We present a new approach to address the challenges associated with maintaining the functionality of triboelectric vibration sensors in smart plywood during woodworking operations involving nails and screws. The current state-of-the-art sensor design employs non-overlapping electrodes, which unfortunately leads to significant compromises in terms of signal strength and clarity, particularly in real-world scenarios that involve electromagnetic (EM) interference. To overcome these limitations, we propose a method that enables the woodworker to manually isolate short-circuited electrodes. This method facilitates the creation of sensors using overlapping electrodes, while also incorporating EM shielding, thereby resulting in a substantial improvement in the sensor’s robustness when detecting user activities. To validate the effectiveness of our proposed approach, we conducted a series of experiments, which not only shed light on the drawbacks of non-overlapping electrode designs but also demonstrated the significant improvements achieved through our method.

Tagnoo is a computational plywood augmented with RFID tags, aimed at empowering woodworkers to effortlessly create room-scale smart environments. Unlike existing solutions, Tagnoo does not necessitate technical expertise or disrupt established woodworking routines. This battery-free and cost-effective solution seamlessly integrates computation capabilities into plywood, while preserving its original appearance and functionality. In this paper, we explore various parameters that can influence Tagnoo’s sensing performance and woodworking compatibility through a series of experiments. Additionally, we demonstrate the construction of a small office environment, comprising a desk, chair, shelf, and floor, all crafted by an experienced woodworker using conventional tools such as a table saw and screws while adhering to established construction workflows. Our evaluation confirms that the smart environment can accurately recognize 18 daily objects and user activities, such as a user sitting on the floor or a glass lunchbox placed on the desk, with over 90% accuracy.

Quality of experience (QoE) has been widely recognized as the primary metric to evaluate user experience in multimedia applications. However, the QoE assessment of tactile virtual environments is still highly dependent on subjective measures. Inspired by the fact that physiological signals can characterize the user's emotional state, we propose a QoE measurement method for virtual reality (VR) with vibrotactile feedback based on frontal lobe power asymmetry (FLPA). The subjective score of vibrotactile experience in VR is used as the ground truth of QoE. The selection of QoE measurement indicators consists of two steps. First, the relationship between FLPA phenomenon and scores of QoE is preliminarily established by statistical methods and Spearman Correlation Coefficient. Then, the most important FLPA feature is selected by random forest, which is the best indicator for QoE measurement. The brain neural images show that vibrotactile feedback in VR can evoke FLPA phenomenon. Correlation analysis shows that there is a significant correlation between subjective scores of QoE and FLPA features. The classification results show that the selected best FLPA feature can be used as a physiological indicator to measure and predict QoE. We achieve mutual interpretation of EEG-based physiological measurements and subjective cognitive outcomes of QoE.