Physiological Sensor Fusion for Accurate Time-to-Completion Prediction
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Solution Overview
Problem
Current fitness trackers that employ only one type of sensor are unable to predict performance with the same level of accuracy as needed for precise time-to-completion (TTC) measurements in structured physical activities.
Innovation Solution
The system employs physiological sensor fusion, combining signals from heart rate, temperature, accelerometers, and other sensors, along with trained artificial intelligence (AI) models to predict TTC for users engaged in structured activities like ruck marches or cycling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of physiological sensors are fused together, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple types of physiological sensors (heart rate monitor, accelerometer, temperature sensor, etc.) into a single integrated wearable device. This merging approach allows the system to collect and process multiple physiological signals simultaneously, improving prediction accuracy while managing device complexity through unified hardware architecture and centralized processing.
Solution Approach 2:
The wearable device is designed with multi-functionality, where a single device performs multiple functions including heart rate monitoring, motion detection, temperature sensing, and data processing. This universal approach enables the device to handle diverse sensor types and processing requirements without requiring separate specialized devices for each function.
2Measurement precision
If physiological sensor fusion is implemented, then time-to-completion prediction accuracy is improved, but manufacturing cost increases
Solution Approach 1:
By merging multiple sensor types into a single integrated wearable device, the system reduces the need for multiple separate devices and their associated manufacturing processes. The integrated approach allows for shared components, unified processing architecture, and streamlined production, thereby managing manufacturing costs while achieving accurate TTC predictions through sensor fusion.
3Measurement precision
If GPS is used for performance tracking, then location-based accuracy is improved, but system reliability decreases in environments lacking GPS signals
Solution Approach 1:
The patent introduces physiological sensors as an intermediary measurement approach to complement or replace GPS-based tracking. By using heart rate, motion, and temperature data as intermediaries to infer performance metrics, the system can maintain reliability in environments where GPS signals are unavailable, such as indoor settings or areas with dense foliage, while still providing accurate location-based tracking when GPS is available.
Data Source
AI summary
An exemplary system and method employing physiological sensor fusion and trained artificial intelligence models to predict/estimate a time-to-completion for a user undergoing a structured activity. The time-to-completion may be determined at a given segment of the activity as defined only by the physiological sensor measurement. The exemplary system and method may provide individualized fitness information for a customized military training regimen or athlete programs, to provide comprehensive and localized snapshots of physical performance based on the entire history of the event/activity.