Name
Abstract | ||
|---|---|---|
Motor adaptation studies can provide insight into how the brain handles ascending and descending neural signals during motor tasks, revealing how neural pathologies affect the capacity to learn and adapt to movement errors. Such studies often involve reaches towards prompted target locations, with adaptation and learning quantified according to Euclidean distance between reach endpoint and target location. This paper describes methods to calculate steady-state error using knowledge of the distribution of univariate, bivariate, and multivariate steady-state reaches. Additionally, in cases where steady-state error is known or estimated, it does not fully describe underlying reach distributions that could be observed at steady-state. Thus, this paper also investigates methods to describe univariate, bivariate, and multivariate steady-state reaching behavior using knowledge of the estimated steady-state error. These methods may yield a clearer understanding of adaptation and steady-state reaching behavior, allowing greater opportunities for inter-study comparison and modeling. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857562 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Monte Carlo method,Task analysis,Computer science,Multivariate statistics,Euclidean distance,Algorithm,Artificial intelligence,Steady state,Bivariate analysis,Univariate,Probability density function | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric J. Earley | 1 | 0 | 0.34 |
| Levi J Hargrove | 2 | 438 | 42.47 |