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Researchers have made a lot of effort for the lightweight and high flexibility of wearable electronic devices, which also requires the associated energy harvesting equipment to have ultra-thin thickness and high stretchability. Therefore, a piezoelectric-triboelectric hybrid self-powered sensor (PTHS) has been proposed which can be used as the second layer of the human body. This elastic PTHS can even work on a person’s fingers without disturbing the body’s movements. The open circuit voltage and short circuit current of devices with a projected area of 30 mm × 25 mm can reach 1.2 V and 30 nA, respectively. Two piezoelectrically-triboelectrically sensors with machine learning optimized identification strategies were experimentally proven as the potential applications of the PTHS. The PTHS’s ultra-thin thickness, high stretchability and superior geometry control features are promising in electronic skin, artificial muscle and soft robotics. The novelty of this work is that a smart mask integrated with PTHS can generate a signal of the hybrid sensor for the biomechanical motion classifier. After suitable training, an overall accuracy of 87.9% using long short-term memory can be achieved. [ABSTRACT FROM AUTHOR]

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user's head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user's head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems. [ABSTRACT FROM AUTHOR]