Ordinary tasks, such as handling a cup of water. may become hard challenges for the survivors of brain injury. These studies investigate how healthy subjects control the arm when it comes in contact with the environment. Understanding how the nervous system learns to control the interactions with objects is of great importance in motor rehabilitation because interactive behaviors are essential to many activities of daily living. The proposed research is divided in three specific aims:
AIM 1 : To investigate the control of upper extremity interactions with mechanical constraints.These studies investigate how information about the shape of a surface is acquired through touch and how shape affects the execution of hand movements over the surface. Subjects are asked to move their hand while holding a robotic device that reproduces the mechanics of surfaces with variable shape. We formulate the hypotheses that, first. in the absence of vision, repeated movements of the hand over a curved surface lead to an adaptive process and second, that adaptation reveals, through the change in movement trajectories, an automatic and implicit learning of the surface geometry.
AIM 2 : To understand the control of objects with internal degrees of freedom.These studies are aimed at assessing how healthy subjects learn to control objects that are not rigid, like, for instance, a bucket full of water. The experiments pursue the hypothesis that subjects develop through practice internal representation of the manipulated objects. These representations allow them to predict how objects respond to applied forces and motions. To test this hypothesis, these studies make use of virtual mass-spring systems with which subjects interact.
AIM 3 : To identify effective training principles for enhancing skillful interactions with objects.These studies are aimed at evaluating the efficacy of different training methods for enhancing arm movement performance during object manipulation. The studies focus on training methods that may enhance one's ability to control with their arm the behavior of objects with complex dynamics. The experiments will test the efficacy of three training methods: i) the explicit information about correct movements of the hand; ii) the presentation of isolated cues about the effectiveness of control actions, and iii) the enhancement of visual feedback on the overall behavior of a manipulated object.
|Farshchiansadegh, Ali; Melendez-Calderon, Alejandro; Ranganathan, Rajiv et al. (2016) Sensory Agreement Guides Kinetic Energy Optimization of Arm Movements during Object Manipulation. PLoS Comput Biol 12:e1004861|
|Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A (2015) Learning to push and learning to move: the adaptive control of contact forces. Front Comput Neurosci 9:118|
|Saha, Devjani J; Hu, Xiao; Perreault, Eric et al. (2015) The coordinate system for force control. Exp Brain Res 233:899-908|
|Berniker, Max; Franklin, David W; Flanagan, J Randall et al. (2014) Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning. J Neurophysiol 111:1165-82|
|Patton, James L; Wei, Yejun John; Bajaj, Preeti et al. (2013) Visuomotor learning enhanced by augmenting instantaneous trajectory error feedback during reaching. PLoS One 8:e46466|
|Piovesan, D; Melendez-Calderon, A; Mussa-Ivaldi, F A (2013) Haptic recognition of dystonia and spasticity in simulated multi-joint hypertonia. IEEE Int Conf Rehabil Robot 2013:6650449|
|Melendez-Calderon, A; Piovesan, D; Mussa-Ivaldi, F A (2013) Therapist recognition of impaired muscle groups in simulated multi-joint hypertonia. IEEE Int Conf Rehabil Robot 2013:6650425|
|Piovesan, D; Melendez-Calderon, A; Mussa-Ivaldi, F A (2013) Haptic perception of multi-joint hypertonia during simulated patient-therapist physical tele-interaction. Conf Proc IEEE Eng Med Biol Soc 2013:4143-7|
|Piovesan, Davide; Morasso, Pietro; Giannoni, Psiche et al. (2013) Arm stiffness during assisted movement after stroke: the influence of visual feedback and training. IEEE Trans Neural Syst Rehabil Eng 21:454-65|
|Piovesan, Davide; Casadio, Maura; Mussa-Ivaldi, Ferdinando A et al. (2012) Comparing Two Computational Mechanisms for Explaining Functional Recovery in Robot-Therapy of Stroke Survivors. Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron 2012:1488-1493|
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