Spasticity is a common complication for patients with stroke, but only few studies investigate the relation between spasticity and voluntary movement. This study proposed a novel automatic system for assessing the severity of spasticity (SS) of four upper-limb joints, including the elbow, wrist, thumb, and fingers, through voluntary movements. A wearable system which combined 19 inertial measurement units and a pressure ball was proposed to collect the kinematic and force information when the participants perform four tasks, namely cone stacking (CS), fast flexion and extension (FFE), slow ball squeezing (SBS), and fast ball squeezing (FBS). Several time and frequency domain features were extracted from the collected data, and two feature selection approaches based on recursive feature elimination were adopted to select the most influential features. The selected features were input into five machine learning techniques for assessing the SS for each joint. The results indicated that using CS task to assess the SS of elbow and fingers and using FBS task to assess the SS of thumb and wrist can reach the highest weighted-average F1-score. Furthermore, the study also concluded that FBS is the optimal task for assessing all the four upper-limb joints. The overall result shown that the proposed automatic system can assess four upper-limb joints through voluntary movements accurately, which is a breakthrough of finding the relation between spasticity and voluntary movement.

Soft-decision adaptive interpolation (SAI) provides a powerful result in preserving edge structures for interpolation from low-resolution images to obtain high-resolution images. However, the SAI algorithm may produce artifacts in the smooth regions and texture patterns of the interpolated images. To improve the SAI algorithm, we propose an evolutionary game, where every pixel is regarded as a player. The players are divided into two roles, one for unknown high-resolution pixels and the other for known low-resolution pixels. Each role has a different strategy set. The evolutionarily stable strategy in each local image region is a mixed strategy adopted by every player. By considering the mixed strategy as weights for interpolation, we can adaptively estimate the high-resolution image. Experimental results show that the proposed algorithm improves the SAI algorithm by alleviating the artifacts both in PSNR and visual quality.

Dominant sets are a new graph partition method for pairwise data clustering proposed by Pavan and Pelillo. We address the problem of dominant sets with a coalitional game model, in which each data point is treated as a player and similar data points are encouraged to group together for cooperation. We propose betrayal and hermit rules to describe the cooperative behaviors among the players. After applying the betrayal and hermit rules, an optimal and stable graph partition emerges, and all the players in the partition will not change their groups. For computational feasibility, we design an approximate algorithm for finding a dominant set of mutually similar players and then apply the algorithm to an application such as image denoising. In image denoising, every pixel is treated as a player who seeks similar partners according to its patch appearance in its local neighborhood. By averaging the noisy effects with the similar pixels in the dominant sets, we improve nonlocal means image denoising to restore the intrinsic structure of the original images and achieve competitive denoising results with the state-of-the-art methods in visual and quantitative qualities.

In this paper, we present a temporal-state shape context (TSSC) method that exploits space-time shape variations for human action recognition. In our method, the silhouettes of objects in a video clip are organized into three temporal states. These states are defined by fuzzy time intervals, which can lessen the degradation of recognition performance caused by time warping effects. The TSSC features capture local characteristics of the space-time shape induced by consecutive changes of silhouettes. Experimental results show that our method is effective for human action recognition, and is reliable when there are various kinds of deformations. Moreover, our method can identify spatially inconsistent parts between two shapes of the actions, which could be useful in action analysis applications.