An effective non-contact electrostatic detection method is used for human body motion detection. Theoretical analysis and pratical experiments are carried out to prove that this method is effective in the field of human body monitoring, in which a model for human body induced potential by stepping has been proposed. Furthermore, experiment results also prove that it’s feasible to measure the average velocity and route of human body motion by multiple electrodes array. What’s more the real-time velocity and direction of human body motion can be determined by orthogonal electrostatic detector array, and the real-time velocity and direction of human body motion can be obtained within the range of 2 meters.
Tuning the electromechanical performance in piezoresistive composite strain sensors is primarily attained through appropriately employing the materials system and the fabrication process. High sensitivity along with flexibility in the strain sensing devices needs to be met according to the application (e.g. human motion detection, health and sports monitoring). In this paper, a highly stretchable and sensitive strain sensor with a low-cost fabrication is proposed which is acquired by embedding the chopped carbon fibers sandwiched in between silicone rubber layers. The electrical and mechanical features of the sensor were characterized through stretch/release loading tests where a considerably high sensitivity (the gauge factor about 100) was observed with very low hysteresis. This implies high strain reversibility (i.e. full recovery in each cycle) over 700 loading cycles. Moreover, the sensors exhibited ultra-high stretchability (up to ∼300% elongation) in addition to a low stiffness meaning minimal mechanical effects induced by the sensor for sensitive human motion monitoring applications including large and small deformations. The results suggest the promising capability of the present sensor in reflecting the human body motion detection.
In this paper, we propose a new data-driven framework for 3D hand and full-body motion emotion transfer. Specifically, we formulate the motion synthesis task as an image-to-image translation problem. By presenting a motion sequence as an image representation, the emotion can be transferred by our framework using StarGAN. To evaluate our proposed method’s effectiveness, we first conducted a user study to validate the perceived emotion from the captured and synthesized hand motions. We further evaluate the synthesized hand and full body motions qualitatively and quantitatively. Experimental results show that our synthesized motions are comparable to the captured motions and those created by an existing method in terms of naturalness and visual quality.
Emotion Transfer for 3D Hand and Full Body Motion Using StarGAN