Gyroscope Vibration Signal Upsampling via Sensor Interleaving
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Solution Overview
Problem
Current devices equipped with gyroscope sensors, such as smartphones, face challenges in accurately detecting and processing mechanical vibrations caused by signals, particularly audio signals, due to limited sampling frequencies that result in unintelligible speech and reduced spectral component resolution.
Innovation Solution
A system comprising multiple mobile devices with gyroscope sensors and processors that interleave and process vibration signals using machine learning filters, specifically neural networks, to generate an upsampled signal with a higher sampling frequency, effectively aligning and weighting peak vibration signals to enhance signal fidelity and intelligibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gyroscope sensors sample mechanical vibrations at a fixed sampling frequency, then the device can detect vibrations, but the sampling frequency is limited resulting in unintelligible speech and reduced spectral component resolution
Solution Approach 1:
The patent combines vibration signals from multiple gyroscope sensors spaced apart in space to generate a composite upsampled signal. By merging data from multiple sensors and interleaving their sampled signals, the system achieves an effective sampling frequency higher than any individual sensor's sampling rate, thereby improving spectral component resolution and speech intelligibility.
2Productivity
If multiple mobile devices are used to sense vibrations, then the effective sampling rate increases, but the system complexity increases
Solution Approach 1:
The system divides the sensing task across multiple mobile devices, each equipped with a gyroscope sensor. Each device independently samples vibrations at its own sampling frequency, and the signal processor later interleaves these segmented signals to create the upsampled composite signal, thereby achieving high effective sampling rates without requiring a single complex high-speed sensor.
3Productivity
If vibration signals from multiple devices are interleaved to generate upsampled signal, then the sampling frequency increases, but signal alignment and processing complexity increases
Solution Approach 1:
The signal processor uses feedback mechanisms to align vibration signals from multiple devices by comparing timestamps and adjusting for temporal offsets. This feedback-based alignment ensures that signals are properly synchronized before interleaving, maintaining signal integrity while achieving the desired upsampling effect.
4Measurement precision
If machine learning filters are applied to process the upsampled signal, then signal fidelity and intelligibility improve, but computational requirements increase
Solution Approach 1:
The system performs preliminary signal processing steps including interleaving and alignment of vibration signals from multiple devices before applying machine learning filters. This preliminary action prepares the signal in advance, reducing the computational burden on the neural network and lowering energy requirements while maintaining high signal fidelity and intelligibility.
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
The system significantly improves the accuracy and intelligibility of detected signals, allowing for the reproduction of human speech and analysis of audio features, such as speaker identification, by increasing the effective sampling rate and enhancing spectral resolution.
Implementation Method 1
The gyroscope sensor is configured to sense mechanical vibrations caused by signals originating within a vicinity of a mobile device
Data Source
AI summary
Gyroscope data can be used to generate upsampled signal. Multiple mobile devices are spaced apart from each other in a spatial arrangement. Each mobile device includes a gyroscope sensor to detect mechanical vibrations caused by signals originating within a vicinity of a mobile device that includes the gyroscope sensor. Each mobile device includes one or more respective processors to receive representations of the mechanical vibrations sensed by the gyroscope sensor at a sampling frequency, and transmit the representations received at the sampling frequency as a respective vibration signal associated with sampling times. The signal processor is coupled to the multiple mobile devices. The signal processor generates a processed upsampled signal by interleaving the vibration signal received from each mobile device and processing the interleaved signal using one or more machine learning filters, and transmitting the processed upsampled signal.