Bayesian approach to handle missing limbs in Neuroprosthetics
2014 (English)Conference paper, Poster (Refereed)
Motor synergies have been supposed to simplify motor control -. In order to test it, we exploit the correlations of our hand's joints to discover some underlying simplicity in a complex stream of behavioral actions. Instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. Therefore, we asked 7 subjects to interact in 17 daily-life situations and quantified behavior in principled manner using cyber glove technology. We combined Probabilistic Principal Component Analysis (PPCA) with a Bayesian classifier to analyze the data. Our key findings are: 1. we confirmed that hand control is low-dimensional, where 4-5 dimensions were sufficient to explain 80-90% of the variability in the movement data . 2. We established a universally applicable measure of manipulative complexity that allowed us to measure this quantity across vastly different tasks. 3. We discovered that within the first 1000 ms of an action the hand shape already configures itself to vastly different tasks, enabling us to reliable predict the action intention . 4. We suggest how using the statistics of natural finger movements paired with Bayesian latent variable model can be used to infer the movements of missing limbs from existing limbs to control e.g. a prosthetic device. Overall, these predictabilities could be used to build intelligent Neuroprosthetics for lost fingers that implement the task from the movement of the remaining limbs.
Place, publisher, year, edition, pages
Computer Science Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-191565DOI: 10.12751/nncn.bc2014.0166OAI: oai:DiVA.org:kth-191565DiVA: diva2:957396
Bernstein Conference 2014, September 2 - 5, 2014, Göttingen
QC 201609022016-09-012016-09-012016-09-02Bibliographically approved