2018 Fiscal Year Final Research Report
Deep Learning Based Physiological Classifier Feedback for Comfortable Navigation
| Project/Area Number |
16K21719
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
Human interface and interaction
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| Research Institution | Nagoya University |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | 知能情報処理 / 知能ロ ボ ッ ト / Human-Robot Interaction |
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| Outline of Final Research Achievements |
This research grant was used to finance a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle).
This research developed a navigational approach that takes into consideration the perception of comfort by a human passenger. Comfort is the state of being at ease and free from stress; thus, comfortable navigation is a ride that, in addition to being safe, is perceived by the passenger as being free from anxiety and stress. This study considers how to compute passenger comfortable paths. To compute such paths, passenger discomfort is studied in locations with good visibility and those with no visibility. Autonomous-navigation experiments are performed to build a map of human discomfort that is used to compute global paths. A path planner is proposed that minimizes a three-variable cost function: location discomfort cost, area visibility cost, and path length cost.
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| Free Research Field |
人間情報学
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| Academic Significance and Societal Importance of the Research Achievements |
This research is based on a data driven approach for safe and smooth autonomous navigation of a personal mobility vehicle (PMV) when facing pedestrians. For autonomous navigation we implemented a Frenet planner to achieve safe and smooth navigation for the passenger and pedestrians around.
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