IP Library › Granted Patent US 12,380,789
Granted Patent B2
US 12,380,789 · App. 18/628,653 · Granted Aug 5, 2025

Detecting falls using a mobile device

Inventors: Hung A. Pham (Oakland, CA); Stephen P. Jackson (San Francisco, CA); Vinay R. Majigi (Mountain View, CA); Karthik Jayaraman Raghuram (Mountain View, CA); Adeeti V. Ullal (Mountain View, CA); Yann Jerome Julien Renard (San Carlos, CA); Telford Earl Forgety, III (San Jose, CA)
Assignee: Apple Inc.
G08B21/0446A61B5/002A61B5/0205A61B5/024A61B5/1112A61B5/1117A61B5/1121A61B5/1123A61B5/681A61B5/7246A61B5/7264A61B5/7405A61B5/742A61B5/7455A61B5/746A61B5/747G01C5/06G01C21/12G01S19/13G06F3/011G08B13/2454A61B2503/10A61B2560/0242A61B2562/0219
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,380,789
App. No.
18/628,653
Filed
Apr 5, 2024
Granted
Aug 5, 2025
Kind
B2
Art Unit
2685
USPC
340/539.11
Abstract

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

Claims (54)

1. A method comprising:

receiving, by a mobile device, motion data obtained by one or more sensors worn by a user;

determining, by the mobile device, and based on the motion data, that the user experienced an impact;

determining, by the mobile device, and based on the motion data, one or more behavioral features of the user's motion prior to the impact; and

determining, by the mobile device, whether the user has fallen based on the determination that the user experienced the impact and the one or more behavior features.

2. The method of claim 1 , wherein the one or more behavioral features are determined based on a behavior model having at least some of the motion data as an input.

3. The method of claim 2 , wherein determining the one or more behavioral features comprises:

determining, based on the behavior model, that the user performed a bracing motion prior to the impact.

4. The method of claim 3 , wherein determining that the user performed the bracing motion comprises:

determining that the user's wrist moved outward from the user's body.

5. The method of claim 2 , wherein determining the one or more behavioral features comprises:

determining, based on the behavior model, that the user performed a flailing motion prior to the impact.

6. The method of claim 5 , wherein determining that the user performed the flailing motion comprises at least one of:

determining a change in an orientation of the users' wrist over time,

determining that a motion of the user's wrist reversed one or more times.

7. The method of claim 2 , wherein determining the one or more behavioral features comprises:

determining, based on the behavior model, that the user performed a balancing motion prior to the impact.

8. The method of claim 7 , wherein determining the one or more behavioral features comprises:

determining the user's wrist moved along a positive arc length.

9. The method of claim 2 , further comprising:

determining that the user has fallen, and

determining, based on the behavior model, a type of fall of the user.

10. The method of claim 9 , wherein the type of fall is at least one of:

a slip,

a trip, or

a roll.

11. The method of claim 9 , wherein the type of fall is selected from among:

a slip,

a trip, or

a roll.

12. The method of claim 2 , further comprising:

determining that the user has fallen, and

determining, based on the behavior model, that the user has recovered after the fall.

13. The method of claim 1 , wherein the mobile device is a wearable mobile device.

14. The method of claim 1 , wherein at least some of the one or more sensors are disposed on or in the mobile device.

15. The method of claim 1 , wherein at least some of the one or more sensors are remote from the mobile device.

16. A system comprising:

one or more processors; and

one or more non-transitory computer readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving motion data obtained by one or more sensors worn by a user;

determining, based on the motion data, that the user experienced an impact;

determining, based on the motion data, one or more behavioral features of the user's motion prior to the impact; and

determining whether the user has fallen based on the determination that the user experienced the impact and the one or more behavior features.

17. The system of claim 16 , wherein the system comprises a mobile device including the one or more sensors.

18. The system of claim 16 , wherein the one or more behavioral features are determined based on a behavior model having at least some of the motion data as an input.

19. The system of claim 16 , wherein determining the one or more behavioral features comprises at least one of:

determining, based on the behavior model, that the user performed a bracing motion prior to the impact,

determining, based on the behavior model, that the user performed a flailing motion prior to the impact, or

determining, based on the behavior model, that the user performed a balancing motion prior to the impact.

20. One or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving, by a mobile device, motion data obtained by one or more sensors worn by a user;

determining, by the mobile device, and based on the motion data, that the user experienced an impact;

determining, by the mobile device, and based on the motion data, one or more behavioral features of the user's motion prior to the impact; and

determining, by the mobile device, whether the user has fallen based on the determination that the user experienced the impact and the one or more behavior features.

Continuity (6)
Continuation 17945977 · Sep 15, 2022
Continuation 16929043 · Jul 14, 2020
Continuation In Part 16852370 · Apr 17, 2020
Continuation 16128464 · Sep 11, 2018
Provisional Application 62565988 · Sep 29, 2017
Related Publication 20240273990A1 · Aug 15, 2024
References Cited (99)
US 8206325B1 · Najafi et al. · 2012 [cited by applicant]
US 8217795B2 · Carlton-Foss · 2012 [cited by applicant]
US 8909497B1 · Shkolnikov · 2014 [cited by applicant]
US 9179864B2 · Otto et al. · 2015 [cited by applicant]
US 9342108B2 · Rothkopf et al. · 2016 [cited by applicant]
US 9597004B2 · Hughes et al. · 2017 [cited by applicant]
US 9640057B1 · Ross · 2017 [cited by applicant]
US 9704369B2 · Richardson · 2017 [cited by examiner]
US 9773397B2 · Ten Kate et al. · 2017 [cited by applicant]
US 9818282B2 · Burton et al. · 2017 [cited by applicant]
US 9959733B2 · Gu et al. · 2018 [cited by applicant]
US 10147296B2 · Gregg · 2018 [cited by applicant]
US 10226204B2 · Heaton · 2019 [cited by applicant]
US 10629048B2 · Tan et al. · 2020 [cited by applicant]
US 10692011B2 · Pathak et al. · 2020 [cited by applicant]
US 10950112B2 · Kate et al. · 2021 [cited by applicant]
US 11276290B2 · Tan et al. · 2022 [cited by applicant]
US 11282361B2 · Sharma et al. · 2022 [cited by applicant]
US 11282362B2 · Tan et al. · 2022 [cited by applicant]
US 11282363B2 · Tan et al. · 2022 [cited by applicant]
US 11527140B2 · Pham et al. · 2022 [cited by applicant]
US 11842615B2 · Tan et al. · 2023 [cited by applicant]
US 12027027B2 · Pham · 2024 [cited by examiner]
US 12039850B2 · Simmons · 2024 [cited by examiner]
US 20080001735A1 · Tran · 2008 [cited by applicant]
US 20090322540A1 · Richardson et al. · 2009 [cited by applicant]
US 20120101411A1 · Hausdorff et al. · 2012 [cited by applicant]
US 20120259577A1 · Ganyi · 2012 [cited by applicant]
US 20120314901A1 · Hanson et al. · 2012 [cited by applicant]
US 20130054180A1 · Barfield · 2013 [cited by applicant]
US 20130120147A1 · Narasimhan et al. · 2013 [cited by applicant]
US 20130143519A1 · Doezema · 2013 [cited by applicant]
US 20140100487A1 · McNair · 2014 [cited by applicant]
US 20140278229A1 · Hong et al. · 2014 [cited by applicant]
US 20140338445A1 · Lin et al. · 2014 [cited by applicant]
US 20140375461A1 · Richardson et al. · 2014 [cited by applicant]
US 20150226764A1 · Ten Kate · 2015 [cited by applicant]
US 20150269824A1 · Zhang · 2015 [cited by applicant]
US 20160113551A1 · Annegarn et al. · 2016 [cited by applicant]
US 20160220153A1 · Annegarn et al. · 2016 [cited by applicant]
US 20160260311A1 · Asano · 2016 [cited by applicant]
US 20170055851A1 · Al-Ali · 2017 [cited by applicant]
US 20170193787A1 · Devdas et al. · 2017 [cited by applicant]
US 20170200359A1 · Gregg · 2017 [cited by applicant]
US 20180000385A1 · Heaton et al. · 2018 [cited by applicant]
US 20180070889A1 · Lee et al. · 2018 [cited by applicant]
US 20180177436A1 · Chang et al. · 2018 [cited by applicant]
US 20180247713A1 · Rothman · 2018 [cited by applicant]
US 20180333083A1 · Orellano · 2018 [cited by applicant]
US 20190099114A1 · Mouradian et al. · 2019 [cited by applicant]
US 20190103007A1 · Tan et al. · 2019 [cited by applicant]
US 20190200915A1 · Baker et al. · 2019 [cited by applicant]
US 20190320945A1 · Johnson et al. · 2019 [cited by applicant]
US 20200205697A1 · Zheng et al. · 2020 [cited by applicant]
US 20200250954A1 · Tan et al. · 2020 [cited by applicant]
US 20200342735A1 · Tan et al. · 2020 [cited by applicant]
US 20200342736A1 · Tan et al. · 2020 [cited by applicant]
US 20200342737A1 · Pham et al. · 2020 [cited by applicant]
US 20210005071A1 · Sharma et al. · 2021 [cited by applicant]
US 20220036714A1 · Tan et al. · 2022 [cited by applicant]
US 20230042265A1 · Pham et al. · 2023 [cited by applicant]
US 20230112071A1 · Khalak et al. · 2023 [cited by applicant]
US 20230252909A1 · McNair · 2023 [cited by applicant]
US 20240127683A1 · Tan et al. · 2024 [cited by applicant]
US 20250046172A1 · Tan et al. · 2025 [cited by applicant]
CN 102186420 · 2011 [cited by applicant]
CN 102903207 · 2013 [cited by applicant]
CN 103593944 · 2014 [cited by applicant]
CN 104055518 · 2014 [cited by applicant]
CN 104504855 · 2015 [cited by applicant]
CN 105448040 · 2016 [cited by applicant]
CN 105530865 · 2016 [cited by applicant]
CN 105769205 · 2016 [cited by applicant]
CN 106037749 · 2016 [cited by applicant]
CN 106530611 · 2017 [cited by applicant]
CN 107123239 · 2017 [cited by applicant]
CN 107205679 · 2017 [cited by applicant]
CN 107233099 · 2017 [cited by applicant]
CN 107305645 · 2017 [cited by applicant]
CN 108257679 · 2018 [cited by applicant]
CN 111132603 · 2020 [cited by applicant]
CN 111383420 · 2020 [cited by applicant]
JP 2007507320 · 2007 [cited by applicant]
JP 2011521349 · 2011 [cited by applicant]
JP 4915263 · 2012 [cited by applicant]
JP 2016512777 · 2016 [cited by applicant]
JP 2016529081 · 2016 [cited by applicant]
JP 2016177437 · 2016 [cited by applicant]
JP 2016177449 · 2016 [cited by applicant]
KR 1020110071212 · 2011 [cited by applicant]
KR 101754576 · 2017 [cited by applicant]
WO WO2012146957 · 2012 [cited by applicant]
WO WO2015087164 · 2015 [cited by applicant]
Ibomoiye Domor Mienye, Yanxia Sun, Zenghui Wang, 2020, “An improved ensemble learning approach for the prediction of heart disease risk,” Informatics in Medicine Unlocked, vol. 20,100402, ISSN 2352-9148. (Year: 2020). [cited by applicant]
Fortino et al., “Fall-MobileGuard: Smart real-time fall detection system,” EAI International Conference on Body Area Networks, Sep. 28, 2015, pp. 44-50. [cited by applicant]
Shahiduzzaman et al., “Fall Detection by Accelerometer and Heart Rate Variability Measurement,” Global Journal of Computer Science and Technology: G Interdisciplinary, Dec. 31, 2015, 15(3), 7 pages. [cited by applicant]
Shahzad et al., “FallDroid: An Automated Smart-Phone-Based Fall Detection System Using Multiple Kernel Learning,” IEEE Transactions of Industrial Informatics, May 23, 2018, 11 pages. [cited by applicant]
‘who.com’ [online]. “Falls,” Published on Jan. 16, 2018, [retrieved on Mar. 15, 2019], retrieved from the Internet: URL :<https://www.who.int/en/news-room/fact-sheets/detail/falls>. 4 pages. [cited by applicant]
Yongkun et al., “A New Smart Fall-down Detector for Senior Healthcare System Using Inertial Micro sensors,” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 26, 201… [cited by applicant]