A computational approach to gestural interactions of the upper limb on planar surfaces

There are many compelling reasons for proposing new gestural interactions: one might want to use a novel sensor that affords access to data that couldn’t be previously captured, or transpose a well-known task into a different unexplored scenario. After an initial design phase, the creation, optimisation or understanding of new interactions remains, however, a challenge. Models have been used to foresee interaction properties: Fitts’ law, for example, accurately predicts movement time in pointing and steering tasks. But what happens when no existing models apply? The core assertion to this work is that a computational approach provides frameworks and associated tools that are needed to model such interactions. This is supported through three research projects, in which discriminative models are used to enable interactions, optimisation is included as an integral part of their design and reinforcement learning is used to explore motions users produce in such interactions

A study on interaction-driven comparison between analog and digital gaming control interface on smartphone

This study aims to find empirical evidence of effectiveness levels: comfort, efficiency, and accuracy between analog and digital interface on smartphone game control through the two different usability tests: 1) A Pilot Study for measuring a correlation contrast with direct and indirect input control from six participants in a small group; 2) A Main Study for finding the effectiveness of “Tap-only affords” basis between a digital and analog input control. The usability test was analyzed by both qualitative and quantitative research methods. There was a total of the 81 participants who were divided into two big groups for comparing one hand and two hands input control, and nine participants per each group implemented a smartphone game based on different input control tasks. The findings appear that direct touch screen interaction is more effective on two hands input control tasks while using an indirect physical input control was more effective on one hand touchscreen

Exploring the Front Touch Interface for Virtual Reality Headsets

In this paper, we propose a new interface for virtual reality headset: a touchpad in front of the headset. To demonstrate the feasibility of the front touch interface, we built a prototype device, explored VR UI design space expansion, and performed various user studies. We started with preliminary tests to see how intuitively and accurately people can interact with the front touchpad. Then, we further experimented various user interfaces such as a binary selection, a typical menu layout, and a keyboard. Two-Finger and Drag-n-Tap were also explored to find the appropriate selection technique. As a low-cost, light-weight, and in low power budget technology, a touch sensor can make an ideal interface for mobile headset. Also, front touch area can be large enough to allow wide range of interaction types such as multi-finger interactions. With this novel front touch interface, we paved a way to new virtual reality interaction methods

MilliSonic: Pushing the Limits of Acoustic Motion Tracking

Recent years have seen interest in device tracking and localization using acoustic signals. State-of-the-art acoustic motion tracking systems however do not achieve millimeter accuracy and require large separation between microphones and speakers, and as a result, do not meet the requirements for many VR/AR applications. Further, tracking multiple concurrent acoustic transmissions from VR devices today requires sacrificing accuracy or frame rate. We present MilliSonic, a novel system that pushes the limits of acoustic based motion tracking. Our core contribution is a novel localization algorithm that can provably achieve sub-millimeter 1D tracking accuracy in the presence of multipath, while using only a single beacon with a small 4-microphone array.Further, MilliSonic enables concurrent tracking of up to four smartphones without reducing frame rate or accuracy. Our evaluation shows that MilliSonic achieves 0.7mm median 1D accuracy and a 2.6mm median 3D accuracy for smartphones, which is 5x more accurate than state-of-the-art systems. MilliSonic enables two previously infeasible interaction applications: a) 3D tracking of VR headsets using the smartphone as a beacon and b) fine-grained 3D tracking for the Google Cardboard VR system using a small microphone array

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion