help them during their pedestrian journeys without to make them dependent on the technology.The following section presents a brief state of the art on wayfinding, and on behaviors and difficulties encountered by people with intellectual disabilities. Then, adaptation principles are proposed in order to answer their problem of autonomy in the urban mobility. Different mock-ups illustrate this proposal. The paper ends with a conclusion and research perspectives. STATE OF THE ARTPeople with ID have specific functioning, needs and difficulties. In particular, they report significant differences in the urban mobility situations they encounter and in their problem-solving strategies, compared to a control group. They seemed to be more often lost, in conflict with another person, in a dangerous situation or in a situation involving a disruption in the transportation schedule. They more often choose to ask another person for help rather than change their route [3]. In addition, they seem to recognize relevant landmarks less effectively than typical subjects, which leads to an impact on their wayfinding performance [1]. People with ID are so not encouraged to go out, even more for trips they do not really know much about or not at all. Whether it comes from themselves or from their relatives, the fear of not being able to find their way back is an obstacle to their independence. However, it is quite possible for them to learn specific routes through individualized and supported learning [2,8]. Unfortunately, this learning based on regular training is particularly time consuming.Literature provides guidelines for improving ID people navigation through spatial cognition. Sohlberg and colleagues [10] recommend to: (1) use landmarks, (2) give short and clear instructions, (3) use written and auditory modalities, (4) link with a caregiver if needed, (5) allow the repetition of the instructions to overcome memory issues and parasite noises in urban environment, and (6) to include user's notes and landmarks personally chosen. These recommendations address two main points: taking into account information coming from the user (behavioral information) as well as from the outside (contextual information); personalization.
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