Perttu Hämäläinen

Using peer tutoring in evaluating the usability of a physically interactive computer game with children

Abstract This paper presents a novel approach to usability evaluation with children called peer tutoring. Peer tutoring means that children teach other children to use the software that is evaluated. The basic philosophy behind this is to view software as a part of childs play, so that the teaching process is analogous to explaining the rules of a game such as hide and seek. If the software is easy to teach and learn, it is more likely that the amount of users increases in a social setting such as a school. The peer tutoring approach provides information about teachability and learnability of software and it also promotes communication in the test situation, compared to a test person communicating with an adult instructor. The approach has been applied to the development of a perceptually interactive user interface in QuiQuis Giant Bounce, a physically and vocally interactive computer game for 4–9 year old children. The results and experiences of using peer tutoring are promising and it has proved to be effective in detecting usability flaws and in improving the design of the game.

Martial arts in artificial reality

This paper presents Kick Ass Kung-Fu, a martial arts game installation where the player fights virtual enemies with kicks and punches as well as acrobatic moves such as cartwheels. Using real-time image processing and computer vision, the video image of the user is embedded inside 3D graphics. Compared to previous work, our system uses a profile view and two displays, which allows an improved view of many martial arts techniques. We also explore exaggerated motion and dynamic slow-motion effects to transform the aesthetic of kung-fu movies into an interactive, embodied experience. The system is described and analyzed based on results from testing the game in a theater, in a television show, and in a user study with 46 martial arts practitioners.

Using heart rate to control an interactive game

This paper presents a novel way of using real-time heart rate information to control a physically interactive biathlon (skiing and shooting) computer game. Instead of interfacing the game to an exercise bike or other equipment with speed output, the skiing speed is directly proportional to heart rate. You can freely choose the form of physical exercise, which makes it easier for people with different skill levels and backgrounds to play together. The system can be used with any exercise machine or form. To make playing meaningful instead of simply exercising as hard as you can, a high heart rate impedes the shooting part of the game by making the sight less steady. This balancing mechanism lets the player try out different tactics, varying from very slow skiing and sharp shooting to fast skiing and random shooting. The game has been evaluated in a user study with eight participants. The results show that heart rate interaction is fun and usable interaction method.

Wizard of Oz prototyping of computer vision based action games for children

This paper describes the use of the Wizard of Oz (WOz) method in the design of computer vision based action games controlled with body movements. A WOz study was carried out with 34 children of ages 7 to 9 in order to find out the most intuitive movements for game controls and to evaluate the relationship between avatar and player actions. Our study extends the previous Wizard of Oz studies by showing that WOz prototyping of perceptive action games is feasible despite the delay caused by the wizard. The results also show that distinctive movement categories and gesture patterns can be found by observing the children playing games controlled by a human wizard. The approach minimizes the need for fully functional prototypes in the early stages of the design and provides video material for testing and developing computer vision algorithms, as well as guidelines for animating the game character.

Children's intuitive gestures in vision-based action games

Novel computer vision-based game technologies aim to give players more immersive and physically challenging gaming experiences.

Augmented climbing: interacting with projected graphics on a climbing wall

This paper describes our efforts in developing a novel augmented climbing wall. Our system combines projected graphics on an artificial climbing wall and body tracking using computer vision technology. The system is intended for accelerating motor skill learning or to make monotonous parts of the training fun by adding relevant goals and encouraging social collaboration. We describe six initial prototypes and the feedback obtained from testing them with intermediate and experienced climbers.

Online control of simulated humanoids using particle belief propagation

We present a novel, general-purpose Model-Predictive Control (MPC) algorithm that we call Control Particle Belief Propagation (C-PBP). C-PBP combines multimodal, gradient-free sampling and a Markov Random Field factorization to effectively perform simultaneous path finding and smoothing in high-dimensional spaces. We demonstrate the method in online synthesis of interactive and physically valid humanoid movements, including balancing, recovery from both small and extreme disturbances, reaching, balancing on a ball, juggling a ball, and fully steerable locomotion in an environment with obstacles. Such a large repertoire of movements has not been demonstrated before at interactive frame rates, especially considering that all our movement emerges from simple cost functions. Furthermore, we abstain from using any precomputation to train a control policy offline, reference data such as motion capture clips, or state machines that break the movements down into more manageable subtasks. Operating under these conditions enables rapid and convenient iteration when designing the cost functions.

The Augmented Climbing Wall: High-Exertion Proximity Interaction on a Wall-Sized Interactive Surface

We present the design and evaluation of the Augmented Climbing Wall (ACW). The system combines computer vision and interactive projected graphics for motivating and instructing indoor wall climbing. We have installed the system in a commercial climbing center, where it has been successfully used by hundreds of climbers, including both children and adults. Our primary contribution is a novel movement-based game system that can inform the design of future games and augmented sports. We evaluate ACW based on three user studies (N=50, N=10, N=10) and further observations and interviews. We highlight three central themes of how digital augmentation can contribute to a sport: increasing diversity of movement and challenges, enabling user-created content in an otherwise risky environment, and enabling procedurally generated content. We further discuss how ACW represents an underexplored class of interactive systems, i.e., proximity interaction on wall-sized interactive surfaces, which presents novel human-computer interaction challenges.

A computer vision and hearing based user interface for a computer game for children

This paper describes the design of a perceptual user interface for controlling a flying cartoon-animated dragon in QuiQuis Giant Bounce, a physically and vocally interactive computer game for 4 to 9 years old children. The dragon mimics the users movements and breathes fire when the user shouts. The game works on a PC computer equipped with practically any low-cost microphone and webcam. It is targeted for uncontrolled real-life environments such as homes and schools.

Online motion synthesis using sequential Monte Carlo

We present a Model-Predictive Control (MPC) system for online synthesis of interactive and physically valid character motion. Our system enables a complex (36-DOF) 3D human character model to balance in a given pose, dodge projectiles, and improvise a get up strategy if forced to lose balance, all in a dynamic and unpredictable environment. Such contact-rich, predictive and reactive motions have previously only been generated offline or using a handcrafted state machine or a dataset of reference motions, which our system does not require. For each animation frame, our system generates trajectories of character control parameters for the near future --- a few seconds --- using Sequential Monte Carlo sampling. Our main technical contribution is a multimodal, tree-based sampler that simultaneously explores multiple different near-term control strategies represented as parameter splines. The strategies represented by each sample are evaluated in parallel using a causal physics engine. The best strategy, as determined by an objective function measuring goal achievement, fluidity of motion, etc., is used as the control signal for the current frame, but maintaining multiple hypotheses is crucial for adapting to dynamically changing environments.