Wearable Breathing Pattern Tracking From Foot Strike Synchronization
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Solution Overview
Problem
Current wearable devices do not monitor or provide feedback on breathing patterns, which are critical for optimizing performance and reducing injuries in activities like running, walking, cycling, and swimming, and there is a need for a convenient, low-cost device to teach, train, and track breathing techniques.
Innovation Solution
A wearable device with a breathe in-breathe out sensor and movement sensor to detect diaphragmatic breathing patterns, providing immediate feedback and data tracking, and integrating with other sensors like accelerometers and gyroscopes to identify rhythmic breathing patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices track basic parameters like steps and heart rate using existing sensors, then monitoring capability is improved, but breathing pattern monitoring capability remains insufficient
Solution Approach 1:
The patent applies multi-functionality by enabling existing wearable device sensors (accelerometers, gyroscopes, pressure sensors) to serve dual purposes: their original functions (step counting, motion detection) plus breathing pattern monitoring. The sensor fusion algorithm processes sensor data to simultaneously track both general movement and specific diaphragmatic breathing patterns, allowing one device to perform multiple monitoring functions without adding dedicated breathing sensors.
Solution Approach 2:
The patent replaces the need for specialized mechanical breathing sensors with computational methods. Instead of using dedicated pressure or flow sensors to detect breathing, the system uses algorithmic processing of data from existing motion sensors to infer breathing patterns. This substitution of mechanical sensing with computational analysis reduces device complexity while maintaining measurement capability.
2Measurement precision
If dedicated breathing sensors are added to wearable devices, then breathing monitoring accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The patent substitutes specialized breathing detection hardware with computational algorithms that process data from existing sensors. The sensor fusion technique combines signals from accelerometers, gyroscopes, and pressure sensors to detect diaphragmatic breathing patterns without requiring dedicated breathing sensors, thereby maintaining accuracy while reducing hardware complexity.
Solution Approach 2:
The patent creates a virtual model of breathing patterns by processing and analyzing sensor data to generate breathing rate and rhythm information. Instead of directly measuring breathing with specialized sensors, the system creates a computational representation of breathing patterns that mirrors what dedicated sensors would detect, achieving the same functional outcome with existing hardware.
3Measurement precision
If multiple sensors are integrated to monitor breathing patterns, then monitoring capability is improved, but data processing complexity increases
Solution Approach 1:
The patent replaces complex multi-sensor integration with a unified algorithmic approach. Instead of processing separate data streams from multiple specialized sensors, the system uses a single sensor fusion algorithm that simultaneously processes data from existing sensors to identify breathing patterns, simplifying the data processing architecture while maintaining monitoring precision.
Solution Approach 2:
The sensor fusion algorithm serves multiple functions: it processes sensor data for general motion tracking and simultaneously extracts breathing pattern information. This multi-functional processing approach allows the same computational routine to handle both generic activity monitoring and specific breathing analysis, reducing overall data processing complexity.
4Productivity
If breathing feedback is provided in real-time, then performance optimization is improved, but device power consumption increases
Solution Approach 1:
The patent replaces power-intensive dedicated breathing sensors with computational analysis of existing sensor data. The sensor fusion algorithm processes low-power sensor inputs to generate breathing feedback, achieving real-time performance optimization with minimal additional energy consumption since the primary processing workload already exists for general activity tracking.
Data Source
AI summary
A wearable device and system has been developed to help users learn the practices of diaphragmatic breathing and breathing patterns to improve running and walking performance. The wearable has a breathing sensor (breathe in-breathe out) and a movement sensor used to identify foot strikes. A processor computes the number of foot strikes occurring while inhaling and the number of foot strikes occurring while exhaling to report breathing patterns as a function of time. The algorithms may be modified to teach breathing patterns to athletes in other sports and deep breathing for numerous movement and minimal movement applications.