18240230. PEDESTRIAN DEAD RECKONING USING DIFFERENTIAL GEOMETRIC PROPERTIES OF HUMAN GAIT simplified abstract (Apple Inc.)
Contents
- 1 PEDESTRIAN DEAD RECKONING USING DIFFERENTIAL GEOMETRIC PROPERTIES OF HUMAN GAIT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PEDESTRIAN DEAD RECKONING USING DIFFERENTIAL GEOMETRIC PROPERTIES OF HUMAN GAIT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
PEDESTRIAN DEAD RECKONING USING DIFFERENTIAL GEOMETRIC PROPERTIES OF HUMAN GAIT
Organization Name
Inventor(s)
Ryan K. Burns of Santa Clara CA (US)
Xiaoyuan Tu of Sunnyvale CA (US)
PEDESTRIAN DEAD RECKONING USING DIFFERENTIAL GEOMETRIC PROPERTIES OF HUMAN GAIT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18240230 titled 'PEDESTRIAN DEAD RECKONING USING DIFFERENTIAL GEOMETRIC PROPERTIES OF HUMAN GAIT
Simplified Explanation
The patent application describes a method for determining the direction of travel and speed of a user based on acceleration data from a motion sensor in a mobile device.
- Receiving acceleration data from a motion sensor of a mobile device carried by a user
- Computing a tangent-normal-binormal (TNB) reference frame from the acceleration data
- Computing a direction of travel of the user based on the orientation of a unit binormal (B) vector of the TNB reference frame
- Computing a speed of the user based on a linear regression model applied to the kinematic properties of the TNB reference frame
Potential Applications
This technology could be applied in fitness tracking apps, navigation systems, virtual reality games, and sports performance analysis.
Problems Solved
This technology solves the problem of accurately determining the direction and speed of a user's movement based on acceleration data from a mobile device.
Benefits
The benefits of this technology include improved accuracy in tracking user movement, enhanced user experience in virtual reality applications, and better performance analysis in sports training.
Potential Commercial Applications
The potential commercial applications of this technology include fitness tracking devices, navigation apps, virtual reality gaming systems, and sports performance analysis tools.
Possible Prior Art
One possible prior art could be motion capture systems used in sports training and animation industry to track movement and analyze performance.
What are the specific technical details of the linear regression model used in this method?
The specific technical details of the linear regression model used in this method are not provided in the abstract. It would be helpful to know the variables considered in the regression model and the methodology used to calculate the speed of the user.
How does this method handle variations in user movement patterns, such as different walking styles or running speeds?
The abstract does not mention how this method handles variations in user movement patterns. It would be interesting to know if the algorithm is adaptable to different walking styles or running speeds, and how it accounts for these variations in determining the direction of travel and speed of the user.
Original Abstract Submitted
In some embodiments, a method comprises: receiving acceleration data from a motion sensor of a mobile device carried by a user, the acceleration data represented by a space curve in a three-dimensional (3D) acceleration space, the space curve indicative of a cyclical vertical displacement of the user's center of mass accompanied by a lateral left and right sway of the center of mass when the user is stepping; computing a tangent-normal-binormal (TNB) reference frame from the acceleration data, the TNB reference frame describing instantaneous geometric and kinematic properties of the space curve over time; and computing a direction of travel of the user based on an orientation of a unit binormal (B) vector of the TNB reference frame in the 3D acceleration space, and computing a speed of the user based on a linear regression model applied to the kinematic properties of the TNB reference frame.
