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AuthorRasouli, Mahdi
AuthorChellamuthu, Karthik
AuthorCabibihan, John-John
AuthorKukreja, Sunil L.
Available date2021-09-01T10:02:42Z
Publication Date2016
Publication NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
ResourceScopus
URIhttp://dx.doi.org/10.1109/BIOROB.2016.7523629
URIhttp://hdl.handle.net/10576/22363
AbstractReliable decoding of a user's intention is a key step to control prosthetic devices. Force myography (FMG) is often used to assess topographic force patterns resulting from volumetric changes of activated muscles. However, during limb position changes this approach may give deteriorating performance over time. To address this limitation, we developed a position-Aware platform that integrates an inertial measurement unit (IMU) and a force sensing array (FSA) with an advanced signal processing module. The module analyzes data using an artificial neural network (ANN) to predict an intended hand movement. Our results demonstrate that by utilizing multi-sensory information this decoding strategy provides a 90% accuracy. 2016 IEEE.
Languageen
PublisherIEEE Computer Society
SubjectDecoding
Neural networks
Prosthetics
Robotics
Signal processing
Units of measurement
Activated muscles
Advanced signal processing
Decoding strategy
Inertial measurement unit
Prosthetic devices
Upper prosthetic limbs
User's intentions
Volumetric changes
Artificial limbs
TitleTowards enhanced control of upper prosthetic limbs: A force-myographic approach
TypeConference Paper
Pagination232-236
Volume Number2016-July
dc.accessType Abstract Only


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