Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a P ervasive P ose Est imation scheme for fall detection ( P \( ^2 \) Est ), which measures changes in tilt angle and height of the human body. For the tilt measurement, P \( ^2 \) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P \( ^2 \) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P \( ^2 \) Est in various situations and environments. Our extensive experimental results have demonstrated that P \( ^2 \) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy.
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Sensing the walking velocity of a person by using mobile devices
In an indoor space, determining a person’s speed of mobility has a lot of research significance
and applicability in real-world scenarios. This research has developed a mobile application to
investigate how to determine a person’s walking speed. The goal was to determine a person’s
walking speed by using the number of steps. There has been similar work to test the
accelerometer sensor in detecting steps. However, the accuracy of using the steps to calculate
the velocity was not studied. This application uses the accelerometer sensor in the mobile device
to detect steps and then compute the velocity. The accelerometer provides information about
the user’s motion and acceleration, and an algorithm was developed to use that data to
determine the steps. Once steps are determined, the person’s speed is calculated by using the
change of location within a pre-determined space and time. Therefore, accurately measuring the
number of steps was vital and it was determined that the position of the mobile device in the
body plays a significant role in that accuracy. Therefore, the experiment used three device
positions: the pants front pocket, the right hand, and the backpack. While walking, the number
of steps were manually counted and recorded. A comparison was made between the recorded
number of steps to the application’s measured steps. The experiment was conducted multiple
times for each device position. The placement of the mobile devices in the front pants pocket
gives the most accurate results, whereas the other two device positions gave reasonably
accurate results. The position of the device played an important part in the research and had a
significant impact on the accuracy of the results. In the future, testing can include additional
device positions. Additionally, other mobile device sensors could be included in the testing and
can be compared with this approach.
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- Award ID(s):
- 2131100
- NSF-PAR ID:
- 10354397
- Date Published:
- Journal Name:
- The Twenty-Sixth ACM Annual Consortium for Computing Sciences in Colleges Northeastern Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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