PACMHCI V5, ISS, November 2021 Editorial

It is our great pleasure to welcome you to this issue of the Proceedings of the ACM on Human-Computer Interaction, the second to focus on the contributions from the research community Interactive Surfaces and Spaces (ISS). Interactive Surfaces and Spaces increasingly pervade our everyday life, appearing in various sizes, shapes, and application contexts, offering a rich variety of ways to interact. This diverse research community explores the design, development, and use of new and emerging interactive surface technologies and interactive spaces. The call for articles for this issue on ISS attracted 77 submissions, from all over the world. This issue has 23 papers, 4 submitted in February 2021 and 19 submitted in July 2021. After the winter round, 4 (total of 19 articles, 21.1%) articles were accepted and 5 (26.3%) articles required major revisions. After the summer round, 19 (total of 58 articles, 32.8%) articles were accepted, and 18 (31,0%) articles required major revisions. The editorial committee worked hard over the two iterations of the review process, winter and summer rounds, to arrive at final decisions. In total, counting both the winter and the summer rounds, 23 articles (total of 77 articles, 29.9%) were accepted. All authors of the accepted articles are invited to present at the ISS conference from November 14--17, 2021. This issue exists because of the dedicated volunteer effort of 31 senior editors who served as Associate Chairs (ACs), 105 expert reviewers in the winter round, and 206 expert reviewers in the summer round to ensure high quality and insightful reviews for all articles. Reviewers and committee members were kept constant for papers that submitted to both rounds. The Editorial Board is presented here: https://iss.acm.org/2021/organization/editorial_board

Analysis and design for interactive surface environments

In the research space of interactive surface environments, toolkits have a central role in rapid prototyping. They simplify operating both hardware and software technologies. However, the accelerated development of these technologies discontinues the usability of toolkits, in some cases making toolkits obsolete. One approach to address this challenge is establishing future-proof hardware and software interfaces based on the study of prevailing interactive surface environments. In this paper, we study interactive surface implementations and proposes a metamodeling infrastructure to support the analysis of prevailing implementations toward designing future-proof hardware and software modeling constructs. Our approach employs the unified modeling language and emphasizes the flexible description of existing systems. To evaluate the proposed approach, six existing research prototypes have been used to conduct traces, and the consistency demonstrated is promising. A face validation study with experts has also been conducted. Expert perceptions from the face validation study suggest potential benefit in using the UML-based approach as a shared notation for studying interactive surface environments.

An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model

Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB’s toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.