Becoming a Rapid Shooter in a Game Using Embodied Electrical Muscle Stimulation: Development and Usability Study
Bibliographic record
Abstract
Background: Electrical muscle stimulation (EMS) systems enhance human capabilities, such as reaction time, by inducing preemptive muscle contractions. One of the key challenges for EMS applications is preserving the user's sense of agency, and it is defined as a subjective experience of initiating and controlling one's actions. Prior research highlights the importance of the "sweet spot"-a balance between sense of agency and preemptive gain-for effective EMS use. However, most prior studies have focused on simplistic tasks, leaving a gap in understanding how the sweet spot functions in complex and ecological game scenarios. Moreover, the potential benefits of a personalized approach were not explored. Objective: This study examines the effects of embodied EMS on performance and sense of agency in a serious-games-based pistol-shooting scenario. Additionally, we investigated the effects of personalization in the same game scenario. Methods: Two studies were conducted. Study 1 identified the optimal EMS timing ("sweet spot") to improve response time while preserving agency. A total of 13 participants completed a visual stimulus-response task after EMS calibration. Participants performed 150 right-button clicks on a target using an EMS-equipped mouse, with EMS timings ranging from -200 to +100 ms. An agency questionnaire followed each trial. Logistic regression was used to identify the sweet spot. Study 2 applied the findings of Study 1 to a pistol-shooting game. A total of 10 new participants were recruited. Before gameplay, individual sweet spots were measured for the individually embodied EMS condition. During the game, participants navigated to a target location, distinguished enemies from hostages, and shot enemies. After a practice session, participants completed the game under 4 counterbalanced conditions: averagely embodied EMS (used the average sweet spot value from Study 1), individually embodied EMS (used each participant's own sweet spot value), immediate EMS (stimulation at a target onset timing), and no EMS. Results: Study 1 identified a sweet spot that significantly improved reaction time while preserving agency. Logistic analysis showed an average sweet spot of 46.8 ms and individual variability across participants. Study 2 confirmed these findings in a pistol-shooting game. Both averagely and individually embodied EMS significantly reduced reaction times compared to no EMS (P=.03 and P=.001, respectively), without compromising agency. Individually embodied EMS yielded additional benefits. In the slower than average group, the sense of agency in the individually embodied EMS condition showed an additional benefit that was not observed in the averagely embodied EMS condition (P=.003 and P=.095, respectively). Conclusions: The findings indicate that embodied EMS enhances performance in complex game scenarios while maintaining users' sense of agency, with individualization yielding additional benefits. These results extend prior evidence to more complex game contexts and provide valuable implications for the personalized design of EMS systems in sports training, rehabilitation, and serious gaming.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".