Usability Evaluation Methods for Gesture-Based Games: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Gestural interaction systems are increasingly being used, mainly in games, expanding the idea of entertainment and providing experiences with the purpose of promoting better physical and/or mental health. Therefore, it is necessary to establish mechanisms for evaluating the usability of these interfaces, which make gestures the basis of interaction, to achieve a balance between functionality and ease of use. OBJECTIVE: This study aims to present the results of a systematic review focused on usability evaluation methods for gesture-based games, considering devices with motion-sensing capability. We considered the usability methods used, the common interface issues, and the strategies adopted to build good gesture-based games. METHODS: The research was centered on four electronic databases: IEEE, Association for Computing Machinery (ACM), Springer, and Science Direct from September 4 to 21, 2015. Within 1427 studies evaluated, 10 matched the eligibility criteria. As a requirement, we considered studies about gesture-based games, Kinect and/or Wii as devices, and the use of a usability method to evaluate the user interface. RESULTS: In the 10 studies found, there was no standardization in the methods because they considered diverse analysis variables. Heterogeneously, authors used different instruments to evaluate gesture-based interfaces and no default approach was proposed. Questionnaires were the most used instruments (70%, 7/10), followed by interviews (30%, 3/10), and observation and video recording (20%, 2/10). Moreover, 60% (6/10) of the studies used gesture-based serious games to evaluate the performance of elderly participants in rehabilitation tasks. This highlights the need for creating an evaluation protocol for older adults to provide a user-friendly interface according to the user's age and limitations. CONCLUSIONS: Through this study, we conclude this field is in need of a usability evaluation method for serious games, especially games for older adults, and that the definition of a methodology and a test protocol may offer the user more comfort, welfare, and confidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".