Ambulation Detection via Orthogonal Motion Component Analysis
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Solution Overview
Problem
Existing methods for detecting ambulation motion, particularly in elderly or sick individuals who walk slowly, are inaccurate due to reliance on comparison of acceleration magnitudes to thresholds, which fail to account for slow movements effectively.
Innovation Solution
A processing apparatus that extracts orthogonal motion components from high-pass filtered accelerometer data, compares their direction to the gravity component, and classifies motion as ambulation using source-separation techniques like blind source separation and independent component analysis, allowing for accurate detection of slow ambulation without prior knowledge of the gravity vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If acceleration magnitude comparison to thresholds is used for ambulation detection, then the detection method is simple, but the detection accuracy deteriorates for slow walking
Solution Approach 1:
The accelerometer data is segmented into orthogonal motion components (vertical, anterior-posterior, medio-lateral) that represent different directions of movement. This segmentation allows the system to analyze specific motion patterns characteristic of ambulation rather than relying on overall acceleration magnitude, thereby improving detection accuracy for slow walking while maintaining computational simplicity.
Solution Approach 2:
The invention transitions from analyzing acceleration magnitude (scalar) to analyzing motion components in multiple orthogonal dimensions (vector decomposition). By examining the directional characteristics of motion components relative to the gravity vector, the system can detect ambulation patterns more accurately, especially for slow walking where magnitude-based methods fail.
2Measurement precision
If source-separation techniques are used to extract motion components, then the precision of motion component identification is improved, but the computational complexity increases
Solution Approach 1:
The invention extracts orthogonal motion components from the raw accelerometer signal using source-separation techniques. By separating the vertical, anterior-posterior, and medio-lateral motion components, the system can identify ambulation-specific patterns with high precision. The extraction focuses on isolating the relevant motion signals from the composite accelerometer data.
Solution Approach 2:
The invention transforms the accelerometer data by applying high-pass and low-pass filters to isolate different frequency components, then decomposes the signal into orthogonal motion components. This parameter transformation approach converts the raw acceleration signal into meaningful motion characteristics that can be accurately classified for ambulation detection.
3Measurement precision
If gravity component determination is performed to compare motion directions, then the accuracy of ambulation classification is improved, but the processing time increases
Solution Approach 1:
The gravity component is determined in advance through low-pass filtering of the accelerometer data, establishing a reference direction before ambulation detection begins. This preliminary determination of the gravity vector allows for rapid comparison with motion components during actual ambulation detection, reducing real-time processing requirements while maintaining high classification accuracy.
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
Enables reliable detection of slow ambulation motions, improving the interpretation of vital signs and enabling auto-calibration, with enhanced precision and accuracy in identifying motion components, even in non-clinical settings.
Implementation Method 1
obtaining accelerometer data indicative of a trunk motion of the subject
Implementation Method 2
performing a high pass filter on the accelerometer data to obtain high-pass filtered accelerometer data
Implementation Method 3
performing a low-pass filter on the accelerometer data to obtain low-pass filtered accelerometer data; determining a gravity component from the low-pass filtered accelerometer data
Data Source
Figure 1~2
Figure 3~4B
Figure 5A~5F
AI summary
The present invention relates to a processing apparatus (10), system (1) and method (20) for determining an ambulation motion of a subject. Advantageously also a slow ambulation motion may be detected. The processing apparatus is configured to perform the steps of obtaining accelerometer data indicative of a trunk motion of the subject; extracting one or more orthogonal motion components from high-pass filtered accelerometer data; determining a gravity component from low-pass filtered accelerometer data; comparing a direction of at least one of said orthogonal motion components with a direction of the gravity component; and classifying a motion of the subject as ambulation if said motion component is orthogonal or parallel to said gravity component.