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FMRI and MEG Compatible Hand Motion Sensor

EasyChair Preprint no. 8170

2 pagesDate: June 1, 2022


Combing neuroimaging with motion sensor data greatly benefit investigations of somatosensory function. However, constructing wearable sensors compatible with Functional magnetic resonance imaging (fMRI)) and magnetoencephalography (MEG) poses a significant design challenge due to constraints on electronics and ferromagnetic components. Existing fibre optic or vision based solutions are either expensive or require unfeasible line of sight. We propose a soft MR/MEG compatible joint sensor using fluidic transmission and metal free construction to track finger motion during grasping tasks. Bending of the finger joint displaces the liquid within the sensor chamber, which is transmitted a sufficient distance from the sensitive equipment through tubing to a display and detected using computer vision. Experiments on a 3D printed cable driven prosthetic hand showed a low repeatability error (<1%) and the finger pose was reconstructed with an accuracy of 6.8%, and demonstrated the feasibility of the sensor as a wearable device for tracking hand motion in neuroimaging experiments.

Keyphrases: computer vision, Neuroimaging, soft-sensing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Lingyu Lyu and Elena Monfort-Sanchez and Mark Runciman and George Mylonas and James Avery},
  title = {FMRI and MEG Compatible Hand Motion Sensor},
  howpublished = {EasyChair Preprint no. 8170},

  year = {EasyChair, 2022}}
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