View source for 20240075895.CRASH DETECTION ON MOBILE DEVICE simplified abstract (apple inc.)

=CRASH DETECTION ON MOBILE DEVICE=


==Organization Name==

[[:Category:apple inc.|apple inc.]]


[[Category:apple inc.]]

==Inventor(s)==

[[:Category:Vinay R. Majjigi of Mountain View CA (US)|Vinay R. Majjigi of Mountain View CA (US)]][[Category:Vinay R. Majjigi of Mountain View CA (US)]]

[[:Category:Sriram Venkateswaran of Sunnyvale CA (US)|Sriram Venkateswaran of Sunnyvale CA (US)]][[Category:Sriram Venkateswaran of Sunnyvale CA (US)]]

[[:Category:Aniket Aranake of San Jose CA (US)|Aniket Aranake of San Jose CA (US)]][[Category:Aniket Aranake of San Jose CA (US)]]

[[:Category:Tejal Bhamre of Mountain View CA (US)|Tejal Bhamre of Mountain View CA (US)]][[Category:Tejal Bhamre of Mountain View CA (US)]]

[[:Category:Alexandru Popovici of Santa Clara CA (US)|Alexandru Popovici of Santa Clara CA (US)]][[Category:Alexandru Popovici of Santa Clara CA (US)]]

[[:Category:Parisa Dehleh Hossein Zadeh of San Jose CA (US)|Parisa Dehleh Hossein Zadeh of San Jose CA (US)]][[Category:Parisa Dehleh Hossein Zadeh of San Jose CA (US)]]

[[:Category:Yann Jerome Julien Renard of San Carlos CA (US)|Yann Jerome Julien Renard of San Carlos CA (US)]][[Category:Yann Jerome Julien Renard of San Carlos CA (US)]]

[[:Category:Yi Wen Liao of San Jose CA (US)|Yi Wen Liao of San Jose CA (US)]][[Category:Yi Wen Liao of San Jose CA (US)]]

[[:Category:Stephen P. Jackson of San Francisco CA (US)|Stephen P. Jackson of San Francisco CA (US)]][[Category:Stephen P. Jackson of San Francisco CA (US)]]

[[:Category:Rebecca L. Clarkson of San Francisco CA (US)|Rebecca L. Clarkson of San Francisco CA (US)]][[Category:Rebecca L. Clarkson of San Francisco CA (US)]]

[[:Category:Henry Choi of Cupertino CA (US)|Henry Choi of Cupertino CA (US)]][[Category:Henry Choi of Cupertino CA (US)]]

[[:Category:Paul D. Bryan of San Jose CA (US)|Paul D. Bryan of San Jose CA (US)]][[Category:Paul D. Bryan of San Jose CA (US)]]

[[:Category:Mrinal Agarwal of San Jose CA (US)|Mrinal Agarwal of San Jose CA (US)]][[Category:Mrinal Agarwal of San Jose CA (US)]]

[[:Category:Ethan Goolish of Mountain View CA (US)|Ethan Goolish of Mountain View CA (US)]][[Category:Ethan Goolish of Mountain View CA (US)]]

[[:Category:Richard G. Liu of Sherman Oaks CA (US)|Richard G. Liu of Sherman Oaks CA (US)]][[Category:Richard G. Liu of Sherman Oaks CA (US)]]

[[:Category:Omar Aziz of Santa Clara CA (US)|Omar Aziz of Santa Clara CA (US)]][[Category:Omar Aziz of Santa Clara CA (US)]]

[[:Category:Alvaro J. Melendez Hasbun of San Francisco CA (US)|Alvaro J. Melendez Hasbun of San Francisco CA (US)]][[Category:Alvaro J. Melendez Hasbun of San Francisco CA (US)]]

[[:Category:David Ojeda Avellaneda of San Francisco CA (US)|David Ojeda Avellaneda of San Francisco CA (US)]][[Category:David Ojeda Avellaneda of San Francisco CA (US)]]

[[:Category:Sunny Kai Pang Chow of San Jose CA (US)|Sunny Kai Pang Chow of San Jose CA (US)]][[Category:Sunny Kai Pang Chow of San Jose CA (US)]]

[[:Category:Pedro O. Varangot of San Francisco CA (US)|Pedro O. Varangot of San Francisco CA (US)]][[Category:Pedro O. Varangot of San Francisco CA (US)]]

[[:Category:Tianye Sun of Sunnyvale CA (US)|Tianye Sun of Sunnyvale CA (US)]][[Category:Tianye Sun of Sunnyvale CA (US)]]

[[:Category:Karthik Jayaraman Raghuram of Foster City CA (US)|Karthik Jayaraman Raghuram of Foster City CA (US)]][[Category:Karthik Jayaraman Raghuram of Foster City CA (US)]]

[[:Category:Hung A. Pham of Oakland CA (US)|Hung A. Pham of Oakland CA (US)]][[Category:Hung A. Pham of Oakland CA (US)]]

==CRASH DETECTION ON MOBILE DEVICE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240075895 titled 'CRASH DETECTION ON MOBILE DEVICE

==Simplified Explanation==

The patent application describes a method for crash detection on mobile devices using sensor data and machine learning models.

* Detect crash event on a mobile device
* Extract multimodal features from sensor data
* Compute crash decisions using machine learning models
* Determine severe vehicle crash based on decisions and severity model

== Potential Applications ==
This technology could be applied in:
* Automotive safety systems
* Emergency response systems
* Wearable technology for personal safety

== Problems Solved ==
This technology addresses:
* Timely detection of vehicle crashes
* Enhanced emergency response coordination
* Improved safety features on mobile devices

== Benefits ==
The benefits of this technology include:
* Increased safety for users of mobile devices
* Faster response times in emergency situations
* Enhanced data collection for crash analysis

== Potential Commercial Applications ==
This technology could be utilized in:
* Automotive industry for integrated safety systems
* Insurance companies for claims processing
* Mobile device manufacturers for enhanced features

== Possible Prior Art ==
One possible prior art could be traditional crash detection systems that rely on single sensor data rather than multimodal features and machine learning models.

=== Unanswered Questions ===

== How does this technology impact user privacy? ==
The article does not address the potential privacy concerns related to collecting and analyzing sensor data from mobile devices for crash detection purposes.

== What are the limitations of this technology in different environmental conditions? ==
The article does not discuss how this technology performs in various environmental conditions such as extreme weather or poor visibility.


==Original Abstract Submitted==

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.



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[[Category:G06F18/213]]