Wearable Ring Optical Channel Selection via Motion-Weighted ML
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Solution Overview
Problem
Wearable devices face challenges in determining accurate measurement qualities of optical channels during user activities, as movement introduces noise that affects signal quality, making it difficult to select the optimal channels for data acquisition.
Innovation Solution
The implementation of a machine learning model that weighs different measurement quality metrics based on historical motion data, allowing the wearable device to identify the most suitable optical channels for data collection during activities by correlating collected motion data with historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple optical channels are used for data collection during activity, then measurement quality may be maintained, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the set of active optical channels based on real-time motion data and historical performance patterns. During high-motion activities, the machine learning model identifies and activates only those channels that have historically performed well under similar motion conditions, rather than using all channels statically. This dynamic adaptation maintains measurement quality while reducing the number of active channels to conserve power.
Solution Approach 2:
The system changes operational parameters by adjusting which optical channels are active based on motion intensity and type. The machine learning model analyzes motion data to determine optimal channel configurations, switching between different subsets of optical channels depending on the activity level. This parameter change allows the system to optimize between measurement quality and power consumption for different activity states.
2Measurement precision
If motion data is collected and processed to identify optimal channels, then data quality during activity improves, but device complexity increases
Solution Approach 1:
The machine learning model enables the device to automatically select optimal optical channels based on its own collected motion data and historical performance records. The system serves itself by using its internal sensors to detect motion patterns and autonomously determining which optical channels will perform best, eliminating the need for external intervention or complex manual configuration. This self-service approach improves data quality while keeping the control system relatively simple.
Solution Approach 2:
The system implements a feedback loop where motion data from sensors is continuously fed into the machine learning model, which then selects optimal optical channels based on this feedback. The model learns from historical motion data and measurement outcomes, continuously improving its channel selection accuracy. This feedback mechanism allows the system to adapt to different activity patterns while maintaining a manageable level of complexity through iterative learning rather than complex rule-based systems.
Data Source
AI summary
Methods, systems, and devices for wearable ring device are described. For example, a system may acquire first physiological data via multiple optical channels of a wearable ring device and may acquire motion data associated with the wearable ring device based on the user performing an activity. The system may input the motion data into a machine learning model to identify one or more optical channels of the multiple optical channels associated with a greatest measurement quality, the machine learning model including a set of measurement quality metrics that are weighted in accordance with historical motion data. In such cases, the set of measurement quality metrics may be weighted based on a correlation between the motion data and at least a subset of the historical motion data. The wearable ring device may collect second physiological data using the one or more identified optical channels based on the user performing the activity.