Method, apparatus, and system for wireless sleep monitoring

A system for monitoring a sleep motion of a user in a venue, comprising:

• at least one sensor in the venue, wherein the at least one sensor comprises a non-contact sensor having no physical contact with the user;
• a processor communicatively coupled to the at least one sensor;
• a memory communicatively coupled to the processor;
• and
• a set of instructions stored in the memory which, when executed by the processor, causes the processor to perform:
• communicating a wireless signal in a wireless multipath channel of the venue in a non-contact manner based on the non-contact sensor;
• extracting a time series of channel information (TSCI) of the wireless multipath channel of the venue from the wireless signal;
• obtaining, based on the at least one sensor, a plurality of time series of sensing features (TSSF) associated with the sleep motion of the user in the venue, wherein:
• the plurality of TSSF comprises a first TSSF and a second TSSF,
• each sensing feature (SF) of the first TSSF comprises one of:
• a distance score, a Euclidean distance, a similarity score, a correlation, a covariance, or an inner product, based on two adjacent groups of channel information (CI) of the TSCI,
• the second TSSF comprises periodicity features associated with a periodicity of the sleep motion of the user,
• the second TSSF is associated with a breathing rate of the user,
• both the first TSSF and the second TSSF are obtained based on the non-contact sensor;
• and
• monitoring the sleep motion of the user jointly based on the plurality of TSSF,
• wherein monitoring the sleep motion of the user comprises:
• training a sleep classifier based on data related to breathing rate variance and breathing rate deviation, using machine learning,
• computing a first time function of breathing rate variance by computing variance of a detrended breathing rate function within a first sliding time window,
• computing a second time function of breathing rate deviation by computing a distance between an average non-rapid-eye-movement (NREM) breathing rate and a percentile of breathing rate within a second sliding time window,
• recognizing jointly, based on the sleep classifier, a sleep stage of the user as one of rapid-eye-movement (REM) or NREM based on the first time function of breathing rate variance and the second time function of breathing rate deviation.

A method of a sleep monitoring system, comprising:

• obtaining, based on at least one sensor in a venue, a plurality of time series of sensing features (TSSF) associated with a sleep motion of a user in the venue;
• communicating, based on a non-contact sensor having no physical contact with the user, a wireless signal in a wireless multipath channel of the venue;
• extracting a time series of channel information (TSCI) of the wireless multipath channel of the venue from the wireless signal, wherein:
• the plurality of TSSF comprises a first TSSF and a second TSSF,
• the second TSSF comprises periodicity features associated with a periodicity of the sleep motion of the user,
• the second TSSF is associated with a breathing rate of the user;
• and
• monitoring the sleep motion of the user jointly based on the plurality of TSSF,
• wherein monitoring the sleep motion of the user comprises:
• training a sleep classifier based on data related to breathing rate variance and breathing rate deviation, using machine learning,
• computing a first time function of breathing rate variance by computing variance of a detrended breathing rate function within a first sliding time window,
• computing a second time function of breathing rate deviation by computing a distance between an average non-rapid-eye-movement (NREM) breathing rate and a percentile of breathing rate within a second sliding time window,
• recognizing jointly, based on the sleep classifier, a sleep stage of the user as one of rapid-eye-movement (REM) or NREM based on the first time function of breathing rate variance and the second time function of breathing rate deviation.

A server device of a sleep monitoring system, comprising:

• a processor communicatively coupled to at least one sensor in a venue;
• a memory communicatively coupled to the processor;
• and
• a set of instructions stored in the memory which, when executed by the processor, causes the processor to perform:
• obtaining a plurality of time series of sensing features (TSSF) associated with a sleep motion of a user in the venue, wherein:
• each of the plurality of TSSF is associated with a respective sensor of the at least one sensor,
• the plurality of TSSF comprises a first TSSF and a second TSSF,
• the second TSSF comprises periodicity features associated with a periodicity of the sleep motion of the user,
• the second TSSF is associated with a breathing rate of the user,
• at least one of the first TSSF or the second TSSF is obtained based on a time series of channel information (TSCI) of a wireless multipath channel of the venue,
• the TSCI is extracted from a wireless signal communicated in the wireless multipath channel of the venue by one of the at least one sensor in the venue;
• and monitoring the sleep motion of the user jointly based on the plurality of TSSF,
• wherein monitoring the sleep motion of the user comprises:
• training a sleep classifier based on data related to breathing rate variance and breathing rate deviation, using machine learning,
• computing a first time function of breathing rate variance by computing variance of a detrended breathing rate function within a first sliding time window,
• computing a second time function of breathing rate deviation by computing a distance between an average non-rapid-eye-movement (NREM) breathing rate and a percentile of breathing rate within a second sliding time window,
• recognizing jointly, based on the sleep classifier, a sleep stage of the user as one of rapid-eye-movement (REM) or NREM based on the first time function of breathing rate variance and the second time function of breathing rate deviation.