Jonathon Sensinger, University of New Brunswick

Research

Biomedical Computational Motor Control Human Robot Interface Myoelectric Nonlinear Model Predictive Control Optimal Feedback Control Psychophysics

Latest papers

Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator.
PLoS computational biology · 2018 · senior author
Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?
PloS one · 2015 · first author

All 2 publications

Latest funding

Personalized spinal cord rehabilitation: A pilot study integrating explanatory models with curiosity-driven computational approaches
SSHRC · 2023 · Principal investigator
$250,000
Using stochastic optimal feedback control and computational motor control to design personalized and adaptive human robot interfaces
NSERC · 2021 · Principal investigator
$184,000
Exploration of optimal prosthesis feedback information using computational motor control
NSERC · 2014 · Principal investigator
$217,000

All 4 grants and awards

2 publications.

01

Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator.

Blustein D, Shehata A, Englehart K, Sensinger J

PLoS computational biology2018Senior author

DOI: 10.1371/journal.pcbi.1006501 PMID: 30586387

02

Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?

Sensinger J, Aleman-Zapata A, Englehart K

PloS one2015First author

DOI: 10.1371/journal.pone.0136251 PMID: 26313560

Personalized spinal cord rehabilitation: A pilot study integrating explanatory models with curiosity-driven computational approaches

Principal investigatorSSHRC2023New Frontiers in Research Fund - Exploration

Principal investigators: Sensinger, Jonathon

$250,000

Using stochastic optimal feedback control and computational motor control to design personalized and adaptive human robot interfaces

Principal investigatorNSERC2021Discovery Grants Program - Individual

Principal investigators: Sensinger, Jonathon

Keywords: biomedical; myoelectric; optimal feedback control; nonlinear model predictive control; computational motor control; human robot interface; psychophysics

$184,000

Exploration of optimal prosthesis feedback information using computational motor control

Principal investigatorNSERC2014Discovery Grants Program - Individual

Principal investigators: Sensinger, Jonathon

$217,000

Haptic Interface for: Computational Motor Control for Better Control of Prosthetic Devices

Principal investigatorNSERC2014Research Tools and Instruments - Category 1 (<$150,000)

Principal investigators: Sensinger, Jonathon

$112,372

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Frequent collaborators

Kevin Englehart

Electrical and Computer Engineering

2 shared papers, latest 2018

Write to this professor

A short, specific email works best. This draft uses one of their recent papers; replace the parts in brackets with your own details before sending.

Applying for

Paper to mentionMention their SSHRC grant from 2023

Subject: Prospective PhD student: Biomedical Dear Professor Sensinger, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator." (2018), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your SSHRC-funded project "Personalized spinal cord rehabilitation: A pilot study integrating explanatory models with curiosity-driven computational approaches" (2023), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]