Millimeter-wave technology offers users detailed views of body parts through walls or other obstructions. This invention generates and measures an RF signal (millimeter wave) and processes the data with machine learning to extract and identify body motion within a region. Unlike other systems that focus on breathing or heart rate, this system can distinguish larger scale motions of specific limbs.
This detection system includes both hardware and computationally efficient algorithms to detect motion and interpret it in terms of human skeletal components. Similar patents focus on detecting motions through walls for security or detecting motions for computer-human interactions.
Background:
Currently, there are several technologies that are able to provide imaging of humans through a wall or another obstruction. These technologies rely on identifying vital signs like minor chest movements during breaths to detect humans, but these can be inaccurately recorded in complex environments. The tracking of skeletal structure motions would enable more detailed and accurate imaging.
Clothing is highly transparent in the millimeter-wave band of electromagnetic radiation which makes millimeter-wave sensors an obvious choice for security screening purposes. Passive and active millimeter wave systems are commonly used to detect concealed objects. However, the nature of this longer-wave technology far outside the visible wavelength range makes image processing of the data collected more intensive; the data must be heavily analyzed to create 2D images. Therefore, it is advantageous to use efficient algorithms for faster frame rates.
As an alternative to monitoring systems, some groups are investigating uses of RFID chips because they have become cheaper with longer lasting battery life. Yet, these require multiple chips for multiple motions and have applications more for specific medical purposes than remote monitoring.
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