18205478. STRIDE LENGTH ESTIMATION AND CALIBRATION AT THE WRIST simplified abstract (Apple Inc.)

STRIDE LENGTH ESTIMATION AND CALIBRATION AT THE WRIST

Organization Name

Apple Inc.

Inventor(s)

Lucie A. Huet of Mountain View CA (US)

Adeeti V. Ullal of Emerald Hills CA (US)

Allison L. Gilmore of Redwood City CA (US)

Gabriel A. Blanco of San Francisco CA (US)

Karthik Jayaraman Raghuram of Foster City CA (US)

Maryam Etezadi-amoli of Santa Clara CA (US)

Richard A. Fineman of Campbell CA (US)

STRIDE LENGTH ESTIMATION AND CALIBRATION AT THE WRIST - A simplified explanation of the abstract

This abstract first appeared for US patent application 18205478 titled 'STRIDE LENGTH ESTIMATION AND CALIBRATION AT THE WRIST

Simplified Explanation

The patent application describes a method for estimating and calibrating stride length using sensor data from a wearable device worn on the wrist. The method involves deriving features from the sensor data and using an estimation model to estimate a form-based stride length based on these features and the user's height. The form-based stride length is then calibrated. Additionally, user cadence and speed can be used to estimate a speed-based stride length, which can be blended with the form-based stride length to obtain a final estimated stride length.

• Method for estimating and calibrating stride length using sensor data from a wearable device on the wrist
• Features are derived from the sensor data
• Estimation model is used to estimate form-based stride length based on features and user height
• Form-based stride length is calibrated
• User cadence and speed can be used to estimate speed-based stride length
• Speed-based stride length can be blended with form-based stride length for a final estimated stride length

Potential Applications

• Fitness tracking devices
• Sports performance analysis
• Rehabilitation monitoring

Problems Solved

• Accurate estimation and calibration of stride length
• Improved accuracy in fitness tracking and sports performance analysis
• Enhanced monitoring and analysis of rehabilitation progress

Benefits

• More accurate stride length estimation
• Improved tracking and analysis of user's fitness and performance
• Better monitoring and assessment of rehabilitation progress

Original Abstract Submitted

Embodiments are disclosed for stride length estimation and calibration at the wrist. In some embodiments, a method comprises: obtaining sensor data from a wearable device worn on a wrist of a user; deriving features from the sensor data; estimating a form-based stride length using an estimation model that takes the features and user height as input; and calibrating the form-based stride length. In other embodiments, user cadence and speed are used to estimate speed-based stride length which, upon certain conditions, is blended with the form-based stride length to get a final estimated stride length of the user.