A Multi-Body Model of an upper-limb prosthesis for grip force estimation and related object interaction application

Hand amputation greatly affects the ability of a person to perform activities of daily living (ADLs). For this reason, prosthetic hands should present grasping characteristics to allow the manipulation of objects of different shapes and dimensions. This is the case of the Hannes prosthetic hand, an under-actuated myoelectric prosthesis able to adapt the grasping configuration to the object shape using the actuation of a single motor and the differential mechanism that characterize this device. In this paper, we present the development of a multi-body and multi-domain model of the Hannes hand, which was experimentally validated using an external grasp force sensor to compare the model outcomes to the actual results. The model is used to investigate the correlation between available measurements from the prosthesis and the stiffness of the grasped objects, passing by the exploration of one of the most challenging aspects for tendon-driven under-actuated prosthetic devices: the friction experienced in the transmission mechanism. Therefore, the current analysis leads to the development of a novel control strategy through the use of an object stiffness classifier. The work provides an alternative model-based approach to overcome the absence of force sensors in under-actuated prosthetic hands for object recognition tasks. The obtained results are promising with contained percentage errors.