Gait Neural Network for Human-Exoskeleton Interaction

Fang, Bin; Zhou, Quan; Sun, Fuchun; Shan, Jiayuan; Wang, Ming; Cheng, Xin; Zhang, Qin

doi:10.3389/fnbot.2020.00058

Cited by 30 publications

(18 citation statements)

References 18 publications

(17 reference statements)

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“…They are capable of learning from unlabeled data without human supervision (unsupervised learning) and creating patterns for decision-making to perform some tasks. However, the majority of NNs are trained in a supervised way by humans (supervised learning) [156,157]. With the exponential growth in AI, DL is valid in reinforcement learning (RL) as a function approximator.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Design and Control of Lower Extremity Exoskeletons for Bipedal Walking

2022

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Exoskeleton robots are electrically, pneumatically, or hydraulically actuated devices that externally support the bones and cartilage of the human body while trying to mimic the human movement capabilities and augment muscle power. The lower extremity exoskeleton device may support specific human joints such as hip, knee, and ankle, or provide support to carry and balance the weight of the full upper body. Their assistive functionality for physically-abled and disabled humans is demanded in medical, industrial, military, safety applications, and other related fields. The vision of humans walking with an exoskeleton without external support is the prospect of the robotics and artificial intelligence working groups. This paper presents a survey on the design and control of lower extremity exoskeletons for bipedal walking. First, a historical view on the development of walking exoskeletons is presented and various lower body exoskeleton designs are categorized in different application areas. Then, these designs are studied from design, modeling, and control viewpoints. Finally, a discussion on future research directions is provided.

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Section: Discussionmentioning

confidence: 99%

A Survey on Design and Control of Lower Extremity Exoskeletons for Bipedal Walking

2022

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“…Abedin et al [72] 57. 19 Fang et al [73] 79.24 Maitre et al [74] 84.89 Rasnayaka et al [75] 85 O'Halloran et al [76] 90. 55 Sun et al [77] 88 Tahir et al [23] 90.91 Badawi et al [25] 88 Masum et al [78] 91.…”

Section: Methods Accuracy Using Mhealth (%) Methods Accuracy Using Hugamentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

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“…Zaroug et al [19] used LSTM networks for multi-timestep forecasting of lower limb trajectories using motion capture data. Fang et al [20] implemented a gait neural network (GNN) using temporal convolutional networks for gait mode recognition and gait trajectory prediction using data from an array of sensors attached to the lower limbs and pelvis.…”

Section: Related Workmentioning

confidence: 99%

A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

Dey

Schilling²

2023

IEEE Trans. Ind. Inf.

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Lower limb dysfunction hinders the locomotive activity in individuals and compels them to use assistive devices such as prostheses or orthoses to restore the missing locomotive functions. Passive assistive devices have limitations in replacing the lost functionality. On the other hand, active devices could restore more natural locomotion using motorized joints. To make proper use of these embedded motors, a control architecture is required to translate the motion intent of the user into the intended prosthesis joint trajectories. Here, we propose a temporal convolution-based online foot angle trajectory prediction network (FATP-N) that predicts the foot angular positions during unconstrained walking from the shank angular position measured using a single wearable motion tracker sensor. The data acquisition experiments were designed to reflect natural gait with turns, varying inclines, speeds, and mixed cadences. The trained models were prepared for real-time prediction by compressing them to reduce the storage and time complexity. The validations were performed on different motion conditions, including those that have not been used for model training, to verify the robustness of the model. The proposed FATP-N achieved high accuracy and could generate predictions in real-time. The results indicate that the proposed approach offers a possible architecture for real-time control of powered intelligent prostheses.

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Gait Neural Network for Human-Exoskeleton Interaction

Cited by 30 publications

References 18 publications

A Survey on Design and Control of Lower Extremity Exoskeletons for Bipedal Walking

A Survey on Design and Control of Lower Extremity Exoskeletons for Bipedal Walking

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet

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