Apple Inc.

Sriram Venkateswaran

Vinay R Majjigi

Yann Jerome Julien Renard

Parisa Dehleh Hossein Zadeh

Hung A Pham

Karthik Jayaraman Raghuram

Omar Aziz

Xing Tan

Stephen P Jackson

Mrinal Agarwal

Sunny Kai Pang Chow

Huayu Ding

Xiaoyue Zhang

Henry Choi

Tejal Bhamre

Rebecca L Clarkson

Aniket Aranake

Alexandru Popovici

Manjunath Shankar Rao

Yi Wen Liao

Paul D Bryan

Ethan Goolish

Alvaro J Melendez Hasbun

Tianye Sun

Richard G Liu

David Ojeda Avellaneda

Lauren Schutz

Pedro O Varangot

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.

Crash detection on mobile device

In an example method, a mobile device receives sensor data obtained by one or more sensor over a time period. The one or more sensors are worn by a user. Further, the mobile device determines a context of the user based on the sensor data, and obtains a set of rules for processing the sensor data based on the context, where the set of rules is specific to the context. The mobile device determines at least one of a likelihood that the user has fallen or a likelihood that the user requires assistance based on the sensor data and the set of rules, and generates one or more notifications based on at least one of the likelihood that the user has fallen or the likelihood that the user requires assistance.

Context aware fall detection using a mobile device

In an example method, a mobile device obtains a database including a plurality of data records. Each data record includes an indication of a respective impact previously experienced by a user of the mobile device, and sensor data generated by one or more first sensors worn by the user during that impact. The mobile device obtains sensor data generated by one or more second sensors worn by the user over a period of time, and determines whether the user has fallen during the period of time based on the database and the additional sensor data. The mobile device generates one or more notifications based on the determination of whether the user has fallen during the period of time.

Detecting falls using a mobile device

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Sunnyvale, CA, United States of America

Sriram Venkateswaran