Headset Motion Windows for Accurate Posture Transition Detection
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Solution Overview
Problem
Existing mobile devices lack the capability to accurately detect and classify user posture transitions, such as standing from a seated position, which is crucial for applications like head tracking, exercise repetition counting, and health monitoring.
Innovation Solution
A system and method using motion sensors, such as a headset with 3-axis MEMS accelerometer and gyro, to capture inertial vertical acceleration and torso rotation data, and employ classifiers like Naïve Bayes, deep learning, or motion template matching to classify posture transitions by analyzing windows of motion data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion sensors are used to detect user activities, then broad activity classifications can be obtained, but precise detection of posture transitions is not achieved
Solution Approach 1:
The patent segments the continuous motion data into discrete windows representing specific postural transitions. Each window captures a defined time period of motion data that can be independently analyzed to detect specific transitions such as sit-to-stand or stand-to-sit events, thereby improving detection precision without overwhelming complexity
Solution Approach 2:
The patent transforms raw motion sensor data into derived parameters including vertical displacement, acceleration thresholds, and temporal patterns. By changing the parameter representation from raw sensor values to meaningful motion characteristics, the system achieves precise posture transition detection while managing analytical complexity
2Measurement precision
If motion data windows are analyzed for posture transitions, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by pre-defining motion data windows and establishing acceleration thresholds before full analysis. This preliminary structuring of the data allows for faster subsequent classification of posture transitions, reducing overall processing time while maintaining high accuracy
Solution Approach 2:
The patent analyzes motion data in periodic time windows rather than continuously processing all data streams. This periodic approach processes discrete segments of motion data at defined intervals, achieving accurate posture transition classification while minimizing computational overhead and processing time
3Reliability
If multiple classifiers are used to classify posture transitions, then classification accuracy improves, but computational requirements increase
Solution Approach 1:
The patent dynamically selects and applies different classifiers based on the specific posture transition being detected. Rather than continuously applying all classifiers, the system adapts its classification approach to match the detected motion pattern, improving classification reliability while reducing energy consumption by activating only the necessary computational resources
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 precise detection and classification of posture transitions, providing context information for enhanced mobile applications, including posture ergonomics and health monitoring.
Implementation Method 1
The motion data includes measurements of the user's inertial vertical acceleration and rotation about the user's torso
Implementation Method 2
The motion data includes measurements of the user's inertial vertical acceleration and rotation about the user's torso
Data Source
AI summary
Embodiments are disclosed for user posture transition detection and classification. In an embodiment, a method comprises: obtaining, using one or more processors, motion data from a headset worn by a user; determining, using the one or more processors, one or more windows of motion data that indicate biomechanics of one or more phases of a user's postural transition; and classifying, using the one or more processors, as the user's postural transition based on the one or more windows of data.