One central feature of the human motor system is the presence of redundancy as pointed out by Bernstein (1967). The redundancy implies that the number of variable at a given level of analysis is higher than the number of constraints. Thus, there are an infinite number of solutions to execute the task. Several principles used by the central nervous system (CNS) to solve the problem of redundancy had been highlighted over the past few years. The redundancy is a multilevel scientific issue. At the upper limb level we can observe up to three levels:
- First level: kinematics. For a given endpoint (i.e. the wrist) the CNS can chose an infinite number of paths.
- Second level: dynamics level. Fingers are parallel elements that you can use to produce forces. The number of force configurations under the finger is endless. Thus the CNS has to choose a give force sharing pattern among the fingers.
- Third level: muscle tensions. For a given moment at a joint level, muscle tensions relationship in between agonist and antagonist muscles are also infinite.
The biomechanics theme of the Hybrid Sensorimotor Performance team has three sub goals to help to understand how the CNS answers the redundancy issue:
The first sub-goal concerns the determination of fingertip forces during prehension tasks such as key pinch, tip pinch, grasp and grip. Four-fingers pressing tasks are also important to reveal CNS controls. The data we can collect at the fingertip level will help to highlight motor control laws (e.g. Enslaving, Force Deficit, Sharing Pattern, etc.)
The second sub-goal concerns the analysis of muscle pattern during a prehension task to better understand CNS behavior. Using an accurate biomechanical model of the hand can make a real difference for tendon tension estimations. We develop a realistic biomechanical model of the hand using AnimatLab ® which can also tune the muscle activation via motoneurons activities.
The third sub-goal concerns the Inclusion of EMG (the only neural signal relative to muscle contraction) to the biomechanical model in order to increase the accuracy of the estimation of the tendon tension. EMG can highlight each muscle activity. We can use these measures to constrain the biomechanical model and refine the estimations.
Based on Merletti’s studies on High density EMG, we develop our own transportable HDEMG device in partnership with the GIPSA-lab (Grenoble). In this situation, we consider muscle activity as a picture and we analyze the temporal change of this picture. Actual methods allow seeing only two points of this picture (bipolar detection) which is not enough to link muscle activity to the movement or fingertip force production. The sub goal relative to the development of this device is to analyze the entire picture using High Density Electrodes up to 128 detection points. The HDEMG will introduce a new way to visualize muscle activity and provide new information such as motor point displacement.
