Multimode Biometric Sensor Adaptive Signal Processing
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Solution Overview
Problem
Current sensor devices face challenges in achieving high accuracy and convenience for biometric data collection due to limitations in activity types and intensities they can monitor, often requiring ideal placement locations that are not always convenient for users, and they struggle with energy efficiency due to their miniature size.
Innovation Solution
The implementation of multiple device modes that adjust based on motion intensity, placement location, and activity type, using time and frequency domain analyses to enhance computation speed and accuracy while maintaining energy efficiency, allowing for accurate biometric data tracking regardless of placement or activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor devices limit activity types and intensities to achieve high accuracy, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The system dynamically adapts its processing mode based on detected activity characteristics. It transitions between time-domain processing (for high-intensity activities with strong signals) and frequency-domain processing (for low-intensity activities with weak signals), allowing accurate biometric tracking across diverse activity types without requiring manual configuration or limiting activity scope
Solution Approach 2:
The system changes processing parameters based on signal characteristics. When signal-to-noise ratio exceeds a threshold, it uses time-domain analysis with shorter processing windows; when below the threshold, it switches to frequency-domain analysis with longer processing windows, thereby maintaining measurement precision across varying activity intensities and types
2Ease of operation
If sensor devices are placed in convenient locations, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system compensates for suboptimal placement by adapting processing parameters. For weak signals typical of convenient but non-ideal placements (e.g., wrist during low-intensity activity), it switches to frequency-domain processing with extended windows and spectral analysis, thereby maintaining measurement precision without requiring ideal placement locations
Solution Approach 2:
The system introduces an intermediate processing stage that characterizes signal quality and selects appropriate analysis methods. This intermediary classification mechanism bridges the gap between convenient placement and accurate measurement by automatically adjusting processing strategies based on actual signal conditions rather than assuming ideal placement
3Measurement precision
If sensor devices use high-speed computation to improve accuracy, then measurement precision is improved, but use of energy deteriorates
Solution Approach 1:
The system dynamically selects computation intensity based on activity characteristics. For high-intensity activities with strong periodic signals, it uses efficient time-domain processing with short windows; for low-intensity activities, it employs frequency-domain processing only when necessary, thereby optimizing the balance between computation speed, accuracy, and energy consumption
Solution Approach 2:
The system uses periodic processing with adaptive window lengths. Instead of continuous high-speed processing, it processes data in periodic batches with duration adapted to signal quality - shorter batches for strong signals, longer batches for weak signals - reducing overall energy consumption while maintaining accuracy
Data Source
AI summary
The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type. The device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process or the BMD applies a time domain analysis on data provided by a sensor of the BMD for a first activity, and applies a frequency domain analysis on the data for a second activity, which contributes to improved accuracy and speed of biometric data.