Torso-mounted inertial sensor step detection
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Solution Overview
Problem
Existing torso-mounted inertial sensors face performance issues when the mounting location changes, leading to inaccurate step detection and motion analysis due to varying motion signatures, particularly in decomposing displacement into rotations and translations during pedestrian movements.
Innovation Solution
A method utilizing only accelerometer signals to estimate hip elevation and compute relative translation direction, combined with gyro information for rotation calculation, which is robust to changes in torso mounting location, enabling accurate step detection and scaling regardless of sensor orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If inertial sensors are mounted at different locations on the torso, then the system becomes more versatile and adaptable to different wearing conditions, but the motion signatures change and performance deteriorates
Solution Approach 1:
The system dynamically adjusts detection parameters based on the mounting location. By identifying whether the sensor is worn on the front, back, or side of the torso, the system modifies its step detection thresholds and motion signature expectations accordingly, allowing accurate operation regardless of where the sensor is positioned on the body.
2Productivity
If a fixed predefined step length is used for distance computation, then the system is simpler and faster, but measurement precision deteriorates when motion varies
Solution Approach 1:
The system transitions from using a fixed predefined step length to dynamically estimating step length based on the actual motion detected by the inertial sensors. The step length is computed in real-time using the sensor data, allowing the system to adapt to varying walking speeds, terrains, and motion patterns while maintaining both speed and accuracy.
3Reliability
If threshold-based step detection is used to eliminate false detections, then false positives are reduced, but soft steps are missed
Solution Approach 1:
Instead of using a single high threshold that might miss soft steps, the system employs multiple detection thresholds at different sensitivity levels. This allows the system to detect both strong and weak step signals, ensuring that soft steps are not missed while still filtering out false detections through the multi-level thresholding approach.
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 allows for reliable detection of steps and estimation of movement direction, including non-forward motions, and corrects errors caused by sensor orientation changes, providing detailed and accurate path tracking without requiring complex motion classifiers or additional sensors like GPS.
Implementation Method 1
The method can use only accelerometer signals and works robustly with a torso mounted location
Implementation Method 2
The cumulative rotation over a path is computed using gyro information
Data Source
AI summary
A method for detecting a human's steps and estimating the horizontal translation direction and scaling of the resulting motion relative to an inertial sensor is described. When a pedestrian takes a sequence of steps the displacement can be decomposed into a sequence of rotations and translations over each step. A translation is the change in the location of pedestrian's center of mass and a rotation is the change along z-axis of the pedestrian's orientation. A translation can be described by a vector and a rotation by an angle.