)

References 39 publications

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“…After training, the network returned one kind of hand motion through pattern recognition from real-time signals. Finally, the prosthetic hand performed the motion according to the identified motion [35,36,37,38].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand

Jing

et al. 2019

Sensors

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Although arch motions of the palm substantially contribute to frequent hand grasping, they are usually neglected in the development of prosthetic hands which focuses on digit movements. We designed the arch function for its implementation on an adaptive multi-finger prosthetic hand. The digits from the developed hand can perform adaptive grasping, and two carpometacarpal joints enable the palm of the prosthetic hand to form an arch with the thumb. Moreover, the arch posture can be passively released, mimicking the human hand switching between sphere and medium wrap grasps according to the situation. Other requirements such as weight, cost, and size limitations for hand prostheses were also considered. As a result, we only used three actuators fully embedded in the palm through a novel tendon-driven transmission. Although the prosthetic hand is almost the same size of an adult hand, it weighs only 146 g and can perform 70% of the 10 most frequent grasps.

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Section: Experiments and Resultsmentioning

confidence: 99%

Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand

Jing

et al. 2019

Sensors

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“…Furthermore, the applications of the exoskeleton robot in the medical institutes would be investigated. We expect that the exoskeleton robot will be widely used as shared equipment [44] in the architectural industry, logistics industry and many more domains. Therefore, the technologies of adaptively learning the unique gait feature would be widely applied.…”

Section: Discussionmentioning

confidence: 99%

Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Wang

et al. 2019

Sensors

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The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.

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“…Wu X. et al, With control strategies developed for people with immobility (for example, patients with complete spinal cord injury), aimed to enable the person's lower extremity active joints to walk on a predefined orbit. As a result of the study, it was concluded that the appropriate walking pattern was estimated for each person (Wu, Liu, Liu, Chen, & Guo, 2018). In a study by Fernandes et al, A strong knee orthosis (PCO) was used to provide auxiliary commands based on the user's intention to move by electromyography (EMG) signalsAs a result, the assistive strategy developed has shown that the user can effectively follow the intention of movement and that patients with muscle strength now have the potential for gait rehabilitation (Fernandes et al, 2019).…”

Section: Introductionmentioning

confidence: 96%

Investigation of EMG Signals in Lower Extremity Muscle Groups During Robotic Gait Exercises

Uzunhisarcikli¹,

Çetinkaya²,

Fidan³

et al. 2019

European Journal of Science and Technology

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Many people have been exposed to lower extremity function losses due to neurological, pathological or traffic accidents. In the physical therapy and rehabilitation of these patients, treatment programs based on robotic systems have started to be preferred instead of conventional methods. In robotic gait rehabilitation, mobilized lower extremity exoskeletons such as Rewalk or un-mobilized lower extremity exoskeletons such as RoboGait are used. It is important to evaluate the rehabilitation process in patients with lower extremity problems. Measurement of surface electromyogram (EMG) signals during the treatment process give information about the functional activities of the muscles. Obtained information plays an important role in determining the intention of patient motion in musculoskeletal design and musculoskeletal activities of the musculoskeletal. Changes in muscle activation timing and amplitude during the use of lower extremity exoskeleton can be determined by analysis of EMG. In this study, muscles involved in walking movement during robotic rehabilitation were examined. The examined iliopsoas, gluteus maximus, gluteus medius muscles provide flexion, extension and abduction movements of the hip, while the medial gastrocnemius and tibialis anterior muscles perform flexion and dorsiflexion movements of the foot. During the gait, the knee joint patency is controlled by the Vastus Medialis and Biceps Femoris muscles. In this study, while 6 patients with lower limb dysfunction were walking on the RoboGait device, the muscle activation potentials obtained from 7 different muscle groups were transferred to the computer simultaneously and wirelessly and displayed in the Matlab environment. The EMG signals measured with the MicroCor Lab device are shaped according to the activation of the muscles during walking. The electrode placement plan is critical for the analysis of EMG signals, and an appropriate electrode placement plan was obtained as a result of the study. Examined measured signals by following with the electrode placement plan, the maximum gluteus and iliopsoas muscles responsible for the extension and flexion movements of the hips are more effective during walking. Gletous maximum muscle was found to be the most effective muscle in walking while the iliopsoas muscle group was involved in the first movement of the leg. As a result of this study, these findings will help to follow the development of the treatment process and to develop EMG controlled mobilized lower extremity exoskeletons.

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Individualized Gait Pattern Generation for Sharing Lower Limb Exoskeleton Robot

Cited by 87 publications

References 39 publications

Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand

Design and Implementation of Arch Function for Adaptive Multi-Finger Prosthetic Hand

Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Investigation of EMG Signals in Lower Extremity Muscle Groups During Robotic Gait Exercises

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