Evaluation of safety-related performance of wearable lower limb exoskeleton robot (WLLER): A systematic review

Wang, Duojin; Gu, Xiaoping; Li, Wenzhuo; Jin, Yaoxiang; Yang, Maisi; Yu, Hongliu

doi:10.1016/j.robot.2022.104308

Cited by 7 publications

(2 citation statements)

References 100 publications

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“…Estimating muscle forces is an important research direction in studying human motion control mechanisms and understanding joint mechanics. Furthermore, estimating muscle forces is essential in many research fields, including quantitative assessment of the contribution of different muscles in human motion [2], quantitative evaluation of muscle injury and evaluation of rehabilitation training effectiveness [3,4], and providing theoretical support for the design of rehabilitation equipment, such as prostheses, orthotics, and exoskeletons [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System

Liu,

et al. 2024

Sensors

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Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN–LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN–LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN–LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

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Section: Introductionmentioning

confidence: 99%

Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System

Liu,

et al. 2024

Sensors

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“…[1][2][3][4] Over the past decade, lowerlimb exoskeletons for walking assistance have constantly evolved, and many have been developed and described in the literature. [5][6][7][8] The currently available devices, such as Rewalk™ (ReWalk Robotics Ltd, Yokneam, Israel), 9,10 Ekso™ (Ekso Bionics Holdings, Inc., Richmond, California, USA), 11,12 HAL ® (Cyberdyne Inc., Tsukuba, Japan), [13][14][15] Indego ® (Parker Hannifin Corp., Cleveland, Ohio, USA), 16,17 Mina v2 (Florida Institute for Human and Machine Cognition, Pensacola, Florida, USA), 18,19 and WalkON Suit (Sogang University, Seoul, South Korea), 20,21 have different design specifications in terms of overall configuration, weight, and functionality. Many commonalities of these devices are listed as follows.…”

Section: Introductionmentioning

confidence: 99%

Stable gait generation method for lower-limb exoskeleton based on instrumented crutches

Zheng,

Gao,

Zhao

et al. 2023

International Journal of Advanced Robotic Systems

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Lower-limb exoskeletons have attracted considerable interest because they can provide impaired individuals with the ability to walk upright. Trajectory generation of walking gait is a crucial issue for lower-limb exoskeletons that has not yet been satisfactorily solved. The purpose of this article is to study a stable gait generation method considering the subjective walking intention. Inspired by the motion synergy between crutches and lower limbs, a stable gait generation method for flat-ground walking was proposed for a lower-limb exoskeleton. The motion synergy in the sagittal plane between the crutch-pitch-angle and step length was validated using theoretical and experimental methods. The synergistic relationship between the maximum crutch-pitch-angle and step-length coefficient was established and optimized. Based on the synergistic relationship, the gait trajectory of the exoskeleton with variable step length can be instinctively generated with a crutch swing. The gait stability of the human-exoskeleton system was modeled and analyzed. Consecutive walking experiments were conducted on flat ground in which a compact lower-limb exoskeleton and pair of instrumented crutches were employed. The results demonstrate that gait based on the synergistic relationship is effective and stable, thereby verifying the feasibility of this human-in-the-loop gait generation method.

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Adaptive Gait Trajectory and Event Prediction of Lower Limb Exoskeletons for Various Terrains Using Reinforcement Learning

Yu,

Zhao,

Chen

et al. 2023

J Intell Robot Syst

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Evaluation of safety-related performance of wearable lower limb exoskeleton robot (WLLER): A systematic review

Cited by 7 publications

References 100 publications

Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System

Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System

Stable gait generation method for lower-limb exoskeleton based on instrumented crutches

Adaptive Gait Trajectory and Event Prediction of Lower Limb Exoskeletons for Various Terrains Using Reinforcement Learning

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