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Reva E. Johnson
Valparaiso University
12Publications
4H-index
41Citations
Publications 12
Newest
#1Mike Wininger (UHart: University of Hartford)
#2Reva E. Johnson (Valpo: Valparaiso University)H-Index: 4
#1Eric J. Earley (NU: Northwestern University)H-Index: 1
#2Reva E. Johnson (Valpo: Valparaiso University)H-Index: 4
Last.Jon Sensinger (UNB: University of New Brunswick)H-Index: 2
view all 4 authors...
Sensory feedback is critical in fine motor control, learning, and adaptation. However, robotic prosthetic limbs currently lack the feedback segment of the communication loop between user and device. Sensory substitution feedback can close this gap, but sometimes this improvement only persists when users cannot see their prosthesis, suggesting the provided feedback is redundant with vision. Thus, given the choice, users rely on vision over artificial feedback. To effectively augment vision, senso...
#1Eric J. Earley (NU: Northwestern University)H-Index: 1
#2Kyle J. KavenyH-Index: 1
Last.Jon Sensinger (UNB: University of New Brunswick)H-Index: 2
view all 5 authors...
Despite significant research developing myoelectric prosthesis controllers, many amputees have difficulty controlling their devices due in part to reduced sensory feedback. Many attempts at providing supplemental sensory feedback have not significantly aided control. We hypothesize this is because the feedback provided contains redundant information already provided by vision. However, whereas vision provides egocentric, position-based feedback, sensory feedback tied to joint coordinates may pro...
#1Reva E. Johnson (Valpo: Valparaiso University)H-Index: 4
#2Konrad Paul Kording (NU: Northwestern University)H-Index: 42
Last.Jonathon W. Sensinger (UNB: University of New Brunswick)H-Index: 10
view all 4 authors...
In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even ...
Apr 1, 2015 in NER (International IEEE/EMBS Conference on Neural Engineering)
#1Sophie A. Daigle (UNB: University of New Brunswick)
#2Reva E. Johnson (Rehabilitation Institute of Chicago)H-Index: 4
Last.Jonathon W. Sensinger (UNB: University of New Brunswick)H-Index: 10
view all 3 authors...
Powered upper limb prostheses typically use EMG to control movement. EMG control is often variable and inefficient, and it is unclear if persons benefit from use of internal models, which have been shown to improve performance with traditional human-machine interfaces. We investigated how internal model use affected errors and effort across a group of 20 subjects using EMG control to perform a tracking task. To vary the ability of subjects to form an internal model, we altered the amount of avai...
Apr 1, 2015 in NER (International IEEE/EMBS Conference on Neural Engineering)
#1Reva E. Johnson (Rehabilitation Institute of Chicago)H-Index: 4
#2Konrad Paul Kording (NU: Northwestern University)H-Index: 42
Last.Jonathon W. Sensinger (UNB: University of New Brunswick)H-Index: 10
view all 4 authors...
EMG control of powered upper limb prostheses is difficult and imprecise. One approach for improving control is to help amputees develop more accurate internal models of their prosthetic device. This may be facilitated by an intuitive mapping of neural signals to device movement, a way of providing sensory feedback, or training methods. A first step, arguably, is to understand how an amputation affects adaptation. Here we studied trial-by-trial adaptation in a simple target-directed task with tra...
#1Reva E. Johnson (NU: Northwestern University)H-Index: 4
#2Konrad Paul Kording (NU: Northwestern University)H-Index: 42
Last.Jonathon W. Sensinger (UNB: University of New Brunswick)H-Index: 10
view all 4 authors...
Powered prostheses are controlled using electromyographic (EMG) signals, which may introduce high levels of uncertainty even for simple tasks. According to Bayesian theories, higher uncertainty should influence how the brain adapts motor commands in response to perceived errors. Such adaptation may critically influence how patients interact with their prosthetic devices; however, we do not yet understand adaptation behavior with EMG control. Models of adaptation can offer insights on movement pl...
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