RESUMEN
Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool to enable researchers to conveniently explore and annotate multi-day high-sampling rate raw accelerometer data. The tool visualizes high-sampling-rate raw data and time-stamped annotations generated by existing activity recognition algorithms and human annotators; the annotations can then be directly modified by the researchers to create their own, improved, annotated datasets. In this paper, we describe the tool's features and implementation that facilitate convenient exploration and annotation of multi-day data and demonstrate its use in generating activity annotations.
RESUMEN
Many studies have already shown that games can be a useful tool to make boring or difficult tasks more engaging. However, with serious game design being a relatively nascent field, such experiences can still be hard to learn and not very motivating. In this paper, we explore the use of learning and motivation frameworks to improve player experience in the well-known citizen science game Foldit. Using Cognitive Load Theory (CLT) and Self Determination Theory (SDT), we developed six interface and mechanical changes to the tutorial levels in Foldit designed to increase engagement and retention. We tested these features with new players of Foldit and collected both behavioral data, using game metrics, and prior experience data, using self-report measures. This study offers three major contributions: (1) we document the process of operationalizing CLT and SDT as new game features, a unique methodology not used in game design previously; (2) the user interface, specifically the level selection screen, significantly impacts how players progress through the game; and (3) a player's expertise, whether from prior domain knowledge or prior gaming experience, increases their engagement. We discuss both implications of these findings as well as how these implementations can generalize to other designs.
RESUMEN
Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces and interventions. However, developing valid algorithms that use accelerometer data to detect everyday activities often requires large amounts of training datasets, precisely labeled with the start and end times of the activities of interest. Acquiring annotated data is challenging and time-consuming. Applied games, such as human computation games (HCGs) have been used to annotate images, sounds, and videos to support advances in machine learning using the collective effort of "non-expert game players." However, their potential to annotate accelerometer data has not been formally explored. In this paper, we present two proof-of-concept, web-based HCGs aimed at enabling game players to annotate accelerometer data. Using results from pilot studies with Amazon Mechanical Turk players, we discuss key challenges, opportunities, and, more generally, the potential of using applied videogames for annotating raw accelerometer data to support activity recognition research.
RESUMEN
Visualization is a valuable tool in problem solving, especially for citizen science games. In this study, we analyze data from 36,351 unique players of the citizen science game Foldit over a period of 5 years to understand how their choice of visualization options are affected by expertise and problem type. We identified clusters of visualization options, and found differences in how experts and novices view puzzles and that experts differentially change their views based on puzzle type. These results can inform new design approaches to help both novice and expert players visualize novel problems, develop expertise, and problem solve.
RESUMEN
In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots-automated player simulations based on models of players with varying sets of skills-to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
