A soft-contact model for computing safety margins in human prehension |
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| Affiliation: | 1. College of Health Professionals, Medical University of South Carolina, Charleston, SC 29425, United States;2. Department of Health and Kinesiology, Purdue University, West Lafayette, IN 47907, United States;1. School of Kinesiology & Health Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada;1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran;2. Lauflabor Locomotion Laboratory, TU Darmstadt, Darmstadt, Germany;3. Department of Motion Science at Friedrich-Schiller-University Jena, Germany;1. UMR CNRS 7338, Biomécanique et Bio-ingénierie, Université de Technologie de Compiègne, Compiègne, France;2. School of Science and Engineering, Reykjavik University, Reykjavik, Iceland;3. Laboratoire Traitement du Signal et de L’Image, INSERM, Université de Rennes 1, Campus de Beaulieu, Rennes, France;1. Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstr. 190, Zurich CH-8057, Switzerland;2. Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland;3. Institute of Cognitive Neuroscience, University College London, United Kingdom;1. College of Computer and Control Engineering, Nankai University, China;2. State Key Laboratory of Integrated Service Networks, Xidian University, China;3. School of Information Science, Korean Bible University, South Korea;1. Lehrstuhl für Bewegungswissenschaft, Technische Universität München, Georg-Brauchle-Ring 60-62, München, Germany;2. Centre for Sports Medicine and Human Performance, Brunel University, Middlesex UB83PH, UK;3. School of Psychology, Queen’s University Belfast, Belfast, UK |
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| Abstract: | The soft human digit tip forms contact with grasped objects over a finite area and applies a moment about an axis normal to the area. These moments are important for ensuring stability during precision grasping. However, the contribution of these moments to grasp stability is rarely investigated in prehension studies. The more popular hard-contact model assumes that the digits exert a force vector but no free moment on the grasped object. Many sensorimotor studies use this model and show that humans estimate friction coefficients to scale the normal force to grasp objects stably, i.e. the smoother the surface, the tighter the grasp. The difference between the applied normal force and the minimal normal force needed to prevent slipping is called safety margin and this index is widely used as a measure of grasp planning. Here, we define and quantify safety margin using a more realistic contact model that allows digits to apply both forces and moments. Specifically, we adapt a soft-contact model from robotics and demonstrate that the safety margin thus computed is a more accurate and robust index of grasp planning than its hard-contact variant. Previously, we have used the soft-contact model to propose two indices of grasp planning that show how humans account for the shape and inertial properties of an object. A soft-contact based safety margin offers complementary insights by quantifying how humans may account for surface properties of the object and skin tissue during grasp planning and execution. |
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| Keywords: | Soft-contact Safety margins Friction Grasp planning Prehension |
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