Sport Motion Capture with Sensor Drift Compensation
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Solution Overview
Problem
Current wearable technologies for motion detection in sports and fitness are limited in providing accurate and real-time analysis of complex motions, especially in high-speed scenarios, due to limitations in sensor data processing and integration with human biomechanics models.
Innovation Solution
A system comprising multiple sensor modules with inertial sensors, gyroscopes, and magnetometers, connected via a wireless protocol for real-time data streaming, using a cloud architecture for processing and analysis, which includes features like timestamp synchronization, error correction, and biomechanical modeling to compensate for sensor drifts and provide instant feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor modules are used to capture complex motions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the motion capture function into multiple distributed sensor modules (hub module and satellite modules), each equipped with inertial sensors. This segmentation allows comprehensive body motion tracking while maintaining modular architecture that simplifies individual module design and manufacturing.
Solution Approach 2:
Each sensor module is designed as a universal unit capable of capturing motion data from different body parts. The modules use standardized inertial sensors and communication protocols, allowing the same hardware design to be deployed across multiple locations on the body, reducing overall system complexity despite increased quantity.
2Speed
If real-time data streaming is implemented for high-speed motion capture, then speed of data processing is improved, but loss of information increases due to sensor drift
Solution Approach 1:
The hub module receives sensor data from multiple satellite modules and performs centralized processing with feedback mechanisms. This allows real-time drift compensation by comparing data across multiple sensors and using feedback loops to correct accumulated errors, maintaining accuracy during high-speed motion capture.
Solution Approach 2:
The system merges data from multiple inertial sensors across different modules, combining their measurements to compensate for individual sensor drifts. This fusion approach uses the redundancy of multiple sensors to cancel out drift errors while maintaining real-time processing capability.
3Productivity
If cloud architecture is used for processing data from thousands of users, then productivity is improved, but use of energy increases for data transmission
Solution Approach 1:
The system extracts and transmits only essential motion data to the cloud, performing preliminary processing and filtering at the edge devices (sensor modules and hub). This selective data extraction reduces transmission volume and energy consumption while maintaining the cloud's high processing capacity for complex analytics.
Solution Approach 2:
The system implements partial processing at the cloud level, focusing computational resources on specific analytics tasks rather than processing all raw data. This allows the cloud architecture to handle thousands of users efficiently while minimizing the energy required for continuous full-data transmission.
4Reliability
If timestamp synchronization and error correction are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs timestamp synchronization and error correction preliminarily at the source (sensor modules and hub) before data transmission. By pre-synchronizing timestamps and applying error correction codes in advance, the system reduces the need for complex post-processing and ensures data reliability without requiring overly complicated communication protocols.
Data Source
AI summary
Techniques for sport-specific training with captured body motions are described. A computing device is provided to receive sensing data wirelessly from a plurality of sensor modules respectively attached to different body parts of a user in accordance with a particular sport. The motion of the user per the received sensing data is derived. A corresponding 3D graphic environment pertaining to the sport is provided on a display to show reference motion performed by a reference chosen by the user with the derived motion performed by the user. Differences between the reference motion and the derived motion are highlighted or animated in the 3D graphic environment.