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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.157 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".