Dual-Earbud Motion Sensing for Running Asymmetry Estimation
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Solution Overview
Problem
Existing devices struggle to accurately estimate movement dynamics using single-sided sensors, which are prone to noise and drift, and fail to provide comprehensive metrics like stride length imbalance and vertical oscillation asymmetry.
Innovation Solution
Utilizing dual-sided sensors, such as earbuds with IMUs and microphones on either side of the body, to analyze motion characteristics and apply machine learning models for precise running dynamics estimation, including footfall detection and digital signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-sided sensors are used to estimate movement dynamics, then device complexity is reduced, but measurement precision deteriorates due to noise and drift
Solution Approach 1:
The system divides the body into multiple sensing zones (first zone and second zone) with separate sensors in each zone. This segmentation allows independent measurement of movement characteristics for each zone, improving overall measurement precision by reducing the noise and drift issues associated with single-sided sensors through comparative analysis of multiple zones.
2Device complexity
If single-sided sensors are used, then device complexity is reduced, but comprehensive metrics like stride length imbalance and vertical oscillation asymmetry cannot be provided
Solution Approach 1:
The system transitions from single-sided to dual-sided sensor placement, adding a new spatial dimension to the measurement approach. By placing sensors in both a first zone and a second zone (opposite sides of the body), the system gains the ability to measure bilateral asymmetries and provide comprehensive metrics such as stride length imbalance and vertical oscillation asymmetry that were previously unobtainable.
3Measurement precision
If dual-sided sensors are used to improve measurement precision, then movement dynamics estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The sensors in both the first zone and second zone are configured to detect the same types of movement characteristics (acceleration, orientation, position). This multi-functionality allows the system to use identical sensor hardware across multiple zones, improving measurement precision through redundancy and cross-validation while minimizing the increase in device complexity by avoiding the need for specialized sensors for each zone.
Data Source
AI summary
Aspects of the subject technology obtain sensor data from microphone and or movement sensors disposed in a device on each side of a user's body. The sensor data is used to provide running dynamics estimations including estimations involving differences between movement characteristics of the user's right side of the body versus the user's left side of the body. The estimations can further be used to provide feedback to the user regarding running style, running routes, and injury information.