Inertial Sensor Motion Detection for Portable Devices
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Solution Overview
Problem
Portable devices equipped with inertial sensors face challenges in distinguishing between meaningful and non-meaningful motion, leading to inaccurate navigation and motion tracking, particularly when users interact with the device while stationary or engage in non-locomotive activities.
Innovation Solution
A method and system that utilize inertial sensor data from accelerometers and gyroscopes to process and identify non-meaningful motion by isolating vertical and horizontal components, applying filtering operations, and employing algorithms to differentiate between meaningful and non-meaningful motion based on statistical and frequency analyses, as well as historical data and use case patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inertial sensors are used to detect motion in portable devices, then motion detection capability is improved, but false detection of non-meaningful motion increases
Solution Approach 1:
The patent segments the motion detection process into multiple independent analysis components: accelerometer data processing, gyroscope data processing, statistical analysis, and frequency analysis. Each component processes specific aspects of the sensor data separately, then their results are combined to make a final determination. This segmentation allows each component to specialize in detecting specific characteristics of meaningful versus non-meaningful motion, improving overall accuracy while reducing false positives.
Solution Approach 2:
The patent introduces an intermediary analysis layer between raw sensor data and motion detection output. This intermediary layer performs statistical and frequency analyses on the accelerometer and gyroscope data, creating intermediate metrics that characterize the nature of the detected motion. These intermediate metrics serve as mediators that help distinguish between meaningful locomotion and non-meaningful device interactions, reducing false detections while maintaining sensitivity to actual motion.
2Device complexity
If basic accelerometer data processing is used, then device complexity is reduced, but motion characterization precision deteriorates
Solution Approach 1:
The processing algorithm is segmented into distinct modular components: an accelerometer processing module, a gyroscope processing module, a statistical analysis module, and a frequency analysis module. Each module performs a specific function and can be independently optimized or adjusted. This modular segmentation manages complexity by organizing the processing pipeline into manageable, well-defined stages while maintaining high precision through specialized processing in each stage.
Solution Approach 2:
The patent transitions from analyzing motion in simple spatial dimensions to analyzing motion in multiple dimensions simultaneously. It processes both accelerometer data (linear acceleration) and gyroscope data (angular velocity) in three-dimensional space, then applies statistical and frequency analyses that operate in additional analytical dimensions. This multi-dimensional approach captures complex motion patterns that single-dimension processing would miss, significantly improving motion characterization accuracy without proportionally increasing perceived complexity.
Data Source
AI summary
This disclosure relates to detecting non-meaningful motion with a portable device. In one aspect, a suitable method includes detecting motion with a portable device by obtaining inertial sensor data representing motion of the portable device, wherein the inertial sensor data includes accelerometer data and gyroscope data, processing the accelerometer data, processing the gyroscope data and identifying non-meaningful motion of the portable device based, at least in part, on the processed accelerometer data and the processed gyroscope data.