When developing effective assistive technology, it is crucial to focus on how acceptance and continued use of the technology can be optimized considering the (complexity of the) user and his or her situation. Therefore, this chapter describes methods for creating user models and shows how these were applied to user groups (patients with spinal cord injury, Parkinson's disorder and neuromuscular disorders) of a newly developed assistive technology (AT). The user models include user characteristics such as demographics, relevant medical information, computer interaction behaviour and attitudes towards novel assistive devices. Next, this chapter describes persuasive strategies to improve user acceptance and continued use of AT, specifically aimed at motivating individuals with disabilities to learn to operate the AT and to use it, in order to increase their social participation. Also, this chapter shows how empirical research has tested the effectiveness of the proposed persuasive and personalization (i.e., incorporating user model knowledge) design elements. Finally, this chapter shows how the implications of these findings were used to improve the persuasive design requirements of the AT. In sum, this chapter shows how persuasive personalized design principles (implemented into the AT) improve user acceptance (evaluations) and continued use (performance).
|Titel||Signal Processing to Drive Human-Computer Interaction|
|Subtitel||EEG and eye-controlled interfaces|
|Redacteuren||Spiros Nikolopoulos, Chandan Kumar, Ioannis Kompatsiaris|
|Uitgeverij||Institution of Engineering and Technology (IET)|
|ISBN van elektronische versie||9781785619205|
|ISBN van geprinte versie||9781785619199|
|Status||Gepubliceerd - 2020|