State Classification and Motion Description for the Lower Extremity Exoskeleton SJTU-EX

Miao, Yunjie; Gao, Feng; Pan, Dalei

doi:10.1016/s1672-6529(14)60034-2

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…The allowable pressure range for such sensors is typically from 100 g to 10 kg of gravity. (21,22) Table 2 shows the characteristics of the FSR402 pressure sensor. To collect more plantar pressure information during the walking phase, nine pressure sensors are affixed to each foot for a total of 18 plantar pressure sensors.…”

Section: Plantar Pressure Sensormentioning

confidence: 99%

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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Gait phase recognition is an effective method of analyzing human motion and behavior that can be very meaningful in people's daily life, especially when struggling with assisted rehabilitation. In this paper, a new algorithm that can recognize a human gait phase more accurately is proposed. The new gait phase recognition algorithm is based on a deep memory convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantar pressure sensor array and acceleration sensor array gait data, and then extracted the gait features using the DM-CNN. The measured data of the continuous gait cycle were divided into unit steps, and the data were analyzed and preprocessed. Then, a feature map of each sensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was combined into a fully connected network, and a memory function was introduced to simulate historical behavior. We then tested the algorithm on the phases of a gait cycle and compared the evaluation indicators of each phase. In the experiment, we compared single-mode and multimode recognition results, and compared those with the new hidden Markov model (N-HMM), K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental results show that when the multisensor data are fused, the average recognition accuracy can reach 97.1%, which is higher than those of the other algorithms and improves the recognition of a human gait phase. The accurate recognition of human gait can provide a better theoretical basis for the design of exoskeleton robot control strategies.

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Section: Plantar Pressure Sensormentioning

confidence: 99%

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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“…In the simulation process, we need to limit the rotation of the upper part of the human body to maintain balance. The friction coefficient is 0.6 in the human body and the ground [19,20]. The driving data of each joint is set according to the standard drive function.…”

Section: Adams Simulation Analysismentioning

confidence: 99%

Design and simulation analysis of an improved wearable power knee exoskeleton

Wang¹,

Yan²,

Xiao³

et al. 2019

J. vibroeng.

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The wearable lower limb power robotic exoskeleton is a device that can improve the human walking ability. In this paper, an improved exoskeleton device for knee joint is designed, including the improvement of mechanical structure and hydraulic cylinder. In order to verify the effectiveness of the improvement of the hydraulic cylinder, we have carried out the following studies. Firstly, in terms of mechanical structure, length adjusting device is added to meet the needs of different people. At the same time, a limit device is added to the knee joint to improve the safety performance and comfort. Secondly, the dynamics of the model is carried out by Lagrange, and the exoskeleton model is established for ADAMS motion simulation. The force of ADAMS simulation, the calculated by Lagrange equation and the force of the first edition of hydraulic cylinder are compared, and the force selection of hydraulic cylinder is analyzed. By comparison with the first edition, the optimization rate of the improved hydraulic cylinder reaches 8 %. Finally, in order to verify the rationality of ADAMS simulation and the effectiveness of hydraulic cylinder improvement, the wear test is carried out, the average errors of leg centroid in normal walking, wearing exoskeleton walking and ADAMS simulation data are compared. The average error rate is less than 10 %. The results show that the simulation model design is reasonable, and the effectiveness of the hydraulic cylinder improvement is verified. The exoskeleton device designed can well follow the human motion. The simulation analysis of the exoskeleton provides important parameters for the manufacture and it also provides theoretical basis for the later control theory.

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“…In this article, we refer to active, sub-active or quasi-passive, and passive exoskeletons. 5,13 –15 That is, we focus on the challenges related to hardware design and implementation of control strategies aiming to improve the kinematic interaction between the user and the robot. In the second section, we introduced the two types of exoskeletons to increase strength: exoskeletons that perform tasks such as rigid lifting and carrying objects and exoskeletons dedicated to carrying loads.…”

Section: Introductionmentioning

confidence: 99%

Engineering design strategies for force augmentation exoskeletons: A general review

Martínez-Mata

Ortega

Guzmán-Valdivia

et al. 2023

International Journal of Advanced Robotic Systems

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In the industrial and military sector, work activities are required transporting or supporting heavy loads manually, affecting this the human spinal column due to the weight of the loads or the repetition of this labor. In this regard, the use of force-enhancing exoskeletons is a potential solution to this issue. Therefore, this article summarizes the state of the art in relevant contributions to structural design, control systems, actuators, and performance metrics to evaluate the proper functioning of exoskeletons used for load support and transfer. This is essential to address current and new open problems in these applications, and this includes reducing the metabolic cost and enhancing the loading force in exoskeletons, in which challenges such as structural design and kinetic interactions between the human and the robot are presented. The systematic review of the strategies found in the literature helps addressing these challenges in an orderly way. The proposal of some alternative solutions could help to solving some of the challenges mentioned above, as well as further research to improve the design of these devices is necessary.

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State Classification and Motion Description for the Lower Extremity Exoskeleton SJTU-EX

Cited by 9 publications

References 18 publications

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Design and simulation analysis of an improved wearable power knee exoskeleton

Engineering design strategies for force augmentation exoskeletons: A general review

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