Whoop has developed a method for assessing data quality in physiological monitoring using wearable devices. By comparing data from different devices, a machine learning model can evaluate the accuracy of heart rate data. This innovation improves the reliability of wearable health technology. GlobalData’s report on Whoop gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Whoop, Treatment progress monitoring was a key innovation area identified from patents. Whoop's grant share as of May 2024 was 11%. Grant share is based on the ratio of number of grants to total number of patents.
Quality estimation for heart rate data from wearable devices
A recently granted patent (Publication Number: US11986323B2) outlines a method for evaluating the accuracy of heart rate data obtained from photoplethysmography monitors compared to electrocardiography heart rate monitors. The method involves obtaining calibrated and uncalibrated heart rate data from subjects, along with feature data characterizing the data acquisition context. A quality metric is associated with the uncalibrated data based on its proximity to the calibrated data, and a quality estimator engine is created to assess the accuracy of the uncalibrated data. By analyzing feature data and heart rate measurements, the probability of accurate heart rate data from the photoplethysmography monitors is determined and assigned as a measure of quality for the data within a specific window of measurements. The method also includes providing feedback to users based on the quality assessment, potentially leading to adjustments in the positioning or tension of the photoplethysmography monitors for improved accuracy.
Furthermore, the patent describes the application of the method to wearable devices equipped with photoplethysmography sensors. These devices utilize a quality estimator engine to calculate the probability of accurate heart rate data relative to electrocardiography monitors based on feature data. By analyzing the distribution of values for the photoplethysmography data and corresponding feature data, the probability is assigned as a measure of quality for the data within a specific time window. The method also includes conditionally processing the data and providing user feedback based on the quality assessment, highlighting the potential for real-time monitoring and adjustment of heart rate measurements for improved accuracy and user experience.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
