Heart Rate Correction via Dynamic Activity-Based Frequency Search
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Solution Overview
Problem
Wearable heart rate monitoring devices face challenges in accurately measuring heart rate during exercise due to signal contamination from motion, especially when motion frequencies match heart rate frequencies, leading to inaccurate heart rate measurements and difficulty distinguishing heart rate from background noise.
Innovation Solution
The device employs a dual estimation approach to correct heart rate signals, first through motion correction and zero-crossing analysis, and secondly by identifying highest occurrence frequencies in a frequency search window adjusted based on activity type, to select a candidate heart rate that is consistent with the user's activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion correction is applied to remove motion frequencies from the heart rate signal, then motion contamination is reduced, but when motion frequencies match heart rate frequencies, the heart rate signal itself is removed making heart rate difficult to determine
Solution Approach 1:
The system dynamically adjusts the frequency search window range based on detected activity type. During running activities, the search window is set higher (e.g., 120-200 bpm) to avoid motion frequency interference, while during walking or resting, a lower range is used. This dynamic adaptation allows the system to maintain accurate heart rate detection across different exercise intensities and motion conditions.
Solution Approach 2:
The system changes the parameters of the frequency search window (range and position) based on the detected activity type. By modifying these parameters dynamically, the system can distinguish between motion artifacts and actual heart rate signals even when they overlap in frequency, resolving the contradiction between motion correction and heart rate detection.
2Reliability
If standard motion correction techniques are used, then general motion contamination is mitigated, but harmonics of motion frequencies remain causing inaccurate heart rate measurements
Solution Approach 1:
The system moves from a one-dimensional approach (single frequency removal) to a two-dimensional approach by simultaneously considering both frequency domain characteristics and time-domain activity patterns. By analyzing the signal across multiple dimensions and comparing against activity-specific templates, the system can distinguish between fundamental motion frequencies and their harmonics, removing only the appropriate components while preserving the heart rate signal.
3Ease of operation
If a fixed frequency search window is used for heart rate estimation, then the measurement process is simple, but it cannot adapt to different activity types where motion frequencies align with heart rates
Solution Approach 1:
The system implements dynamic adaptation by automatically detecting the user's activity type (resting, walking, running, etc.) and adjusting the frequency search window parameters accordingly. This maintains ease of operation as the adjustment is automatic, while simultaneously improving adaptability to different activity conditions where motion frequencies may align with heart rates.
Solution Approach 2:
The system uses feedback from motion sensors and activity detection algorithms to continuously monitor exercise intensity and type, then feeds this information back to adjust the frequency search window parameters in real-time. This closed-loop approach maintains simplicity for the user while achieving high adaptability across different exercise conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method improves the accuracy of heart rate estimation by reducing false corrections and harmonics interference, providing a more reliable measurement even in low signal-to-noise scenarios and during activities where motion frequencies align with heart rates.
Implementation Method 1
an optical sensor configured to translate test light reflected from a wearer of the wearable heart rate monitoring device into a machine-readable heart rate signal
Implementation Method 2
a motion sensor configured to translate motion of the wearable heart rate monitoring device into a machine-readable motion signal
Data Source
AI summary
A wearable heart rate monitoring device includes an optical sensor configured to translate test light reflected from a wearer of the wearable heart rate monitoring device into a machine-readable heart rate signal. The wearable heart rate monitoring device also includes a motion sensor configured to translate motion of the wearable heart rate monitoring device into a machine-readable motion signal. The wearable heart rate monitoring device also includes a heart rate reporting machine, configured to determine a type of activity currently being performed by the wearer of the wearable heart rate monitoring device based at least in part on the machine-readable motion signal, and output an estimated heart rate based on at least the machine-readable heart rate signal and the type of activity.