Estimating Human Movements Using Memory of Errors

Daqi Dong, Stan Franklin, Pulin Agrawal

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Humans estimate their movements based on their knowledge of the dynamics of the environment, and on actual sensory data. Wolpert and colleagues have incorporated this understanding into a model that simulates this estimation using the Kalman filter [1]. Inspired by a recent study in neuroscience [2], we here introduce a new factor - memory of errors - into this simulation of movement estimation. These historical errors help humans determine the stability of the environment, which could be either steady or rapidly changing. This condition controls the rate at which a given error will be learned, so as to affect the estimates of future movements. We here apply our new model, a modified Kalman filter incorporating memory of errors, to the simulation of a hand lifting movement, and compare the simulated estimation process with its human counterpart.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalProcedia Computer Science
StatePublished - 2015
Event6th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2015 - Lyon, France
Duration: Nov 6 2015Nov 8 2015

All Science Journal Classification (ASJC) codes

  • General Computer Science


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