Sensory mechanisms involved in obtaining frictional information for perception and grip force adjustment during object manipulation

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Copyright: Afzal, Hafiz
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Abstract
Sensory signals informing about frictional properties of a surface are used both for perception to experience material properties and for motor control to be able to handle objects using adequate manipulative forces. There are fundamental differences between these two purposes and scenarios, how sensory information typically is obtained. This thesis aims to explore the mechanisms involved in the perception of frictional properties of the touched surfaces under conditions relevant for object manipulation. Firstly, I show that in the passive touch condition, when the surface is brought in contact with immobilised finger, humans are unable to use existing friction-related mechanical cues and perceptually associate them with frictional properties. However, a submillimeter range lateral movement significantly improved the subject's ability to evaluate the frictional properties of two otherwise identical surfaces. It is demonstrated that partial slips within the contact area and fingertip tissue deformation create very potent sensory stimuli, enabling tactile afferents to signal friction-dependent mechanical effects translating into slipperiness (friction) perception. Further, I demonstrate that natural movement kinematics facilitate the development of such small skin displacements within the contact area and may play a central role in enabling the perception of surface slipperiness and adjusting grip force to friction when manipulating objects. This demonstrates intimate interdependence between the motor and sensory systems. This work significantly extends our understanding of fundamental tactile sensory processes involved in friction signaling in the context of motor control and dexterous object manipulation tasks. This knowledge and discovered friction sensing principles may assist in designing haptic rendering devices and artificial tactile sensors as well as associated control algorithms to be used in robotic grippers and hand prostheses.
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Publication Year
2023
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Thesis
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PhD Doctorate
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