Wang Dangxiao

Soft tissue simulation with bimanual force feedback

Soft tissue simulation with bimanual force feedback is one of the main research topics toward practical application of surgery simulation. A method for the simulation of soft object using Mass-Spring model is presented to deform human tissue in the bimanual haptic feedback environment. A multi-thread simulation framework is employed to support real-time force feedback calculation and the deformation of the soft object. Haptic force is calculated based on a hybrid physical/geometric model and is filtered before sent to the haptic device in order to get stable force feedback. Two experiments are carried out to evaluate real-time performance and stability. Finally, a preliminary example of application in human tissue simulation is introduced.

Multi-modal human-machine interaction for human intelligence augmentation

Human-machine symbiosis and harmony is an ultimate goal of human-machine interaction (HMI). Recently, the rapid development of artificial intelligence has given rise to concern regarding the domination of machine intelligence over human intelligence. Enhancing human intelligence through multi-modal HMI technology is becoming an important research topic. The studies on neural plasticity indicate that the core capabilities of human cognition including attention control ability and working memory capacity could be enhanced and trained through several approaches including visual-auditory games, haptic interaction tasks, transcranial electromagnetic stimulation, and brain-computer interfaces. In this paper, we propose a systematic paradigm of augmenting cognition through HMI tasks by seamlessly integrating haptic-visual-aural multisensory feedback. This paradigm is constructed on analyzing the inherent features of virtual reality including immersion, interaction, and imagination. Based on the concept of cybernetics, cognitive enhancement methods based on Hebbian learning are proposed to achieve several goals including the realization of controllable cognitive loads, immediate physiological feedback, and bidirectional mind-body interaction. The proposed paradigm may provide new tools for revealing the mechanism of neural plasticity and promote the development of novel human-machine interaction devices; it is promising in the generation of practical values in the domain of personalized education, neural rehabilitation, and cognitive training of specialized personnel.

On-line signature verification based on three-dimensional force signals at critical points

It is an open problem to extract stable and distinguishable features from force signals of handwriting signatures. Based on the writing habits and kinematics of human, a hypothesis was proposed that force signal at critical points, e.g. the starting, the turning and the ending of a signature. etc, consists of personalized features which can distinguish signatures of different persons. Using stroke curvature, the critical points were extracted, and the 3-dimensional force signals at the critical points were also recorded. Employing Dynamic Time Warping (DTW) method, based on a preliminary signature database, verification experiments were carried out using the force signals of a whole stroke and the force signal at the critical points of the stroke respectively. The results look encouraging to verify the hypothesis.