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The results show that all performance parameters degraded when the subject started to move, for all input techniques. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations.
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In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition.
![keystrokes per second test keystrokes per second test](https://linuxhint.com/wp-content/uploads/2019/12/5-29-1024x773.png)
Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Finally, the paper proposes several methods of choosing symbols and weighs the benefits of each method according to evaluation results. The adaptation is formally specified using timed Petri nets and evaluated on a group of users, aiming at producing components that can be re-used without the need for further evaluation, thus enabling rapid development of new applications without the need for real user testing, due to the nature and availability of AAC users. The paper proposes initial automatic adaptation of AAC application user interfaces that is performed only once and then distributed across AAC applications through a specialized AAC platform. Due to complex, and often very individual, communication needs, such persons need the ability to quickly adapt graphical user interfaces according to their needs, skills, impairments and possibilities. Support of ICT in Alternative and Augmentative Communication (AAC) has been recognized as the key enabler of better inclusion of persons with complex communication needs into everyday life.