Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.

3D protein structures can be analyzed using a distance matrix calculated as the pairwise distance between all Cα atoms in the protein model. Although researchers have efficiently used distance matrices to classify proteins and find homologous proteins, much less work has been done on quantitative analysis of distance matrix features. Therefore, the distance matrix was analyzed as gray scale image using KAZE feature extractor algorithm with Bag of Visual Words model. In this study, each protein was represented as a histogram of visual codewords. The analysis showed that a very small number of codewords (~1%) have a high relative frequency (> 0.25) and that the majority of codewords have a relative frequency around 0.05. We have also shown that there is a relationship between the frequency of codewords and the position of the features in a distance matrix. The codewords that are more frequent are located closer to the main diagonal. Less frequent codewords, on the other hand, are located in the corners of the distance matrix, far from the main diagonal. Moreover, the analysis showed a correlation between the number of unique codewords and the 3D repeats in the protein structure. The solenoid and tandem repeats proteins have a significantly lower number of unique codewords than the globular proteins. Finally, the codeword histograms and Support Vector Machine (SVM) classifier were used to classify solenoid and globular proteins. The result showed that the SVM classifier fed with codeword histograms correctly classified 352 out of 354 proteins.

Gesture recognition with miniaturised radar sensors has received increasing attention as a novel interaction medium. The practical use of radar technology, however, often requires sensing through materials. Yet, it is still not well understood how the internal structure of materials impacts recognition performance. To tackle this challenge, we collected a large dataset of 14,090 radar recordings for 6 paradigmatic gesture classes sensed through a variety of everyday materials, performed by humans (6 materials) and a robot system (75 materials). Next, we developed a hybrid CNN+LSTM deep learning model and derived a robust indirect method to measure signal distortions, which we used to compile a comprehensive catalogue of materials for radar-based interaction. Among other findings, our experiments show that it is possible to estimate how different materials would affect gesture recognition performance of arbitrary classifiers by selecting just 3 reference materials. Our catalogue, software, models, data collection platform, and labeled datasets are publicly available.

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This article explores video-viewing behavior when videos are wrapped in interactive content in the case of iOtok, a 13-episodes web documentary series. The interaction and viewing data were collected over a period of one year, providing a dataset of more than 12,200 total video views by 6000 users. Standard metrics (video views, percentage viewed, number of sessions) show higher active participation for registered users compared to unregistered users. Results also indicate that serialization over multiple weeks is an effective strategy for audience building over a long period of time without negatively affecting video views. In viewing behavior analysis, we focused on three perspectives: (i) regularity (watching on a weekly basis or not), (ii) intensity (number of videos per session), and (iii) order of watching. We performed a perspective based and combined perspectives analysis involving manual coding techniques, rule-based, and k-means clustering algorithms to reveal different user profiles (intermittent, exemplary, detached, enthusiastic users, and nibblers) and highlight further viewing behavior differences (e.g., post-series users binge-watched more than concurrent users during first 13 weeks while the series was weekly released). We discuss how these results can be used to inform the design and promotion of future web documentaries.