17952147. Estimating Heart Rate Recovery After Maximum or High-Exertion Activity Based on Sensor Observations of Daily Activities simplified abstract (Apple Inc.)
Estimating Heart Rate Recovery After Maximum or High-Exertion Activity Based on Sensor Observations of Daily Activities
Organization Name
Inventor(s)
Britni A. Crocker of Santa Cruz CA (US)
Adeeti V. Ullal of Emerald Hills CA (US)
Ayse S. Cakmak of Santa Clara CA (US)
Johahn Y. Leung of San Francisco CA (US)
Katherine Niehaus of San Francisco CA (US)
William R. Powers, Iii of San Francisco CA (US)
Estimating Heart Rate Recovery After Maximum or High-Exertion Activity Based on Sensor Observations of Daily Activities - A simplified explanation of the abstract
This abstract first appeared for US patent application 17952147 titled 'Estimating Heart Rate Recovery After Maximum or High-Exertion Activity Based on Sensor Observations of Daily Activities
Simplified Explanation
The patent application describes a method for estimating heart rate recovery (HRR) after intense physical activity using sensor data from a wearable device worn on the wrist.
- The method involves obtaining sensor data and heart rate information from the wearable device.
- An observation window is identified to analyze the sensor data and heart rate during a specific time period.
- Input features for estimating maximum or near maximum exertion HRR are calculated based on the sensor data and heart rate.
- A machine learning model is used to estimate the maximum or near maximum exertion HRR during the observation window.
Potential applications of this technology:
- Fitness tracking devices: This method can be used in wearable fitness trackers to provide users with accurate heart rate recovery measurements after intense workouts.
- Sports performance monitoring: Athletes and coaches can use this technology to assess the effectiveness of training programs and track improvements in heart rate recovery.
- Health monitoring: Patients with cardiovascular conditions can benefit from this technology by monitoring their heart rate recovery and identifying any abnormalities or improvements.
Problems solved by this technology:
- Accurate heart rate recovery estimation: This method provides a more accurate and reliable way to estimate heart rate recovery after intense physical activity, compared to traditional methods.
- Real-time monitoring: The wearable device allows for continuous monitoring of heart rate and provides immediate feedback on heart rate recovery, enabling users to make informed decisions about their exercise intensity and recovery time.
- Personalized feedback: The machine learning model can be trained to provide personalized recommendations and feedback based on an individual's heart rate recovery patterns.
Benefits of this technology:
- Improved fitness tracking: Users can track their progress and set realistic goals based on their heart rate recovery measurements.
- Enhanced performance assessment: Athletes and coaches can use this technology to assess the effectiveness of training programs and make adjustments to optimize performance.
- Early detection of cardiovascular issues: Patients with cardiovascular conditions can monitor their heart rate recovery and detect any abnormalities or improvements, allowing for early intervention and better management of their condition.
Original Abstract Submitted
Embodiments are disclosed for estimating heart rate recovery (HRR) after maximum or high-exertion activity based on sensor observations. In some embodiments, a method comprises: obtaining, with at least one processor, sensor data from a wearable device worn on a wrist of a user; obtaining, with the at least one processor, a heart rate (HR) of the user; identifying, with the at least one processor, an observation window of the sensor data and HR; estimating, with the at least one processor during the observation window, input features for estimating maximum or near maximum exertion HRR of the user based on the sensor data and HR; and estimating, with the at least one processor during the observation window, the maximum or near maximum exertion HRR of the user based on a machine learning model and the input features.
