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The goal of this study is to construct a mathematical model connecting with motor commands from the brain and handwriting movements on a forearm-hand-pen system. It is assumed that the motor commands can be known indirectly from the electromyographic (EMG) signals on the forearm surface. This is equivalent to be possible to predict written letters from the EMG signals. At first, the EMG signals and the pen-tip movements on writing some letters are measured. The measured EMG signals are analyzed by the locally-stationary multivariate auto-regressive (AR) model. Then, we assume that the locally-stationary EMG signals are the stochastic process based on the AR model driven by the motor commands as Gaussian white noise and we can estimate the electronic signals to the motor commands on each forearm muscle. Moreover, we wish to describe and identify the forearm-hand-pen system as the parametric linear model whose inputs are the estimated motor commands and whose outputs are the pen-tip movements on writing letters. Finally, we would like to reproduce written letters from the measured EMG signals and discuss the results.

We present a method for contactless manipulation of multiple small objects in a plane using multiple air jets. When the objects are initially clustered as a group, the group's center of gravity is used as a representative point and the entire group is manipulated. When the objects are initially scattered, the object farthest from the goal is selected and individually controlled. Four jets allowing control of airflow rate and angle are used for a position manipulation experiment with five small balls, and the efficacy of the proposed method is compared and discussed.

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