Fall risk assessment for a user

1 . A computer-implemented method for assessing a fall risk of a user, the computer-implemented method comprising:

obtaining, by one or more processors, data indicative of the user engaging in an increased risk of falling from one or more sensors of a wearable computing device worn by the user, wherein the data indicative of the user engaging in an increased risk of falling comprises at least one of the user standing up and walking too fast after sleeping, the user carrying an object while walking, or the user being distracted while walking;

adjusting, by the one or more processors, a sensitivity of a fall detection threshold of the wearable computing device worn by the user based, at least in part, on the increased risk of falling, wherein adjusting the sensitivity of the fall detection threshold of the wearable computing device comprises:

providing, by the one or more processors, the data indicative of the user engaging in the increased risk of falling from the one or more sensors as an input to a machine-learned model configured to process the data to determine an adjusted fall detection threshold; and

obtaining, by the one or more processors, the adjusted fall detection threshold as an output of the machine-learned model; and

providing, by the one or more processors, a notification to the user based on the increased risk of falling.

2 . The computer-implemented method of claim 1 , wherein obtaining data indicative of the user engaging in the increased risk of falling comprises obtaining, by the one or more processors, a signal from a mobile computing device associated with the user, the signal indicative of the user engaging in the increased risk of falling.

3 . The computer-implemented method of claim 2 , wherein providing the notification comprises providing, by the one or more processors, the notification for display on a display screen of the mobile computing device.

4 . The computer-implemented method of claim 1 , wherein:

the one or more sensors of the wearable computing device includes a plurality of different sensors; and

the adjusted fall detection threshold includes a plurality of adjusted fall detection thresholds, each of the plurality of adjusted fall detection thresholds associated with a respective sensor of the plurality of different sensors.

5 . The computer-implemented method of claim 1 , further comprising:

obtaining, by the one or more processors, sleep data associated with a sleep event.

6 . The computer-implemented method of claim 5 , wherein adjusting the sensitivity of the fall detection threshold of the wearable computing device comprises adjusting, by the one or more processors, the sensitivity of the fall detection threshold of the wearable computing device according to the sleep data and the data indicative of the user engaging in an increased risk of falling.

7 . The computer-implemented method of claim 6 , wherein the sleep data comprises at least one of a duration of the sleep event or a depth of the sleep event.

8 . The computer-implemented method of claim 7 , wherein the depth of the sleep event comprises data indicative of whether the user is waking from a rapid-eye-movement (REM) sleep cycle or a non-REM sleep cycle.

9 . The computer-implemented method of claim 1 , wherein obtaining data indicative of the user engaging in the increased risk of falling includes:

obtaining, by the one or more processors, first data from one or more sensors of a first wearable computing device worn at a first location on a body of the user; and

obtaining, by the one or more processors, second data from one or more sensors of a second wearable computing device worn at a second location on the body of the user, the second location being different than the first location.

10 . The computer-implemented method of claim 9 , wherein adjusting the sensitivity of the fall detection threshold of the wearable computing device comprises adjusting, by the one or more processors, the sensitivity of the fall detection threshold of at least one of the first wearable computing device or the second wearable computing device.

11 . A wearable computing device comprising:

a plurality of sensors; and

one or more processors communicatively coupled with the plurality of sensors, the one or more processors configured to:

obtain data indicative of a user wearing the wearable computing device engaging in an increased risk of falling from the plurality of sensors of the wearable computing device worn by a user, wherein the data indicative of the user engaging in an increased risk of falling comprises at least one of the user standing up and walking too fast after sleeping, the user carrying an object while walking, or the user being distracted while walking;

adjust a sensitivity of a fall detection threshold based, at least in part, on the increased risk of falling, wherein, to adjust the sensitivity of the fall detection threshold, the one or more processors are configured to:

provide the data indicative of the user wearing the wearable computing device engaging in the increased risk of falling as an input to a machine-learned model; and

obtain an adjusted fall detection threshold as an output of the machine-learned model; and

provide a notification to the user based on the increased risk for falling.

12 . The wearable computing device of claim 11 , wherein to obtain data indicative of the user engaging in the increased risk of falling, the one or more processors are configured to:

provide data from one or more sensors of the plurality of sensors as an input to a machine-learned model configured to classify the data from the plurality of sensors as corresponding to a first fall risk activity of a plurality of different fall risk activities; and

obtain an identifier indicative of the first fall risk activity as an output of the machine-learned model.

13 . The wearable computing device of claim 11 , wherein the plurality of sensors include at least one of an accelerometer, a gyroscope, or a barometer.

14 . The wearable computing device of claim 11 , further comprising:

one or more output devices configured to output the notification to the user based on the increased risk for falling.

15 . The wearable computing device of claim 14 , wherein the one or more output devices comprise at least one of a speaker or a display screen.

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

obtaining data indicative of a user engaging in an increased risk of falling from one or more sensors of a wearable computing device worn by a user, wherein the data indicative of the user engaging in an increased risk of falling comprises at least one of the user standing up and walking too fast after sleeping, the user carrying an object while walking, or the user being distracted while walking;

adjusting a sensitivity of a fall detection threshold based, at least in part, on the increased risk of falling, wherein adjusting the sensitivity of the fall detection threshold comprises providing the data indicative of the user engaging in an increased risk of falling from the one or more sensors as an input to a machine-learned model and obtaining an adjusted fall detection threshold as an output of the machine-learned model; and

providing a notification to the user based on the increased risk of falling.