In this paper, we developed a system to estimate human mapreading ability based on three indicating measurements. In order to design the measurements and validate their correctness, we conducted a questionnaire and outdoor experiments and found significant reasons why the participants lost their ways. We modeled such reasons with measurements for testing a sense of space memory, a sense of direction, and a sense of positioning. Although these factors were often called as the major reason of losing one’s way, we could find the reasons from our experiments and formalize them as measurable indicators. Based on these measures, we computed the correlation between the questionnaire and the outdoor experiment and showed a strong relation to validate our measurements. We also performed another experiment to estimate the same ability in our system with the same measurements. As a result, the simulation can estimate the
human map-reading ability almost similarly to the outdoor experiments or questionnaires. This means that with a little effort from users, the system can reveal the ability to a high precision and furthermore it is much better than the questionnaire to analyze the weak points in way-finding of the participant in detail. In future work, we will investigate other measures which we could not clarify in this experiment and increase the emulating degree of the simulating in our system. Especially, with this kind of ability measuring system, we expect that current navigation systems could understand the ability of their users and provide much better adaptive services depending on each user’s ability.
In this paper, we developed a system to estimate human mapreading ability based on three indicating measurements. In order to design the measurements and validate their correctness, we conducted a questionnaire and outdoor experiments and found significant reasons why the participants lost their ways. We modeled such reasons with measurements for testing a sense of space memory, a sense of direction, and a sense of positioning. Although these factors were often called as the major reason of losing one’s way, we could find the reasons from our experiments and formalize them as measurable indicators. Based on these measures, we computed the correlation between the questionnaire and the outdoor experiment and showed a strong relation to validate our measurements. We also performed another experiment to estimate the same ability in our system with the same measurements. As a result, the simulation can estimate the
human map-reading ability almost similarly to the outdoor experiments or questionnaires. This means that with a little effort from users, the system can reveal the ability to a high precision and furthermore it is much better than the questionnaire to analyze the weak points in way-finding of the participant in detail. In future work, we will investigate other measures which we could not clarify in this experiment and increase the emulating degree of the simulating in our system. Especially, with this kind of ability measuring system, we expect that current navigation systems could understand the ability of their users and provide much better adaptive services depending on each user’s ability.
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In this paper, we developed a system to estimate human mapreading ability based on three indicating measurements. In order to design the measurements and validate their correctness, we conducted a questionnaire and outdoor experiments and found significant reasons why the participants lost their ways. We modeled such reasons with measurements for testing a sense of space memory, a sense of direction, and a sense of positioning. Although these factors were often called as the major reason of losing one’s way, we could find the reasons from our experiments and formalize them as measurable indicators. Based on these measures, we computed the correlation between the questionnaire and the outdoor experiment and showed a strong relation to validate our measurements. We also performed another experiment to estimate the same ability in our system with the same measurements. As a result, the simulation can estimate the
human map-reading ability almost similarly to the outdoor experiments or questionnaires. This means that with a little effort from users, the system can reveal the ability to a high precision and furthermore it is much better than the questionnaire to analyze the weak points in way-finding of the participant in detail. In future work, we will investigate other measures which we could not clarify in this experiment and increase the emulating degree of the simulating in our system. Especially, with this kind of ability measuring system, we expect that current navigation systems could understand the ability of their users and provide much better adaptive services depending on each user’s ability.
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