Mobile Device Tidal Volume Estimation via Dynamic Axis Selection
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Solution Overview
Problem
Conventional methods for monitoring tidal volume are invasive, uncomfortable, and inaccurate, often disrupting daily activities and failing to provide continuous, real-time measurements due to the need for cumbersome equipment and incorrect axis alignment of motion sensors on mobile devices.
Innovation Solution
The method dynamically identifies suitable motion-sensor axes in real-time to accurately determine tidal volume by analyzing motion signals from mobile devices, using static segmentation, ballistocardiogram signals, and machine learning to filter and process data, ensuring accurate breathing feature extraction regardless of device orientation or user posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring methods (spirometry, capnography, chest straps) are used, then tidal volume can be measured, but the methods are invasive, uncomfortable, and disruptive to daily activities
Solution Approach 1:
The patent replaces mechanical monitoring devices (mouthpieces, masks, chest straps) with a mobile device-based system that uses motion sensors to detect breathing movements. This substitution eliminates the need for invasive mechanical components while maintaining measurement capability through non-contact motion detection.
Solution Approach 2:
The system uses the mobile device's existing motion sensors to perform monitoring functions without requiring separate dedicated monitoring equipment. The device leverages its own built-in capabilities (accelerometers, gyroscopes) to detect breathing patterns, eliminating the need for additional wearable monitoring gear.
2Ease of operation
If motion sensors are placed on mobile devices to monitor breathing, then noninvasive monitoring is achieved, but accuracy is compromised due to incorrect axis alignment and device orientation
Solution Approach 1:
The system dynamically adjusts its analysis based on the mobile device's actual orientation and axis alignment. Rather than assuming fixed sensor orientations, the system adapts its processing to account for varying device positions and orientations, ensuring accurate breathing feature extraction regardless of how the device is held.
Solution Approach 2:
The system incorporates feedback mechanisms to continuously monitor and adjust for device orientation changes. By detecting the device's actual orientation and using this information to correct measurements, the system compensates for misalignment issues and maintains measurement accuracy throughout the monitoring period.
3Measurement precision
If dynamic axis selection and signal processing are implemented, then measurement accuracy is improved, but computational resources and processing complexity increase
Solution Approach 1:
The patent segments the signal processing into distinct functional stages: motion sensor data collection, breathing feature extraction, axis alignment correction, and tidal volume calculation. This segmentation allows each stage to be optimized independently and reduces overall computational complexity by processing only relevant information at each stage rather than analyzing all raw data simultaneously.
Solution Approach 2:
The system processes only the necessary portions of motion data related to breathing movements, filtering out irrelevant information from other body motions. By focusing computational resources on extracting only the breathing-related components from the motion signals, the system reduces unnecessary processing while maintaining accuracy.
4Productivity
If continuous real-time monitoring is implemented, then availability of breathing data is improved, but energy consumption and device battery drain increase
Solution Approach 1:
The system uses periodic sampling of motion data at optimized intervals rather than continuous processing. By determining appropriate sampling frequencies based on breathing characteristics and adjusting the monitoring cadence, the system maintains continuous availability of breathing data while reducing overall energy consumption compared to truly continuous processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables noninvasive, continuous, and accurate real-time tidal volume monitoring, reducing computational resources and improving accuracy by selecting the most relevant motion signals and axes, thus enhancing the detection of breathing conditions and related health indicators.
Implementation Method 1
detecting a plurality of motion signals from a motion sensor of a mobile device worn by the user
Implementation Method 2
determining a ballistocardiogram signal corresponding to the motion signal about the selected axis
Data Source
AI summary
In one embodiment, a method includes detecting, by a motion sensor of a mobile device worn by a user, multiple motion signals, each representing a motion of the user about one of a number of mobile-device axes defined by an orientation of the mobile device. The method further includes determining, for each of the multiple mobile-device axes, a ballistocardiogram (BCG) signal based on the motion signal corresponding to that mobile-device axis; selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating a user's tidal volume; determining, based on the one or more selected motion signals, one or more breathing features; and estimating, by providing the one or more breathing features to a trained machine-learning model, the user's current tidal volume.