Wi-Fi Sleep Tracking via Multipath Channel Analysis
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Solution Overview
Problem
Current sleep monitoring technologies, such as Polysomnography (PSG), photoplethysmography (PPG), and actigraphy, are invasive, expensive, and not suitable for public use, while mobile solutions provide coarse-grained, less accurate measurements and are undesirable for elders and those with dementia, especially in low-cost IoT devices with limited antenna and bandwidth.
Innovation Solution
A Wi-Fi-based sleep monitoring system that uses a transmitter and receiver to transmit and receive wireless signals through a wireless multipath channel impacted by sleeping motions, processing time series of channel information to compute motion statistics and monitor sleeping motions without the need for dedicated sensors or invasive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Polysomnography (PSG), photoplethysmography (PPG), and actigraphy are used for sleep monitoring, then measurement precision is improved, but device complexity and cost increase, making them invasive and expensive
Solution Approach 1:
The patent replaces mechanical and optical sensing systems (PSG with wired sensors, PPG with optical sensors, actigraphy with mechanical accelerometers) with a wireless radio-frequency-based system. The system uses Wi-Fi or similar wireless signals to detect sleep stages through changes in radio wave propagation characteristics caused by body movements and physiological signals during sleep, eliminating the need for invasive mechanical or optical sensors.
Solution Approach 2:
The patent introduces wireless radio signals as an intermediary medium between the sleep monitor and the subject. Instead of direct contact with sensors (which causes invasiveness), the system transmits radio waves through the environment and detects their interactions with the sleeping subject's body, using the radio wave as a non-invasive intermediary to gather sleep information.
2Ease of operation
If mobile solutions are used for sleep monitoring, then ease of operation is improved, but measurement precision deteriorates, providing only coarse-grained, less accurate measurements
Solution Approach 1:
The patent changes the fundamental measurement parameters from coarse mobile sensor data (accelerometer counts, simple heart rate) to fine-grained radio-frequency parameters including signal strength variations, phase information, frequency spectrum characteristics, and temporal patterns of radio wave interactions. This enables detection of subtle sleep stage transitions while maintaining the wireless, convenient operation of mobile devices.
3Device complexity
If low-cost IoT devices with limited antenna and bandwidth are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the measurement task by dividing the channel information into multiple components (in-phase and quadrature components, frequency bins, time-domain samples) and processing them through multiple analysis stages (spectral analysis, autocorrelation, cross-correlation, principal component analysis). This segmentation allows extraction of precise motion and sleep stage information from limited raw data collected by simple IoT devices.
Solution Approach 2:
The patent transforms limited one-dimensional sensor data into multi-dimensional analysis by converting signal strength measurements into spectral domains (frequency spectra), temporal domains (autocorrelation functions), and spatial domains (principal component analyses). This dimensional transformation extracts rich patterns from simple IoT device measurements, achieving high measurement precision without complex hardware.
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
Enables accurate and non-invasive sleep monitoring on low-cost IoT devices, effectively tracking sleeping motions and improving sleep quality assessment without the limitations of existing technologies.
Implementation Method 1
transmitting a first wireless signal through a wireless multipath channel in a venue; receiving a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion
Data Source
AI summary
Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1, computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.