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Bayesian approach to handle missing limbs in Neuroprosthetics
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. University of Freiburg, Germany; Imperial College London.
Imperial College London.
2014 (Engelska)Konferensbidrag, Poster (med eller utan abstract) (Refereegranskat)
Abstract [en]

Motor synergies have been supposed to simplify motor control [1]-[5]. 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 [6]. 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 [6]. 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.

References

  1. Santello, M., Flanders, M., Soechting, J.F. (1998). Postural hand synergies for tool use. J Neurosci. 18, 10105–10115.
  2. Todorov, E., Ghahramani, Z. (2004). Analysis of the synergies underlying complex hand manipulation. Conf Proc. IEEE Eng. Med. Biol. Soc. 6, 4637-4640.
  3. Weiss, E., Flanders, M. (2004). Muscular and Postural Synergies of the Human Hand. J. Neurophysiol. 92, 523-535.
  4. Tresch, M.C., Cheung, V.C.K., d’Avella, A. (2006). Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J Neurophysiol. 95, 2199–2212.
  5. Faisal, A., Stout D., Apel, J., Bradley, B. (2010). The Manipulative Complexity of Lower Paleolithic Stone Toolmaking. PloS ONE 5(11): e13718.
  6. Belić, J.J., Faisal, A.A. (2011). The structured variability of finger motor coordination in daily tasks. BMC Neuroscience, doi:10.1186/1471-2202-12-S1-P102.
Ort, förlag, år, upplaga, sidor
2014.
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:kth:diva-224228DOI: 10.12751/nncn.bc2014.0166OAI: oai:DiVA.org:kth-224228DiVA, id: diva2:1190551
Konferens
Bernstein Conference 2014, September 2 - 5, 2014, Göttingen
Anmärkning

QC 20180326

Tillgänglig från: 2018-03-14 Skapad: 2018-03-14 Senast uppdaterad: 2018-03-26Bibliografiskt granskad

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Förlagets fulltexthttps://abstracts.g-node.org/abstracts/c90279c2-4bf3-4184-9580-fbc1fcc185b4

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