Abstract:
With the rapid advancement of haptic feedback technology for stylus pens, it has become increasingly important to accurately measure and evaluate the fidelity of haptic f...Show MoreMetadata
Abstract:
With the rapid advancement of haptic feedback technology for stylus pens, it has become increasingly important to accurately measure and evaluate the fidelity of haptic feedback. In this article, we propose a deep metric network framework based on triplet convolutional neural network (Triplet-CNN) to compare the haptic feedback fidelity of styluses. Unlike commonly used objective assessment methods, which provide a score for a single test sample, our approach focuses on comparing the haptic feedback fidelity between different styluses. This is because individuals tend to instinctively compare different options rather than assigning an absolute score to a single test sample. Our method can predict the proportion of users who consider one of the evaluated writing instruments to be more similar to the reference writing instrument than the other based on the vibrotactile signals generated during the pen-surface interaction. To train our network model, we collected acceleration data of pen-surface interactions at different writing speeds and pressures as inputs to the model. We then conducted pairwise comparative subjective experiments to construct a subjective assessment dataset as model outputs. To evaluate the performance of our model, we conducted cross-validation experiments. We used the correlation coefficient between subjective and objective assessment, mean absolute error (MAE), and prediction accuracy within a tolerance error as metrics to measure the consistency between the model’s predictions and the subjective assessment results. The experimental results demonstrate that our model’s predictions align more closely with subjective assessment than traditional signal similarity-based assessment metrics.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 73)