In this paper, we presented an opportunistic system for natural interaction in a vehicle. We proposed a synergistic approach combining wearable and environmental paradigms to recognize micro-gestures on the steering wheel. We compared different fusion methods and segmentation strategies. The proposed ADAPTIVE segmentation strategy and the MCC* fusion method showed the best performance. In fact, we observed that the synergistic approach can perform equal or better than the best stand-alone classifier. The proposed system requires an accurate design to satisfy the requirements of synergy. We presented eight parameters to be used for tuning and improving the interaction design by taking into account the opportunities and limitations of the wearable and environmental paradigms. We applied the proposed opportunistic paradigm to the in-vehicle interaction scenario; however, such concept can be easily extended in other areas of HCI. We observed that the automatic segmentation has a recognition rate of about 6% lower than the manual segmentation. Further analyses, in order to decrease this gap, are planned as future work. Moreover, usability tests will be conducted to understand the impact of the segmentation strategy on the car driver in driving simulation scenario. The adoption of an eye-tracking system will allow us to measure the distraction of the driver and compare it with a standard approach, e.g., buttons or touch screen on the dashboard. Further observations will aim at analyzing the effects of the proposed approach on the driver's behavior.


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    Titel :

    Opportunistic Synergy: a Classifier Fusion Engine for Micro-Gesture Recognition


    Beteiligte:


    Erscheinungsdatum :

    2013


    Format / Umfang :

    8 Seiten, Bilder, Tabellen, 27 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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