2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) 2020
DOI: 10.1109/biorob49111.2020.9224359
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Continuous locomotion mode classification using a robotic hip exoskeleton

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Cited by 9 publications
(3 citation statements)
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“…Exoskeletons with a rigid frame and actuated joints have been studied and commercialized for numerous applications in rehabilitation, walking assistance, and human augmentation (Young and Ferris, 2017 ). In general, these devices have key advantages in adapting to both the environment and user through advanced sensing and artificial intelligence capabilities (Cardona et al, 2020 ; Kang et al, 2020 ; Vélez-Guerrero et al, 2021 ). In addition, hard active devices have been shown to augment human performance in terms of reducing the metabolic cost of ambulation (Mooney et al, 2014 ; Seo et al, 2016 ; Sawicki et al, 2020 ) and joint loading (Weston et al, 2018 ).…”
Section: Useful Exoskeleton Design Features For An Austere Environmentmentioning
confidence: 99%
“…Exoskeletons with a rigid frame and actuated joints have been studied and commercialized for numerous applications in rehabilitation, walking assistance, and human augmentation (Young and Ferris, 2017 ). In general, these devices have key advantages in adapting to both the environment and user through advanced sensing and artificial intelligence capabilities (Cardona et al, 2020 ; Kang et al, 2020 ; Vélez-Guerrero et al, 2021 ). In addition, hard active devices have been shown to augment human performance in terms of reducing the metabolic cost of ambulation (Mooney et al, 2014 ; Seo et al, 2016 ; Sawicki et al, 2020 ) and joint loading (Weston et al, 2018 ).…”
Section: Useful Exoskeleton Design Features For An Austere Environmentmentioning
confidence: 99%
“…the lower limb exoskeleton. In recent years, Young and his colleagues made remarkable progress in NNbased gait phase estimation for multimodal locomotion (Camargo et al, 2021;Kang et al, 2021), which is applied to hip exoskeleton (Kang et al, 2020(Kang et al, , 2021, knee exoskeleton (Lee et al, 2021), and ankle exoskeleton (Shafer et al, 2021). For hip exoskeleton control, they developed a sensor fusion-based NN model to estimate the continuous gait phase in real-time, which can adapt to variable walking speeds ranging from 0.6 to 1.1 m/s (Kang et al, 2021).…”
Section: Gait Phase Estimationmentioning
confidence: 99%
“…Seo et al ( 2019 ) developed a neural network model based on sensor fusion to estimate the gait phase adaptable to dynamic speed in real-time and verified the feasibility of using a machine learning model to accurately estimate the gait phase and improve the controllability of the lower limb exoskeleton. In recent years, Young and his colleagues made remarkable progress in NN-based gait phase estimation for multimodal locomotion (Camargo et al, 2021 ; Kang et al, 2021 ), which is applied to hip exoskeleton (Kang et al, 2020 , 2021 ), knee exoskeleton (Lee et al, 2021 ), and ankle exoskeleton (Shafer et al, 2021 ). For hip exoskeleton control, they developed a sensor fusion-based NN model to estimate the continuous gait phase in real-time, which can adapt to variable walking speeds ranging from 0.6 to 1.1 m/s (Kang et al, 2021 ).…”
Section: Control Strategy Of Robotic Hip Exoskeletonmentioning
confidence: 99%