CRASH DETECTION ON MOBILE DEVICE

Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone. In some embodiments, a method comprises: detecting a crash event on a crash device; extracting multimodal features from sensor data generated by multiple sensing modalities of the crash d...

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Bibliographische Detailangaben
Hauptverfasser:	Liu, Richard G, Venkateswaran, Sriram, Choi, Henry, Renard, Yann Jerome Julien, Majjigi, Vinay R, Bryan, Paul D, Agarwal, Mrinal, Aziz, Omar, Liao, Yi Wen, Melendez Hasbun, Alvaro J, Varangot, Pedro O, Aranake, Aniket, Clarkson, Rebecca L, Ojeda Avellaneda, David, Dehleh Hossein Zadeh, Parisa, Raghuram, Karthik Jayaraman, Chow, Sunny Kai Pang, Jackson, Stephen P, Popovici, Alexandru, Pham, Hung A, Bhamre, Tejal, Goolish, Ethan, Sun, Tianye, Rao, Bharath Narasimha
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DETECTING MASSES OR OBJECTS ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY GEOPHYSICS GRAVITATIONAL MEASUREMENTS MEASURING PHYSICS TESTING WIRELESS COMMUNICATIONS NETWORKS
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Beschreibung
Zusammenfassung:	Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone. In some embodiments, a method comprises: detecting a crash event on a crash device; extracting multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features, wherein at least one multimodal feature is a rotation rate about a mean axis of rotation; and determining that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.