Origin Research Wireless, Inc.

Weihang Gao

Beibei Wang

K J Ray Liu

Oscar Chi-Lim Au

Chenshu Wu

Guozhen Zhu

Yuqian Hu

Xiaolu Zeng

Sakila S Jayaweera

Methods, apparatus and systems for wireless sensing based on deep learning are described. For example, a described method comprises: transmitting a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue; receiving the wireless signal through the wireless multipath channel of the venue, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion of the object; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal; computing a plurality of autocorrelation functions based on the TSCI, each autocorrelation function (ACF) computed based on CI of the TSCI in a respective sliding time window; constructing at least one ACF vector, wherein each respective ACF vector is a vector associated with a respective ACF comprising multiple vector elements each associated with a respective time lag, each vector element being a value of the respective ACF evaluated at the respective time lag; rearranging the at least one ACF vector into rearranged ACF data, wherein each ACF vector is a one-dimensional (1D) ACF-block; and performing a wireless sensing task based on a task engine to do a processing using the rearranged ACF data as an input.

Method, apparatus, and system for wireless sensing based on deep learning

Wireless sensing for in-vehicle child presence detection is described. In one example, a described method comprises: obtaining a time series of channel information (TSCI) of a wireless channel in a vehicle; computing a motion statistics (MS) based on channel information (CI) of the TSCI in a time period; detecting a motion of a user in the time period by comparing the motion statistics with a first threshold, wherein the motion of the user is detected when the motion statistics exceed the first threshold; constructing a 2-dimensional (2D) transform matrix based on 1-dimensional (D) transforms of CI of TSCI in sliding time windows in the time period; detecting a number of horizontal streaks of the 2D transform matrix; detecting a breathing motion of the user in the time period based on the number of detected horizontal streaks of the 2D transform matrix and an associated criterion.

Wireless sensing for in-vehicle child presence detection

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Rockville, MD, United States of America

Weihang Gao