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作者:Jonathon Sensinger , Adrian Aleman-Zapata , Kevin Englehart
来源:[J].PLoS ONE(IF 3.73), 2017, Vol.10 (8)DOAJ
摘要:Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which...
作者:Kevin Englehart , Winnie Jensen ...
来源:[J].Journal of Electromyography and Kinesiology(IF 1.644), 2018, Vol.40, pp.72-80Elsevier
摘要:Abstract(#br)While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six ampu...
作者:Ashkan Radmand , Erik Scheme , Kevin Englehart
来源:[J].Journal of Rehabilitation Research and Development(IF 1.779), 2016, Vol.53 (4), pp.443-456ProQuest
摘要:Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in highe...
作者:Levi Hargrove , Kevin Englehart , Bernard Hudgins
来源:[J].Biomedical Signal Processing and Control(IF 1.074), 2007, Vol.3 (2), pp.175-180Elsevier
摘要:Abstract(#br)Pattern recognition based myoelectric control systems rely on detecting repeatable patterns at given electrode locations. This work describes an experiment to determine the effect of electrode displacements on pattern classification accuracy, and a classifier trainin...
作者:Kevin Englehart , Levi Hargrove
来源:[J].Journal of Electromyography and Kinesiology(IF 1.644), 2012, Vol.22 (3), pp.478-484Elsevier
摘要:Abstract(#br)Pattern recognition based control of powered upper limb myoelectric prostheses offers a means of extracting more information from the available muscles than conventional methods. By identifying repeatable patterns of muscle activity across multiple muscle sites ...
作者:... Ning Jiang , Kevin Englehart , Philip Parker
来源:[J].Medical & Biological Engineering & Computing(IF 1.79), 2010, Vol.48 (1), pp.67-77Springer
摘要:Abstract(#br)Inherent limitations of the surface myoelectric signal, such as the lack of recording sites in high-level amputations, and the sensitivity to placement and impedance effects, confound its wider application in powered prostheses. Since a functionally topographic distr...
作者:... Ahmed Shehata , Kevin Englehart , Jonathon Sensinger
来源:[J].PLOS Computational Biology(IF 5.215), 2018, Vol.14 (12)PLOS
摘要:Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person’s response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used t...
作者:... Mohammad Ali Akhaee , Erik Scheme , Kevin Englehart
来源:[J].PLOS ONE(IF 3.73), 2018, Vol.13 (9)PLOS
摘要:The evolution of deep learning techniques has been transformative as they have allowed complex mappings to be trained between control inputs and outputs without the need for feature engineering. In this work, a myoelectric control system based on convolutional neural network...
作者:Lee Danisch , Kevin Englehart , Andrew Trivett
来源:[J].Sensor Review(IF 0.658), 1999, Vol.19 (2), pp.106-112Emerald
摘要:This paper describes SHAPE TAPE™, a thin array of fiber optic curvature sensors laminated on a ribbon substrate, arranged to sense bend and twist. The resulting signals are used to build a three dimensional computer model containing six degree of freedom position and orienta...
作者:Jonathon Sensinger , Adrian Aleman-Zapata , Kevin Englehart
来源:[J].PLOS ONE(IF 3.73), 2015, Vol.10 (8)PLOS
摘要:Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which...

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