Wearable Heart Rate Detection With Adaptive Motion Noise Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wearable devices with heart rate detection functions face challenges due to motion noise interference, leading to complex calculations and increased manufacturing costs, and difficulties in accurately determining heart rates during physical activity.
Innovation Solution
A wearable device with a heart rate detection assembly and processor that utilizes a finite state machine to determine heart rate states based on crest factors and variability, filtering out motion noise using a Wiener filter and adjusting sampling parameters to improve accuracy and reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calculations are used to filter motion noise from heart rate signals, then measurement precision is improved, but device complexity increases and manufacturing costs increase
Solution Approach 1:
The patent segments the heart rate signal processing into distinct stages: raw signal acquisition, motion noise identification through variability analysis, selective filtering application, and final heart rate calculation. This segmentation allows complex filtering operations to be applied only when and where needed, rather than continuously to all signals, reducing overall computational burden while maintaining accuracy when motion noise is present
Solution Approach 2:
The patent dynamically changes processing parameters based on signal characteristics. By calculating heart rate variability and comparing it against thresholds, the system adapts the level of filtering applied - using more aggressive filtering only when variability indicates motion noise presence. This parameter adaptation resolves the contradiction by adjusting computational intensity to match actual signal quality needs
2Measurement precision
If complex calculations are used to filter motion noise from heart rate signals, then measurement precision is improved, but manufacturing costs increase
Solution Approach 1:
The patent employs computationally inexpensive algorithms that can be implemented in low-cost microcontrollers. Rather than using expensive dedicated hardware or complex signal processing chips, the solution uses software-based variability analysis and adaptive filtering that runs efficiently on inexpensive processors, reducing manufacturing costs while maintaining adequate measurement precision
Solution Approach 2:
The system dynamically adjusts processing intensity based on signal quality metrics. When motion noise is detected through variability analysis, the system increases filtering aggressiveness; when signals are clean, it reduces processing. This adaptive approach allows the use of simpler, cheaper hardware that wouldn't be sufficient for continuous high-precision processing, thereby resolving the cost-precision tradeoff
3Measurement precision
If complex calculations are used to filter motion noise, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent divides signal processing into lightweight preliminary steps (variability calculation, threshold comparison) and heavier filtering operations. The segmentation ensures that computationally intensive filtering is applied only in brief intervals when motion noise is detected, rather than continuously, thereby maintaining measurement precision while preserving overall processing throughput and system responsiveness
Solution Approach 2:
The system applies filtering selectively rather than continuously - using partial action only when variability metrics indicate motion noise presence. This approach processes signals faster on average by skipping unnecessary filtering operations during clean signal periods, while still achieving high measurement precision when motion interference occurs, thus resolving the productivity-precision contradiction
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A wearable device with heart rate detection (100) includes a heart rate detection assembly (101) and a processor (102). The heart rate detection assembly (101) is configured to obtain a plurality of dynamic heart rate signals according to a default frequency. The processor (102) includes a finite state machine (103) and a storage (104). The storage (104) is configured to store the plurality of dynamic heart rate signals, and the finite state machine (103) determines a heart rate state according to a sampling parameter and the plurality of dynamic heart rate signals. The processor (102) obtains a weight according to the heart rate state, and obtains a dynamic heart rate value according to the weight.