Computing Touch-Point Ambiguity on Mobile Touchscreens for Modeling Target Selection Times

Finger-Fitts law (FFitts law) is a model to predict touch-pointing times, modified from Fitts' law. It considers the absolute touch-point precision, or a finger tremor factor σa, to decrease the admissible target area and thus increase the task difficulty. Among choices such as running an independent task or performing parameter optimization, there is no consensus on the best methodology to measure σa. This inconsistency could be detrimental to HCI studies such as pointing technique evaluations and user group comparisons. By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute σa. We found that the parameter optimization method is a suboptimal choice for predicting the performance.

Rancang Bangun Perangkat Keras Penunjuk Menggunakan Lengan Atas dengan Konektivitas Bluetooth

Perangkat penunjuk memberikan bantuan dalam pengoperasian komputer. Perancangan perangkat mouse menggunakan lengan atas bertujuan untuk membantu penyandang tunadaksa untuk mengoperasikan komputer dengan lebih baik. Prototipe mouse perlu dilengkapi dengan fasilitas klik untuk menggantikan tombol klik mouse. Penelitian ini bertujuan untuk meneliti mana yang lebih baik penggunaan tombol klik di kaki kiri (prototipe A) atau kaki kanan (prototipe B). Sedangkan penggerak kursor sendiri menggunakan sensor di lengan atas. Penelitian dilakukan dengan menggunakan metode penilaian Fitts’ Law yang diterapkan oleh ISO 9241-411, pengujian dilakukan dengan 9 subjek non tunadaksa. Subjek melakukan pengujian dengan menggunakan perangkat lunak untuk melakukan pengujian multi-directional tapping sesuai standar ISO 9241-411. Pengisian kuesioner untuk memperoleh data kualitatif dilakukan setelah pengujian menggunakan perangkat lunak. Penyaringan data dilakukan untuk menghilangkan galat, dan ketika data telah siap, dilakukan pengujian statistik untuk menguji performa, tingkat kenyamanan, dan tingkat kelelahan. Pengujian performa dilakukan menggunakan data Time of Movement dan Throughput, sedangkan untuk pengujian tingkat kenyamanan menggunakan Assessment of Comfort, dan untuk pengujian tingkat kelelahan menggunakan Assessment of Fatigue.

A New Approach to Modeling the Prediction of Movement Time

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.

LenSelect: Object Selection in Virtual Environments by Dynamic Object Scaling

We present a novel selection technique for VR called LenSelect. The main idea is to decrease the Index of Difficulty (ID) according to Fitts’ Law by dynamically increasing the size of the potentially selectable objects. This facilitates the selection process especially in cases of small, distant or partly occluded objects, but also for moving targets. In order to evaluate our method, we have defined a set of test scenarios that covers a broad range of use cases, in contrast to often used simpler scenes. Our test scenarios include practically relevant scenarios with realistic objects but also synthetic scenes, all of which are available for download. We have evaluated our method in a user study and compared the results to two state-of-the-art selection techniques and the standard ray-based selection. Our results show that LenSelect performs similar to the fastest method, which is ray-based selection, while significantly reducing the error rate by 44%.

Evaluation of Child–Computer Interaction Using Fitts’ Law: A Comparison between a Standard Computer Mouse and a Head Mouse

This study evaluates and compares the suitability for child–computer interaction (CCI, the branch within human–computer interaction focused on interactive computer systems for children) of two devices: a standard computer mouse and the ENLAZA interface, a head mouse that measures the user’s head posture using an inertial sensor. A multidirectional pointing task was used to assess the motor performance and the users’ ability to learn such a task. The evaluation was based on the interpretation of the metrics derived from Fitts’ law. Ten children aged between 6 and 8 participated in this study. Participants performed a series of pre- and post-training tests for both input devices. After the experiments, data were analyzed and statistically compared. The results show that Fitts’ law can be used to detect changes in the learning process and assess the level of psychomotor development (by comparing the performance of adults and children). In addition, meaningful differences between the fine motor control (hand) and the gross motor control (head) were found by comparing the results of the interaction using the two devices. These findings suggest that Fitts’ law metrics offer a reliable and objective way of measuring the progress of physical training or therapy.

A New Approach to Modeling the Prediction of Movement Time

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