Supernumerary Body Schema Extension to Non-Corporeal Object by Adding Artificial Tactile Feedback using Electrical Stimulation
Conference Paper
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
2019 IEEE. Technological advancement could increase the degree of freedom in devices and tools, which often results in complex control interface. To decrease the learning time and increase usability of complex control interface, intuitiveness becomes an important issue. One interesting and powerful solution to increase intuitiveness in controlling non-corporeal objects is integrating them to the existing body schema. The hypothesis here is that this can be done by establishing a bi-directional communication interface between the body and the non-corporeal object (i.e., establishing control and sensory feedback, along with congruent visual feedback). In this study, a table tennis racket was modified to provide a bi-directional communication interface between the table tennis racket and the peripheral nervous system on the right palm.Two types of experiments were conducted on a single subject to test the hypothesis of body schema extension. The first experiment was designed to test congruent mapping of racket locations to the hand. The second experiment was designed to test the accuracy of perceived location along with the ownership and agency. We measured the error between the location of local pressure on the table tennis racket and the perceived location on the hand, along with a subjective questionnaire on ownership and agency of the table tennis racket. The first experiment confirmed an accurate and congruent mapping of the racket onto the hand. The second experiment showed a significant decrease in error in perceived location, with t-tests displaying statistical difference, after the subject was trained with the bi-directional interface. This study suggests that a bi-directional interface may extend the body schema to include non-corporeal objects.
name of conference
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
