FlexStylus, a flexible stylus, detects deformation of the barrel as a vector with both a rotational and an absolute value, providing two degrees of freedom with the goal of improving the expressivity of digital art using a stylus device. We outline the construction of the prototype and the principles behind the sensing method, which uses a cluster of four fibreoptic based deformation sensors. We propose interaction techniques using the FlexStylus to improve menu navigation and tool selection. Finally, we describe a study comparing users' ability to match a changing target value using a commercial pressure stylus and the FlexStylus' absolute deformation. When using the FlexStylus, users had a significantly higher accuracy overall. This suggests that deformation may be a useful input method for future work considering stylus augmentation.
Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyperparameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multilayer perceptron networks.
While a number of literature reviews have been published in recent times on the applications of optical fibre sensors in smart structures research, these have mainly focused on the use of conventional glass-based fibres. The availability of inexpensive, rugged, and large-core plastic-based optical fibres has resulted in growing interest amongst researchers in their use as low-cost sensors in a variety of areas including chemical sensing, biomedicine, and the measurement of a range of physical parameters. The sensing principles used in plastic optical fibres are often similar to those developed in glass-based fibres, but the advantages associated with plastic fibres render them attractive as an alternative to conventional glass fibres, and their ability to detect and measure physical parameters such as strain, stress, load, temperature, displacement, and pressure makes them suitable for structural health monitoring (SHM) applications. Increasingly their applications as sensors in the field of structural engineering are being studied and reported in literature. This article will provide a concise review of the applications of plastic optical fibre sensors for monitoring the integrity of engineering structures in the context of SHM.
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