The central nervous system uses stereotypical combinations of the three wrist/forearm joint angles to point in a given (2D) direction in space. In this paper, we first confirm and analyze this Donders' law for the wrist as well as the distributions of the joint angles. We find that the quadratic surfaces fitting the experimental wrist configurations during pointing tasks are characterized by a subject-specific Koenderink shape index and by a bias due to the prono-supination angle distribution. We then introduce a simple postural model using only four parameters to explain these characteristics in a pointing task. The model specifies the redundancy of the pointing task by determining the one-dimensional task-equivalent manifold (TEM), parameterized via wrist torsion. For every pointing direction, the torsion is obtained by the concurrent minimization of an extrinsic cost, which guarantees minimal angle rotations (similar to Listing's law for eye movements) and of an intrinsic cost, which penalizes wrist configurations away from comfortable postures. This allows simulating the sequence of wrist orientations to point at eight peripheral targets, from a central one, passing through intermediate points. The simulation first shows that in contrast to eye movements, which can be predicted by only considering the extrinsic cost (i.e., Listing's law), both costs are necessary to account for the wrist/forearm experimental data. Second, fitting the synthetic Donders' law from the simulated task with a quadratic surface yields similar fitting errors compared to experimental data.

With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a feedforward controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must first be modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. It is troublesome and costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Most literature model the actuator using a cascade of rateindependent hysteresis operators and a dynamical system. However, the inertial dynamics of the structure is not the only contributing factor. A complete model will be complex. Thus, based on the studies done on the phenomenological hysteretic behavior with rate, this paper proposes an adaptive rate-dependent feedforward controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive controller. The main contribution of this paper is its ability to perform tracking control of non-periodic motion and is illustrated with the tracking control ability of a couple of different nonperiodic waveforms which were created by passing random numbers through a low pass filter with a cutoff frequency of 20Hz.

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Tremor is defined as the involuntary rhythmic or quasi-rhythmic oscillation of a body part, resulting from alternating or simultaneous contractions of antagonistic muscle groups. While tremor may be physiological, those who have disabling pathological tremors find that performing typical activities for daily living to be physically challenging and emotionally draining. Detecting the presence of tremor and its proper identification are crucial in prescribing the appropriate therapy to lessen its deleterious physical, emotional, psychological, and social impact. While diagnosis relies heavily on clinical evaluation, pattern analysis of surface electromyogram (sEMG) signals can be a useful diagnostic aid for an objective identification of tremor types. Using sEMG system attached to several parts of the patient's body while performing several tasks, this research aims to develop a classifier system that automates the process of tremor types recognition. Finding the optimal model and its corresponding parameters is not a straightforward process. The resulting workflow, however, provides valuable information in understanding the interplay and impact of the different features and their parameters to the behavior and performance of the classifier system. The resulting model analysis helps identify the necessary locations for the placement of sEMG electrodes and relevant features that have significant impact in the process of classification. These information can help clinicians in streamlining the process of diagnosis without sacrificing its accuracy.

This work introduces a coordinate-independent method to analyse movement variability of tasks performed with hand-held tools, such as a pen or a surgical scalpel. We extend the classical uncontrolled manifold (UCM) approach by exploiting the geometry of rigid body motions, used to describe tool configurations. In particular, we analyse variability during a static pointing task with a hand-held tool, where subjects are asked to keep the tool tip in steady contact with another object. In this case the tool is redundant with respect to the task, as subjects control position/orientation of the tool, i.e. 6 degrees-of-freedom (dof), to maintain the tool tip position (3dof) steady. To test the new method, subjects performed a pointing task with and without arm support. The additional dof introduced in the unsupported condition, injecting more variability into the system, represented a resource to minimise variability in the task space via coordinated motion. The results show that all of the seven subjects channeled more variability along directions not directly affecting the task (UCM), consistent with previous literature but now shown in a coordinate-independent way. Variability in the unsupported condition was only slightly larger at the endpoint but much larger in the UCM.