Mid-air arm movements are ubiquitous in VR, AR, and gestural interfaces. While mouse movements have received some attention, the dynamics of mid-air movements are understudied in HCI. In this paper we present an exploratory analysis of the dynamics of aimed mid-air movements. We explore the 3rd order lag (3OL) and existing 2nd order lag (2OL) models for modeling these dynamics. For a majority of movements the 3OL model captures mid-air dynamics better, in particular acceleration. The models can effectively predict the complete time series of position, velocity and acceleration of aimed movements given an initial state and a target using three (2OL) or four (3OL) free parameters.

We present the Levitation Simulator, a system that enables researchers and designers to iteratively develop and prototype levitation interface ideas in Virtual Reality. This includes user tests and formal experiments. We derive a model of the movement of a levitating particle in such an interface. Based on this, we develop an interactive simulation of the levitation interface in VR, which exhibits the dynamical properties of the real interface. The results of a Fitts' Law pointing study show that the Levitation Simulator enables performance, comparable to the real prototype. We developed the first two interactive games, dedicated for levitation interfaces: LeviShooter and BeadBounce, in the Levitation Simulator, and then implemented them on the real interface. Our results indicate that participants experienced similar levels of user engagement when playing the games, in the two environments. We share our Levitation Simulator as Open Source, thereby democratizing levitation research, without the need for a levitation apparatus.

Throwing is a fundamental movement in many sports and games. Given this, accurate throwing in VR applications today is surprisingly difficult. In this paper we explore the nature of the difficulties of throwing in VR in more detail. We present the results of a user study comparing throwing in VR and in the physical world. In a short pre-study with 3 participants we determine an optimal number of throwing repetitions for the main study by exploring the learning curve and subjective fatigue of throwing in VR. In the main study, with 12 participants, we find that throwing precision and accuracy in VR are lower particularly in the distance and height dimensions. It also requires more effort and exhibits different kinematic patterns.

We present LeviCursor, a method for interactively moving a physical, levitating particle in 3D with high agility. The levitating object can move continuously and smoothly in any direction. We optimize the transducer phases for each possible levitation point independently. Using precomputation, our system can determine the optimal transducer phases within a few microseconds and achieves round-trip latencies of 15 ms. Due to our interpolation scheme, the levitated object can be controlled almost instantaneously with sub-millimeter accuracy. We present a particle stabilization mechanism which ensures that the levitating particle is always in the main levitation trap. Lastly, we conduct the first Fitts' law-type pointing study with a real 3D cursor, where participants control the movement of the levitated cursor between two physical targets. The results of the user study demonstrate that using LeviCursor, users reach performance comparable to that of a mouse pointer.

Only time will tell if motion-controlled systems are the future of gaming and other industries and if mid-air gestural input will eventually offer a more intuitive way to play games and interact with computers. Whatever the eventual outcome, it is necessary to assess the ergonomics of mid-air input metaphors and propose design guidelines which will guarantee their safe use in the long run. This paper presents an ergonomic study showing how to mitigate the muscular strain induced by prolonged mid-air gesture interaction by encouraging postural shifts during the interaction. A quantitative and qualitative user study involving 30 subjects validates the setup. The simulated musculo-skeletal load values support our hypothesis and show a statistically significant 19% decrease in average muscle loads on the shoulder, neck, and back area in the modified condition compared to the baseline.

Although different types of touch surfaces have gained extensive attention in HCI, this is the first work to directly compare them for two critical factors: performance and ergonomics. Our data come from a pointing task (N=40) carried out on five common touch surface types: public display (large, vertical, standing), tabletop (large, horizontal, seated), laptop (medium, adjustably tilted, seated), tablet (seated, in hand), and smartphone (single- and two-handed input). Ergonomics indices were calculated from biomechanical simulations of motion capture data combined with recordings of external forces. We provide an extensive dataset for researchers and report the first analyses of similarities and differences that are attributable to the different postures and movement ranges.

This article presents a novel summarization of biomechanical and performance data for user interface designers. Previously, there was no simple way for designers to predict how the location, direction, velocity, precision, or amplitude of users’ movement affects performance and fatigue. We cluster muscle coactivation data from a 3D pointing task covering the whole reachable space of the arm. We identify 11 clusters of pointing movements with distinct muscular, spatio-temporal, and performance properties. We discuss their use as heuristics when designing for 3D pointing.

Parallel Coordinates is an often used visualization method for multidimensional data sets. Its main challenges for large data sets are visual clutter and over plotting which hamper the recognition of patterns in the data. We present an edge-bundling method using density-based clustering for each dimension. This reduces clutter and provides a faster overview of clusters and trends. Moreover, it allows rendering the clustered lines using polygons, decreasing rendering time remarkably. In addition, we design interactions to support multidimensional clustering with this method. A user study shows improvements over the classic parallel coordinates plot in two user tasks: correlation estimation and subset tracing.

Motion-capture-based biomechanical simulation is a non-invasive analysis method that yields a rich description of posture, joint, and muscle activity in human movement. The method is presently gaining ground in sports, medicine, and industrial ergonomics, but it also bears great potential for studies in HCI where the physical ergonomics of a design is important. To make the method more broadly accessible, we study its predictive validity for movements and users typical to studies in HCI. We discuss the sources of error in biomechanical simulation and present results from two validation studies conducted with a state-of-the-art system. Study I tested aimed movements ranging from multitouch gestures to dancing, finding out that the critical limiting factor is the size of movement. Study II compared muscle activation predictions to surface-EMG recordings in a 3D pointing task. The data shows medium-to-high validity that is, however, constrained by some characteristics of the movement and the user. We draw concrete recommendations to practitioners and discuss challenges to developing the method further.

In Human-Computer Interaction (HCI), experts seek to evaluate and compare the performance and ergonomics of user interfaces. Recently, a novel cost-efficient method for estimating physical ergonomics and performance has been introduced to HCI. It is based on optical motion capture and biomechanical simulation. It provides a rich source for analyzing human movements summarized in a multidimensional data set. Existing visualization tools do not sufficiently support the HCI experts in analyzing this data. We identified two shortcomings. First, appropriate visual encodings are missing particularly for the biomechanical aspects of the data. Second, the physical setup of the user interface cannot be incorporated explicitly into existing tools. We present MovExp, a versatile visualization tool that supports the evaluation of user interfaces. In particular, it can be easily adapted by the HCI experts to include the physical setup that is being evaluated, and visualize the data on top of it. Furthermore, it provides a variety of visual encodings to communicate muscular loads, movement directions, and other specifics of HCI studies that employ motion capture and biomechanical simulation. In this design study, we follow a problem-driven research approach. Based on a formalization of the visualization needs and the data structure, we formulate technical requirements for the visualization tool and present novel solutions to the analysis needs of the HCI experts. We show the utility of our tool with four case studies from the daily work of our HCI experts.

We study the design of split keyboards for fast text entry with two thumbs on mobile touchscreen devices. The layout of KALQ was determined through first studying how users should grip a device with two hands. We then assigned letters to keys computationally, using a model of two-thumb tapping. KALQ minimizes thumb travel distance and maximizes alternation between thumbs. An error correction algorithm was added to help address linguistic and motor errors. Users reached a rate of 37 words per minute (with a 5% error rate) after a training program.