To estimate a user's posture, including a direction of the user's body, using a small number of sensors.SOLUTION: A posture estimation system comprises a measurement member 1 located at any part of four limbs of a user, and a posture acquisition part 520 for acquiring the posture of the...
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Attitude estimation system and attitude estimation method
Current Assignee:
MegaChips Corp
Abstract
To estimate a user's posture, including a direction of the user's body, using a small number of sensors.SOLUTION: A posture estimation system comprises a measurement member 1 located at any part of four limbs of a user, and a posture acquisition part 520 for acquiring the posture of the measurement member. The measurement member includes an acceleration sensor 14 and a gyro sensor 15. The posture acquisition part 520 includes a reference coordinate determination part 521 for setting a reference coordinate system of the measurement member based on the user's operation of making the measurement member face a target 3, and an attitude estimation part 522 for estimating an attitude of the measurement member relative to the target by acquiring detection values Da and Dr output from the acceleration sensor and the gyro sensor in response to the user's operation of changing the attitude of the measurement member.
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GPT analysis:
This patent describes a
posture estimation system that determines a user's body orientation using a
minimal number of sensors. It is primarily designed for
gaming, VR, fitness tracking, and motion-based interaction systems.
1. Purpose & Use
The system aims to estimate the
posture and orientation of a user’s body efficiently, using a
small number of sensors instead of a full-body motion capture setup. This is particularly useful for:
- Gaming – Motion-based gameplay using handheld controllers.
- Virtual Reality (VR) & Augmented Reality (AR) – Enhancing user movement tracking.
- Fitness & Rehabilitation – Monitoring body movement for training or therapy.
- Human-Computer Interaction – Intuitive gesture-based controls.
2. Sensor Technologies
The system uses two key
inertial sensors, embedded in a
measuring device (such as a handheld controller or a wearable limb sensor):
- Acceleration Sensor (Accelerometer)
- Measures movement acceleration in three axes (X, Y, Z).
- Helps determine tilt and linear motion.
- Gyro Sensor (Gyroscope)
- Measures rotational velocity in three axes (yaw, pitch, roll).
- Tracks rotational movement and orientation changes over time.
These sensors are typically placed in:
- Handheld controllers (left and right hands).
- Wearable devices (e.g., strapped to feet or arms).
- Potential expansion to lower body tracking (e.g., sensors on both hands and feet).
3. Processing Technologies & Processor Locations
The system processes sensor data at multiple levels, using different processors
located in the controllers and the game console.
A. Processing at the Controller Level (Embedded Processors)
Each
controller (or wearable sensor) contains an
onboard processor that performs
initial data collection and preprocessing:
- Location: Inside each controller (or wearable sensor).
- Functions:
- Collects acceleration and gyroscope data.
- Filters raw data to reduce noise.
- Performs preliminary sensor fusion to combine acceleration and rotational data.
- Communicates with the game console via wireless or wired connection.
B. Processing at the Game Console Level (Central Processing)
The
main computational processing happens inside the game console:
- Location: The game console’s central processor (CPU).
- Functions:
- Reference Coordinate System Setup
- The user performs a calibration motion, aligning the controllers to a fixed target (e.g., display screen).
- This sets a baseline reference coordinate system.
- Posture Estimation
- The console’s processor integrates accelerometer and gyroscope data from the controllers.
- Uses sensor fusion algorithms to track movement and correct drift.
- Common Coordinate Conversion
- Since each controller has an independent coordinate system, the console converts them into a unified coordinate system for consistent tracking.
- Machine Learning-Based Full Body Estimation
- The console’s processor runs a machine learning model to estimate full-body posture based on limited sensor data.
- The model is trained to predict shoulder, arm, and torso positions from hand-held controllers alone.
- Adaptive Motion Correction for Different Users
- The system adjusts for different body sizes by applying acceleration correction algorithms.
- Example: A child's arm will have different acceleration characteristics than an adult's, so the system scales acceleration values based on user height.
4. Advantages Over Traditional Systems
- Fewer sensors required (no need for full-body tracking suits).
- No waist-mounted sensors needed (orientation is inferred from hand-held devices).
- Cost-effective and power-efficient (less hardware, lower processing demands).
- Machine learning integration allows accurate full-body tracking with limited data.
- Adaptable for different users via automated motion scaling.
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. I will sell all my stocks for that SP and start a good life. 
, so a life in luxury should be possible. 
