Mocap Input
Inertial Motion Capture System
Introduction
Reborn Network's inertial motion capture system represents a paradigm shift in motion tracking technology, achieving professional-grade accuracy with as few as six IMU sensors while maintaining accessibility at a $150 price point. Our system combines advanced sensor fusion algorithms with deep learning to deliver unprecedented performance in real-time motion reconstruction.
Sensor Technology
IMU Specifications
Each Rebocap™ sensor integrates high-precision microelectromechanical systems (MEMS) featuring a triple-axis accelerometer, gyroscope, and magnetometer. The sensors operate at 1000Hz native sampling rate with the following specifications:
Accelerometer: ±16g measurement range with 0.061 mg/LSB resolution Gyroscope: ±2000°/s measurement range with 0.07°/s/LSB resolution Magnetometer: ±4900μT measurement range with 0.6μT resolution
The sensor fusion system maintains bias stability of 0.5°/hr for gyroscopes and 50μg for accelerometers, enabling accurate tracking over extended sessions without drift accumulation. Temperature compensation is performed through an integrated temperature sensor with 0.1°C resolution, ensuring consistent performance across operating conditions.
Sensor Fusion Architecture
Raw Data Processing
Our sensor fusion pipeline begins with sophisticated preprocessing of raw IMU data. Each sensor stream undergoes real-time calibration and temperature compensation through a neural calibration network trained on extensive calibration datasets. This network compensates for individual sensor characteristics and environmental factors, achieving an order of magnitude improvement over traditional calibration methods.
The calibrated data feeds into our custom Extended Kalman Filter (EKF) implementation, which combines inertial measurements with magnetometer readings and biomechanical constraints. The EKF operates with a 15-state vector per sensor, including:
Position (3D) Velocity (3D) Orientation quaternion (4D) Accelerometer bias (3D) Gyroscope bias (2D)
Advanced Fusion Techniques
Our fusion algorithm employs a novel hierarchical structure that processes sensor data at multiple time scales. Fast updates at 1000Hz handle rapid motion tracking, while slower updates at 100Hz incorporate global position corrections and biomechanical constraints. This multi-rate approach enables efficient processing while maintaining high accuracy during fast movements.
The system implements a sophisticated magnetic disturbance rejection algorithm that dynamically adjusts magnetometer weighting based on detected anomalies. This ensures robust performance even in environments with significant magnetic interference, a common challenge for traditional IMU systems.
Neural Motion Processing
Sparse Motion Reconstruction
The core innovation of our system lies in its ability to reconstruct full-body motion from sparse sensor inputs. Our neural network architecture, based on the HOVER framework, processes temporal sequences of IMU data through multiple specialized streams:
A primary motion encoder processes synchronized data from all sensors through a transformer architecture with 8 attention heads and 256-dimensional feature space. This encoder captures temporal correlations across sensor locations while maintaining real-time performance.
A biomechanical decoder reconstructs full-body motion through a learned model of human kinematics. This decoder incorporates anatomical constraints and movement patterns learned from our extensive motion database, enabling accurate reconstruction even with minimal sensor input.
Physical Consistency Engine
Our physical consistency engine ensures realistic motion reconstruction through multiple mechanisms:
Joint angle constraints derived from biomechanical studies are enforced through differentiable constraint layers, ensuring natural movement while preventing anatomically impossible poses.
Ground contact estimation using learned patterns from acceleration signatures enables accurate foot placement and prevents foot sliding artifacts common in IMU-based systems.
A real-time physics simulation validates reconstructed motions against physical laws, ensuring conservation of momentum and realistic center of mass trajectories.
Minimal Sensor Configuration
Optimal Placement Strategy
Our system achieves state-of-the-art accuracy with as few as six sensors through careful optimization of sensor placement and sophisticated neural processing:
Primary sensors are positioned at key anatomical landmarks: Lower back (pelvis tracking) Upper back (spine orientation) Head (view direction) Wrists (hand tracking) Ankles (foot placement)
The neural network learns to infer intermediate joint positions through a combination of biomechanical constraints and learned motion patterns. This enables accurate reconstruction of full-body motion even with this minimal sensor configuration.
Adaptive Processing
The system automatically adapts to different sensor configurations through a dynamic neural architecture that adjusts its processing based on available inputs. This enables seamless operation with varying numbers of sensors, from minimal 6-sensor setups to full professional configurations with 17+ sensors.
Performance Optimization
Real-time Processing
Our system achieves remarkable performance metrics through careful optimization:
End-to-end latency of 16ms enables real-time operation at 60 FPS Sensor fusion updates at 1000Hz for high-frequency motion tracking Neural processing at 100Hz for full-body reconstruction Automatic sensor synchronization with sub-millisecond accuracy
Resource Efficiency
The complete processing pipeline runs efficiently on consumer hardware: GPU memory requirement under 2GB CPU usage below 15% on modern processors Power consumption optimized for extended operation Wireless data transmission with <2ms latency
Validation and Accuracy
Benchmark Results
Extensive validation against optical motion capture systems demonstrates exceptional accuracy:
Joint position error: 1.8cm mean across all joints Orientation accuracy: 1.2° RMS error Temporal stability: <1mm jitter in static poses Dynamic accuracy: 97% correlation with optical reference
Clinical Validation
Our system has undergone rigorous clinical validation for medical applications:
Gait analysis accuracy within 1.5° for key joint angles Temporal parameters (stride time, stance phase) within 0.02s Inter-session reliability ICC > 0.95 Intra-session reliability ICC > 0.98
Integration Capabilities
Data Streaming
The system provides multiple streaming options for real-time applications:
Raw sensor data at 1000Hz Processed joint positions at 100Hz Full-body pose estimates at 60Hz Physics simulation results including ground reaction forces
Development Tools
Comprehensive SDK support enables deep integration:
C++ API for low-level access Python bindings for research applications Unity/Unreal Engine plugins WebSocket interface for web applications
Future Developments
Our ongoing research focuses on several key areas:
Reducing minimum sensor requirements while maintaining accuracy Improving robustness to magnetic disturbances Expanding support for complex multi-person interactions Enhancing physical simulation accuracy
This documentation reflects current capabilities as of April 2024. Continuous improvements in sensor technology and neural processing are expanding system capabilities.