Activity Monitoring Sensor Alignment for Passive Motion Removal
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Solution Overview
Problem
Existing activity monitoring systems using accelerometers struggle to accurately distinguish between active and passive movement components, leading to inaccurate energy expenditure calculations, especially in environments where both components are comparable in magnitude, such as cycling on a rough road.
Innovation Solution
A system and method that utilize multiple motion sensors attached to different body parts to estimate gravitational components, determine a rotation matrix for sensor alignment, and subtract passive movement components from readout data, ensuring the extraction of true active movement-induced acceleration signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple motion sensors are used to eliminate passive movement components, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the measurement task across multiple motion sensors placed at different body locations. Each sensor captures a portion of the total movement, and the processor segments the signals to identify and eliminate passive movement components while preserving active movement information.
Solution Approach 2:
The processor acts as an intermediary that receives raw data from multiple motion sensors, applies algorithms to separate active and passive movement components, and produces corrected acceleration data. This intermediary processing layer enables the system to handle the complexity of multiple sensors while delivering precise measurements.
2Measurement precision
If passive movement components are eliminated through signal processing, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system applies different processing treatments to different components of the acceleration signal. Passive movement components identified through the rotation matrix are selectively eliminated, while active movement components are preserved. This local differentiation ensures that only the harmful passive components are removed without sacrificing valid active movement information.
Solution Approach 2:
The system changes the orientation parameters of the sensor data by applying a rotation matrix to transform the coordinate system. This parameter transformation enables the separation of passive and active movement components in a way that preserves the magnitude and characteristics of active movements while eliminating passive interference.
3Measurement precision
If rotation matrix calculation is performed for sensor alignment, then measurement precision is improved, but calculation time increases
Solution Approach 1:
The system performs preliminary calculations of the rotation matrix based on gravitational components before processing the full dataset. By pre-computing the alignment transformation, the system reduces the computational burden during real-time processing, thereby minimizing time loss while maintaining precise sensor orientation alignment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the reliability and robustness of activity monitoring systems by eliminating the impact of external motion factors, providing more accurate energy expenditure calculations across various environments and activities.
Implementation Method 1
When an accelerometer stays still, it measures the earth gravity g that is decomposed along its three sensing axes
Implementation Method 2
If the accelerometer starts to move, besides gravitational acceleration, also inertial acceleration is recorded that results from the movement
Data Source
AI summary
This invention relates to an activity monitoring system adapted to eliminate passive movement components caused by external forces from readout data produced by a first and a second motion sensor when attached to a subject during movement. The readout data include gravitational components, movement components caused by active movement of the subject or subject parts, and the passive movement components. A processor estimates first and second gravitational components produced by the at least first and a second motion sensors. It determines a rotation matrix based on the estimated gravitational components, the rotation matrix denoting rotation required for the first sensor to get aligned with the second sensor in orientation. It then multiplies the readout data produced by the first motion sensor with the rotation matrix when rotating the first sensor towards the second sensor. Finally, it subtracts the result of the multiplying from the readout data produced by the second motion sensor when rotating the first motion sensor towards the second motion sensor.