• synergies
• Arduino
• central nervous system
• control
• electromyographic
• extended Kalman filter
• functional principal component analysis
• human upper limb
• inertial measurement units
• joint angle
• kinematic model
• minimum variance estimation
• neural network
• non-negative matrix factorization
• non-negative matrix factorization time varying
• principal component analysis
• robotic protheses
• Simulink
• TinyML

In this work, I investigate how the synergistic coupling between different upper limb degrees of freedom can be exploited to estimate the temporal evolution of the muscular-skeletal state (electromyographic EMG and kinematics measurements) of the whole limb, from a reduced number of noisy data.

First, I applied the functional component analysis to EMG and postural data of human upper limb to obtain the basis functions. Then, I combined this functions with the scarce sensory information to solve minimum variance estimation. Furthermore, this approach integrated with an optimization problem allows us to identify which degrees of freedom should be measured to minimize the a posteriori covariance matrix.

Finally, leveraging the coupling between the upper limb degrees of freedom, I developed a control strategy for robotic trans-radial prostheses in which a neural network is trained to predict -from a given motion of the residual limb composed by the shoulder and the elbow- the most likely motion of the three-degrees of freedom wrist. I performed a first implementation of the sensing and computational framework using four Inertial Measurement Units to track the motion of the residual upper limb that was fed into the motion predictor. The deployment of the neural architecture was performed at the edge on an Arduino Nano 33 BLE board, minimizing the usage of external computational sources. Extensive experiments to test the proposed framework were carried out using a virtual avatar.