The results of this research suggest that a stereoscopic system would not result in higher objective visual fatigue and cognitive workload than a 2D system, and it might reduce the performance time and increase the precision of surgical operations. In addition, learning efficiency of the stereoscopic system on the novices in this study demonstrated its value for training and education in laparoscopic surgery.
In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform’s effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human–robot interaction.
Fitts’ law predicts the human movement response time for a specific task through a simple linear formulation, in which the intercept and the slope are estimated from the task’s empirical data. This research was motivated by our pilot study, which found that the linear regression’s essential assumptions are not satisfied in the literature. Furthermore, the keystone hypothesis in Fitts’ law, namely that the movement time per response will be directly proportional to the minimum average amount of information per response demanded by the particular amplitude and target width, has never been formally tested. Therefore, in this study we developed an optional formulation by combining the findings from the fields of psychology, physics, and physiology to fulfill the statistical assumptions. An experiment was designed to test the hypothesis in Fitts’ law and to validate the proposed model. To conclude, our results indicated that movement time could be related to the index of difficulty at the same amplitude. The optional formulation accompanies the index of difficulty in Shannon form and performs the prediction better than the traditional model. Finally, a new approach to modeling movement time prediction was deduced from our research results.
Fitts’ law is used as a performance measurement metric in human–computer interactions. The original formulation implied that movement time was identical for movements with the same value of the index of difficulty under varied movement amplitude and target width. An experiment was designed to test this implication. The result indicates that movement time is related to the index of difficulty when the amplitude is constant. Nowadays, most of the icons in applications are represented as two-dimensional targets. An object of equal width and height is a particular case of a two-dimensional target. This target area could be a factor in a Fitts task and impact the movement time, number of errors, and perceived difficulty. Therefore, the area could replace the target width in the formulation of the index of difficulty. The modified index of difficulty is easy to implement without the complexity of post-calculation. Researchers can design the index of difficulty before the empirical test. This research proposes a modified index of difficulty by varying the target’s area and applying the square-root movement time model simultaneously, which results in an excellent performance with a higher R-square and satisfies the residual normality robustly than the traditional formulation of Fitts’ law.
Fitts' law predicts the human movement response time for a specific task by a simple linear formulation, in which the intercept and the slope are estimated from the task's empirical data. This research was motivated by our pilot study, which found that the linear regression's essential assumptions are not satisfied in the literature. Furthermore, the keystone hypothesis in Fitts' law, that the movement time per response will be directly proportional to the minimum average amount of information per response demanded by the particular amplitude and target width, has never been formally tested. Therefore, this study developed an optional formulation derived from fusing the findings in psychology, physics, and physiology for fulfilling the statistical assumptions. An experiment was designed to test the hypothesis in Fitts' law and validate the proposed model. To conclude, our results indicated that movement time could be related to the index of difficulty underlying the same constant amplitude. The optional formulation accompanies the index of difficulty in Shannon form robustly performs the prediction better than the traditional model across studies. Finally, a new approach to modeling movement time prediction is deduced from our research results
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