Wearable Sensor Data Decoupling via Global Frame Transformation
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Solution Overview
Problem
Wearable sensors face accuracy and reliability issues due to their placement on the body, as different locations generate varying sensor data, making it challenging to accurately infer movements without specific positioning, which can be uncomfortable or impractical for users.
Innovation Solution
The system transforms sensor data from a local frame to a global frame using reference events and transformation parameters, such as quaternions or rotational matrices, to decouple sensor location from biomechanical features, allowing for accurate movement analysis regardless of sensor placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the wearable sensor is placed at a specific location on the user's body to ensure accurate movement detection, then the measurement precision is improved, but the ease of operation deteriorates due to comfort and cosmetic issues
Solution Approach 1:
The patent transforms sensor data from the sensor frame to the global frame using transformation parameters (quaternions or rotational matrices). This parameter transformation allows the system to maintain accurate biomechanical feature detection regardless of the sensor's physical location on the body, effectively decoupling measurement precision from placement constraints.
Solution Approach 2:
The patent creates a universal processing framework that can handle sensor data from any body location through frame transformation. The global frame representation makes the system location-agnostic, allowing the same device to accurately detect movements whether worn on the wrist, ankle, or other body parts, thereby improving user compliance without sacrificing precision.
2Ease of operation
If the wearable sensor is placed at different locations on the user's body, then the ease of operation is improved, but the measurement precision deteriorates due to varying sensor data quality
Solution Approach 1:
The patent applies parameter transformation (frame transformation) to convert sensor data from any location into a standardized global frame representation. This allows flexible sensor placement while maintaining consistent measurement quality, as the transformation parameters compensate for location-specific variations in the raw sensor data.
Solution Approach 2:
The global frame acts as an intermediary representation that mediates between diverse sensor locations and the final biomechanical analysis. By introducing this intermediate coordinate system, the patent enables data from any body location to be processed uniformly, maintaining measurement precision across different placement scenarios.
3Measurement precision
If the sensor data is processed using location-specific algorithms to maintain accuracy, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent replaces multiple location-specific algorithms with a single universal frame transformation approach. Instead of having different processing pipelines for wrist, ankle, or other locations, the system uses a unified global frame transformation that works for any sensor placement, thereby reducing device complexity while maintaining precision.
Solution Approach 2:
The patent extracts the location-specific information from the sensor data through frame transformation, separating the essential biomechanical features from the placement-dependent coordinate system. This extraction process simplifies the processing by removing the need for location-specific algorithmic adjustments.
4Measurement precision
If the wearable device is designed for specific body locations to ensure accurate step counting and movement analysis, then the measurement precision is improved, but the adaptability deteriorates
Solution Approach 1:
The patent uses parameter transformation to make step counting and movement analysis algorithms location-agnostic. By transforming sensor data into a global frame, the system maintains accurate step counting and biomechanical analysis regardless of whether the sensor is worn on the wrist, ankle, or other body locations, thereby improving adaptability without sacrificing precision.
Data Source
AI summary
Systems and techniques are disclosed for decoupling a wearable sensor's location on a user from the body movements desired to be measured by the sensor. Sensor data can be dynamically analyzed to determine a reference event (e.g., a static pose), which can be used to generate or update a transformation parameter (e.g., a quaternion or a rotational matrix). The transformation parameter can be applied to sensor data to transform the sensor data from the sensor's frame of reference to a global frame of reference. Once transformed, the movement data in the global frame can be analyzed to identify sensor-location-agnostic biomechanical features (e.g., body movements or movement trends), which can be further analyzed to identify probable or potential diagnoses correlated with the identified biomechanical features.