公开(公告)号：US20240329277A1

公开(公告)日：2024-10-03

申请号：US18737920

申请日：2024-06-07

申请人： Apple Inc.

发明人： Vinay R. Majjigi ,  Bharath Narasimha Rao ,  Sriram Venkateswaran ,  Aniket Aranake ,  Tejal Bhamre ,  Alexandru Popovici ,  Parisa Dehleh Hossein Zadeh ,  Yann Jerome Julien Renard ,  Yi Wen Liao ,  Stephen P. Jackson ,  Rebecca L. Clarkson ,  Henry Choi ,  Paul D. Bryan ,  Mrinal Agarwal ,  Ethan Goolish ,  Richard G. Liu ,  Omar Aziz ,  Alvaro J. Melendez Hasbun ,  David Ojeda Avellaneda ,  Sunny Kai Pang Chow ,  Pedro O. Varangot ,  Tianye Sun ,  Karthik Jayaraman Raghuram ,  Hung A. Pham

IPC分类号： G01V7/02 ,  G01V7/16 ,  G06N20/00 ,  H04W4/90

CPC分类号： G01V7/02 ,  G01V7/16 ,  G06N20/00 ,  H04W4/90

摘要： 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.

公开(公告)号：US20240075895A1

公开(公告)日：2024-03-07

申请号：US18462271

申请日：2023-09-06

申请人： Apple Inc.

发明人： Vinay R. Majjigi ,  Sriram Venkateswaran ,  Aniket Aranake ,  Tejal Bhamre ,  Alexandru Popovici ,  Parisa Dehleh Hossein Zadeh ,  Yann Jerome Julien Renard ,  Yi Wen Liao ,  Stephen P. Jackson ,  Rebecca L. Clarkson ,  Henry Choi ,  Paul D. Bryan ,  Mrinal Agarwal ,  Ethan Goolish ,  Richard G. Liu ,  Omar Aziz ,  Alvaro J. Melendez Hasbun ,  David Ojeda Avellaneda ,  Sunny Kai Pang Chow ,  Pedro O. Varangot ,  Tianye Sun ,  Karthik Jayaraman Raghuram ,  Hung A. Pham

IPC分类号： B60R21/013 ,  G06F18/213

CPC分类号： B60R21/013 ,  G06F18/213 ,  B60R2021/0027

摘要： 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, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.