Apparatus, system and method for detecting physiological movements from audio and multimodal signals

Abstract

A method and device for physiological movement detection with active voice generation is provided. [Solution] A mobile device (100) is configured with an application (200) for detecting the movement of a subject (110). A processor of the mobile device detects the subject's breathing and / or whole-body movement, generates an audio signal in the vicinity of the subject via a connected speaker, and controls sensing of a reflected audio signal via a connected microphone. The reflected audio signal is a reflection of the audio signal from the subject. The processor also processes the reflected audio using demodulation techniques and detects breathing from the processed signal. The audio signal is generated as a series of tone pairs or phase-continuous repetitive waveforms with frequency changes (e.g., triangle or sawtooth) in a frame of slots. The processor further determines sleep state or scoring, fatigue symptoms, object recognition, chronic disease monitoring / prediction, and other output parameters by evaluating the detected movement information.

Description

[Technical Field]

[0001] 1 Cross-reference to related applications This application is a joint venture of U.S. Provisional Patent Application No. 62 / 396,616 (filed September 19, 2016). ), the entire contents of which are incorporated herein by reference.

[0002] 2. Technical Background 2.1 Technology field The present technology relates to detecting body movements associated with a living body. More particularly, the present technology relates to detecting body movements associated with a living body. biological movements (e.g., breathing, cardiac, and / or other low-periodic biological movements) Regarding the use of voice sensing. [Background technology]

[0003] 2.2 Description of Related Art For example, monitoring a person's breathing and body movements (including limbs) while they sleep can For example, such monitoring can be useful in determining whether a sleep disorder breathing condition (e.g., This may be useful in monitoring and / or diagnosing sleep apnea. The barrier to adoption for line positioning or ranging applications is the need for specialized hardware circuits and antennas. There are times when you need a

[0004] Smartphones and other portable electronic communication devices cannot use landlines It has become a common part of daily life, even in developing countries. A method for monitoring physiological activity (i.e., physiological activity) in an efficient and effective manner without the need for specialized equipment. Implementation of such a system and method would address significant technical challenges. That is the thing. Summary of the Invention

[0005] 3. Brief description of the technology The present technology provides systems, methods, and apparatus for detecting movement of a subject, for example, while the subject is sleeping. Based on such motion detection (e.g., breathing motion, subject motion), , sleep-related characteristics, sleep states and / or apnea events may be detected. ,mobile apps related to mobile devices (e.g., smartphones, tablets) In applications, mobile device sensors (e.g., , integrated and / or externally connectable speakers and microphones) are used. do.

[0006] Some versions of the technology may include a processor-readable medium. The medium stores processor-executable instructions. These instructions are executed by the processor. When executed, the processor detects physiological movements of the user. The instruction directs the generation of an audio signal in the user's vicinity via a speaker connected to the electronic processing device. The processor executable instructions may include instructions to control a voice signal reflected from a user. The instruction to control the sensing of the signal via a microphone connected to an electronic processing device. The processor-executable instructions may include instructions to process the sensed audio signal. The processor-executable instructions include instructions for detecting a respiration signal from the processed audio signal. It can be seen.

[0007] In some versions, the audio signal may be in the inaudible range. The audio signal may include a sequence of frames. Each frame of the sequence may contain a series of tone pairs. In the example, each tone pair is associated with a respective time slot within a frame. The first frequency and the second frequency may be different. The first frequency and the second frequency may be orthogonal to each other. The tone pair may include a first tone pair and a second tone pair. The frequency of the first tone pair may be different from the frequency of the second tone pair. The slot may have zero amplitude at the beginning and end, and the peak between the beginning and end may be zero. may have ramping amplitudes from the peak amplitude to the beginning and end .

[0008] In some versions, the duration of a frame may vary. The time width may be the width of a frame. The sequence of color pairs forms different frequency patterns for different slots of the frame. The different frequency patterns can be repeated in multiple frames. The number pattern varies for different frames of the slot during multiple frames of the slot. It can become.

[0009] In some versions, the instructions to control the generation of the audio signal are given as timbral pair frames. The command to control the detection of the reflected sound signal from the user may include a frame modulator. A frame buffer may be included. The command to process the sensed voice signal reflected from the user The instruction may include a demodulator that generates one or more baseband motion signals that include the respiration signal. The demodulator may generate a plurality of baseband motion signals. The interferometer may include a baseband motion signal.

[0010] In some versions, the processor-readable medium comprises a plurality of baseband processors. The plurality of baseband motion signals may include processor executable instructions for processing the plurality of baseband motion signals. The processor executable instructions for processing a motion signal are assembled from a plurality of baseband motion signals. an intermediate frequency processing module and a maximum frequency processing module for generating a combined baseband motion signal; The combined baseband motion signal may include a respiration signal. In some versions, the command to detect a breathing signal is combined The method may include determining the respiration rate from the baseband motion signal. In this configuration, the duration of each time slot in a frame is determined by the frequency difference between the tone pairs. is equal to the sum of the two.

[0011] In some versions of the technology, the audio signal contains a repetitive waveform with varying frequency. The repetitive waveform can be phase continuous. The repetitive waveform with varying frequency can be sawtooth, triangular, etc. and a sinusoidal waveform. and further comprising processor-executable instructions including instructions to vary one or more parameters of the configuration. The one or more parameters may include any one or more of the following: (a) (b) the peak position of the repeating portion of the repeating waveform, (b) the slope of the sloping portion of the repeating portion of the repeating waveform, and (c) The frequency range of the repeating portion of the repeating waveform. In some versions, The repeating portion can be a linear or curved function that varies the frequency of the repeating portion. In some versions, the frequency-varying repeating waveform may include a symmetric triangular waveform. In some versions, the command to control the generation of the audio signal shows the waveform of a repeating waveform. It contains instructions to loop the audio data reflected from the user. The command to control the audio is to save the audio data sampled from the microphone. The instructions to control the processing of the sensed audio signal may include instructions to control the processing of the generated audio signal. , and may include instructions to correlate the sensed audio signal to verify synchronization.

[0012] In some versions, the instruction to process the sensed audio signal is based on the respiratory signal. The downconverter may generate data including the generated The downconverter may mix the signal indicative of the audio signal with the sensed audio signal. The output of the signal indicative of the detected audio signal and the mixture of the sensed audio signal may be filtered. The downconverter mixes a signal representative of the generated audio signal with the sensed audio signal. The downconverter may generate a windowed filtered output. Windowed filtering of a mixture of a signal representative of a signal and a sensed audio signal. The output frequency domain transform matrix may be generated. The function command is to convert multiple channels of the Data Matrix generated by the downconverter. The amplitude and phase information may be extracted from the further processor-executable instructions for computing a plurality of features from the data matrix. The plurality of features may include any one or more of: (a) full-band metrics and (b) the in-band metric, (c) the kurtosis metric, and (d) ) frequency domain analysis metrics. The processor-executable instructions for detecting a respiratory signal include: The respiration rate may be generated based on multiple features.

[0013] In some versions, the processor-executable instructions may include: and further comprising instructions to calibrate the detection by evaluating one or more characteristics of the electronic processing device. The processor-executable instructions further include instructions to generate an audio signal based on the evaluation. The instructions to calibrate the audio-based detection may include calibrating at least one hardware The processor executable instructions may determine environmental or user specific characteristics. The frequency for generating the audio signal may further include instructions to activate a tap mode. , may be selected based on user input to generate one or more test audio signals.

[0014] In some versions, processor-executable instructions are instructions that are used to interface with an electronic processing device. The method may further include instructions to stop generating the audio signal upon detection of user interaction. The user interaction may include any one or more of the following: Chair movement detection, button press detection, screen contact detection, incoming call detection. The executable instructions include generating an audio signal based on detecting an absence of user interaction with the electronic processing device. The instruction may further include an instruction to start

[0015] In some versions, the processor-executable instructions are reflected from the user: The method may further include instructions to detect whole body movement based on processing of the sensed audio signal. In some versions, the processor-executable instructions are sensed through a microphone. The received audio signal is processed to evaluate any one or more of environmental sounds, conversational sounds, and breathing sounds. In some versions, the instruction may further include instructions to detect user movement by evaluating the In the method, the processor-executable instructions include: The system may further include instructions to process signals: (a) a sleep state indicating sleep; (b) a wakefulness state indicating wakefulness; sleep state, (c) sleep stages indicating deep sleep, (d) sleep stages indicating light sleep, and (e ) A sleep stage that indicates REM sleep.

[0016] In some versions, the processor-executable instructions are stored locally on the electronic processing device. detection of audio frequencies and selection of frequency ranges of audio signals that differ from the detected audio frequencies It may also include instructions to activate a setup mode that takes place. In this application, the command to activate the setup mode is superimposed on the detected audio frequency. A frequency range that is not duplicated may be selected.

[0017] In some versions of the technology, the server may include a processor as described herein. The server can access any of the processor-readable media. The processor-executable instructions on the medium(s) may be transmitted over a network to an electronic processing device. The device may be configured to receive a request to download the program to the device.

[0018] In some versions of the technology, a mobile electronic device or electronic processing device The system consists of one or more processors, speakers connected to one or more processors, and one or more a microphone connected to the processor and a signal read by the processor described herein; The present invention may include any processor-readable medium.

[0019] In some versions of the technology, a processor-readable The method includes a processor having access to any of the available media. The processor-executable instructions of the processor-readable medium are transmitted to the electronic processing device via a network. The method may include receiving at the server a request to download the data to the device. may also transmit processor-executable instructions to the electronic processing device in response to a request. It may include.

[0020] In some versions of the technology, a mobile electronic device is used to detect body movements. The method is implemented by a processor as described herein. The method may include accessing, with a processor, any of the readable media. , processor-executable instructions on a processor-readable medium(s), This may include executing on a processor.

[0021] In some versions of the technology, a mobile electronic device is used to detect body movements. The method includes a processor connected to a mobile electronic device to output a The method may include controlling generation of an audio signal in the user's vicinity via a speaker attached to the speaker. The microphone connected to the mobile electronic device receives a reflected sound signal from the user. The method may include controlling the sensing via a sensed reflected sound signal. The method may include processing the reflected audio signal to extract a respiratory signal from the processed reflected audio signal. The method may include detecting.

[0022] In some versions of the technology, a mobile electronic device is used to move and call. A method for detecting the absorption of a mobile electronic device is used. The method includes transmitting the information via a speaker on the mobile electronic device. The method may include transmitting the reflected audio signal to a mobile device. This may include sensing the reflected audio signal via a microphone on the electronic device. The method detects breathing and movement signals from the reflected audio signal. The audio signal may be an inaudible audio signal or an audible audio signal. In some versions, the method includes, prior to transmission, applying an FMCW modulation scheme, an FHRG modulation scheme, and modulation scheme, AFHRG modulation scheme, CW modulation scheme, UWB modulation scheme or A Optionally, the method may include modulating the audio signal using one of a CW modulation scheme. Alternatively, the audio signal may be a modulated low frequency ultrasonic audio signal. In some versions, the reflected When an audio signal is sensed, the method may include demodulating the reflected audio signal. The demodulation of the reflected audio signal is performed by filtering the reflected audio signal and then demodulating the filtered reflected audio signal. and synchronizing the timing of the received and transmitted audio signals. In some versions, generating the audio signal comprises: and performing a calibration function for evaluating at least one characteristic of the audio signal, and generating an audio signal based on the calibration function. The calibration function may include creating a calibration parameter for at least one of the hardware, the environment, or the user. The filter operation can be configured to determine the characteristic characteristics of the filter. may include:

[0023] In some versions of this technology, methods for detecting movement and breathing are used. The method may include generating an audio signal directed to the user. The method may include sensing a reflected audio signal from the laser. This may include detecting breathing and movement signals from the audio signal. In this context, generating, transmitting, sensing and detecting are Optionally, the bedside device may be a treatment device. (e.g., a CPAP device).

[0024] The methods, systems, devices and apparatus described herein allow a processor to functionality (e.g., general-purpose or special-purpose computers, portable computer processing units ( e.g., mobile phone, tablet computer, etc.), respiratory monitor and / or microphone improves the processor capabilities of microphones and other breathing devices that utilize speakers Furthermore, the described methods, systems, devices and apparatus may and / or automatic management, monitoring and / or prevention of sleep conditions (e.g., sleep apnea) Improvements in the technical field of evaluation are possible.

[0025] Of course, some of the above aspects may form sub-aspects of the present technology. and / or various combinations of the various aspects may be used to provide further aspects of the present technology. Or it may constitute a sub-embodiment.

[0026] Other features of the present technology are included in the following detailed description, abstract, drawings, and claims. This becomes clear in light of the information available. [Brief explanation of the drawings]

[0027] The present technology is illustrated by way of example and not by way of limitation in the accompanying drawings, in which like reference numerals refer to: contains the following similar elements:

[0028] [Figure 1] FIG. 1 illustrates an exemplary processing device for receiving audio information from a sleeper that may be suitable for implementing the processes of the present technology. [Figure 2] FIG. 2 is a schematic diagram of a system according to one embodiment of the present technology. [Figure 2A] FIG. 2A is a high-level architectural block diagram of a system for implementing aspects of the present technology. [Figure 3] FIG. 3 is a conceptual diagram of a mobile device configured in accordance with some aspects of the present technology. [Figure 4] FIG. 4 shows an exemplary FHRG sonar frame. [Figure 5] FIG. 5 shows an example of an AFHRG transceiver frame. [Figure 6A] An example of a pulse-driven AFHRG frame is shown. [Figure 6B] An example of a pulse-driven AFHRG frame is shown. [Figure 6C] An example of a pulse-driven AFHRG frame is shown. [Figure 7] Figure 7 shows (A) a conceptual diagram of the FHRG architecture. [Figure 8] FIG. 8 shows an example of an isotropic omnidirectional antenna versus a directional antenna. [Figure 9] Figure 9 shows an example of a sinc filter response. [Figure 10] FIG. 10 shows an example of the attenuation characteristics of a sinc filter. [Figure 11]FIG. 11 is a graph illustrating an example of a baseband respiratory signal. [Figure 12] FIG. 12 shows an example of initial synchronization with a training sound frame. [Figure 13] FIG. 13 shows an example of an AFHRG frame using an intermediate frequency. [Figure 14] FIG. 14 shows an example of an AToF frame. [Figure 15A] 1 shows signal characteristics of an example audible version of an FMCW gradient sequence. [Figure 15B] 1 shows signal characteristics of an example audible version of an FMCW gradient sequence. [Figure 15C] 1 shows signal characteristics of an example audible version of an FMCW gradient sequence. [Figure 16A] 1 shows signal characteristics of an example FMCW sinusoidal profile. [Figure 16B] 1 shows signal characteristics of an example FMCW sinusoidal profile. [Figure 16C] 1 shows signal characteristics of an example FMCW sinusoidal profile. [Figure 17] FIG. 17 shows the signal characteristics of an exemplary audio signal (eg, inaudible audio) in the form of a triangular waveform. [Figures 18A-18B] 18A and 18B illustrate demodulation of the inaudible triangle emitted from the loudspeaker of the smart device of FIG. 17 into a detected respiration waveform using an upward gradient subprocess shown in FIG. 18A and a downward gradient subprocess shown in FIG. 18B. [Figure 19] FIG. 19 illustrates an exemplary method for FMCW processing flow operators / modules (including "2D" (two-dimensional) signal processing). [Figure 20] FIG. 20 illustrates an exemplary method for a downconversion operator / module as part of the FMCW processing flow of FIG. [Figure 21] FIG. 21 illustrates an exemplary method for a 2D analysis operator / module that may be part of the FMCW processing flow of FIG. [Figure 22]FIG. 22 illustrates how absence / presence detection is performed by the absence / presence operator / module. [Figure 23A] FIG. 23A shows several signals on a graph illustrating sensing activity over time at various detection ranges in a "2D" segment of data where a person moves in and out of the various sensing ranges of device 100. [Figure 23B] FIG. 23B shows a portion of the signal in the graph of FIG. 23A, showing region BB from FIG. 23A. [Figure 23C] FIG. 23C shows a portion of the signal in the graph of FIG. 23A, showing region CC from FIG. 23A. DETAILED DESCRIPTION OF THE INVENTION

[0029] Before describing the present technology in more detail, it is important to note that the present technology is not limited to the different methods described herein. It should be understood that the present disclosure is not limited to the specific examples that may be used. The terminology used herein is for the purpose of describing the specific embodiments described herein. It should also be understood that this is not limiting.

[0030] The following description is provided in connection with various aspects of the present technology that may share common characteristics or features. One or more features of any one aspect may be combined with one or more features of another aspect or other aspects. It should be understood that combinations are possible. Any single feature or combination of features in any of these may be used in further exemplary embodiments. can be configured.

[0031] 5.1 Screening, surveillance and diagnosis The present technology can detect movements of a subject, for example, when the subject is sleeping (e.g., breathing movements and / or The present invention relates to a system, method and apparatus for detecting heart-related chest motion. Based on the detection of breathing and / or other movements, the subject's sleep state and apnea events are identified. More specifically, a mobile device (e.g., a smartphone, a tablet) To detect such activity in mobile applications related to the Mobile device sensors (e.g., speaker and microphone) are used for this.

[0032] An exemplary system suitable for practicing the present technology will now be described with reference to FIGS. 1-2, as best shown in FIG. 3, the mobile device 100 or mobile electronic device is a target 110. The object 110 is configured with an application 200 for detecting the movement of the object 110. The mobile device 100 may be placed on a bedside table next to the The processor may be a smartphone or tablet having the above processor. or multiple) may be configured specifically to perform the functions of application 200 ( For example, as a generally open or unrestricted medium (e.g., in a room nearby the device) Typically, an audio signal is generated and transmitted through the air, and the transmitted signal is then transmitted to a The signal reflection is received by sensing with a transducer (e.g., a microphone) and processing the received signals to determine body movement parameters and respiratory parameters). The mobile device 100 may include, among other components, a speaker and a microphone. The speaker uses the generated audio signal and a microphone to receive the reflected signal. Optionally, the voice-based sensing of the mobile device The detection method may be used with other types of devices (e.g., bedside devices (e.g., respiratory therapy devices) chair (e.g., continuous positive airway pressure (e.g., "CPAP") devices or high-flow therapy The method may be performed in or by a device. Examples of devices include pressure devices or blowers (e.g., motors and and impeller), one or more sensors and a central control unit for the pressure device or blower Regarding this, International Patent Publication WO / 2015 / 061848 (Application No. PCT / AU2014 / 050315) (Application date: October 28, 2014) and International Patent Publication WO / 2016 / 145483 (Application number PCT / AU2016 / 050117) (Application date: 2016 The devices described in the patent application Ser. No. 10 / 199,349, filed on March 14, 2003, which is incorporated herein by reference in its entirety, may be considered. Use.

[0033] FIG. 2A is a high-level architectural block diagram of a system for carrying out aspects of the present technology. The system may be implemented, for example, in a mobile device 100. 10 may be configured to generate an audio signal that is transmitted to a subject. As such, the signal may be audible or inaudible. For example, Thus, the audio signal, in some versions, is in the low frequency ultrasonic range (e.g., about 1 Generated in the range 7 kilohertz (KHz) to 23 KHz or approximately 18 KHz to 22 KHz For the purposes of this specification, such frequency ranges are generally considered to be inaudible ranges. The generated signal may be transmitted via transmitter 212. According to the present disclosure, transmitter 21 2 can be a mobile phone speaker. After the generated signal is transmitted, sound waves (e.g., The sound waves reflected from the elephant can be sensed by the receiver 214. It can be a talking microphone.

[0034] As shown at 216, a signal recovery and analog-to-digital signal conversion step is performed. The recovered signal is demodulated, e.g., in a demodulation processing module 218. A signal indicative of a respiratory parameter such as that shown in Figure 1 can be extracted. The audio component of the signal can be extracted, e.g. It can also be processed as shown in 220 in a voice processing module. Processing may include, for example, passive audio to extract breathing or other motion sounds. There are analyses (e.g., different types of activity (e.g., turning over in bed), PLM (periodic leg movements), RLS (restless legs syndrome), snoring, panting, and wheezing. Voice processing eliminates interference sources (e.g., conversations, TV, other media playback, and other ambience) The output of the demodulation phase at 218 can also be extracted from the audio of the signal (e.g., ambient audio / noise sources). In-band processing at 2 (e.g., by an in-band processing module) and 22 4. Out-of-band processing (e.g., by an out-of-band processing module) ) can be exposed to both respiratory and cardiac signals. For example, in-band processing at 222 The out-of-band processing at 224 may be directed to that portion of the signal band that includes other Components (e.g., whole body movements or fine movements) (e.g., rolling over, kicking, gesturing, moving around the room) The in-band signal at 222 can then be directed to the portion of the signal that contains the signal (movement around the area). The output of the out-band processing at 224 is then passed to signal post-processing at 230 (e.g. For example, in one or more signal post-processing modules Signal post-processing at 230 may include, for example, the respiration / cardiac signal at 232. signal quality in processing (e.g., in a respiratory / cardiac signal processing module), 234 processing (e.g., in a signal quality processing module), whole body motion processing in 236 (e.g., in the body movement processing module), and absence / presence processing at 238 There are processes (for example in the absence / presence processing module).

[0035] Although not shown in FIG. 2A, the output of the signal post-processing at 230 is passed to a second post-processing stage. The second post-processing stage may include sleep state, sleep scoring, Fatigue signs, object recognition, chronic disease monitoring and / or prediction, sleep disorder respiratory event detection and and other output parameters (e.g., motion characteristics of the generated motion signal(s)). from any assessment (e.g., breathing-related movements (or absence of such movements), cardiac In the example of Figure 2A, 241 Optional sleep staging processing in the sleep staging processing module (e.g., However, any of these processing modules / blocks One or more of the following may optionally be added (e.g., sleep scoring or staging, fatigue symptoms, etc.) Predictive processing, object recognition processing, chronic disease monitoring and / or prediction processing, sleep disorder respiratory event detection (or other output processing). In some cases, signal post-processing at 230 or second The post-processing stage functions are implemented by the apparatus, system, or method described in any of the following patents or patent applications: The present invention may be carried out using any of the components, devices and / or methods of the present invention. The disclosures of each document herein are incorporated by reference in their entirety: International Patent Application No. PCT / US20 07 / 070196 (Application date: June 1, 2007, Title: "Apparatus, System, and Method for Monitoring Physi "Ophthalmological Signs," International Patent Application No. PCT / US2007 / 083155 (Application date: October 31, 2007, Title: "System and Method for Monitoring Cardio-Respiratory Param eters,” International Patent Application No. PCT / US2009 / 058020 (filing date: 2009 September 23, 2017, Title: "Contactless and Minimal-Con tact Monitoring of Quality of Life Param eters for Assessment and Intervention”, country International Application No. PCT / US2010 / 023177 (filing date: February 4, 2010, title : “Apparatus, System, and Method for Chro nic Disease Monitoring”, International Patent Application No. PCT / AU201 3 / 000564 (Application date: March 30, 2013, Title: "Method and Apparatus for Monitoring Cardio-Pulmona ry Health”, International Patent Application No. PCT / AU2015 / 050273 (filing date: May 25, 2015, Title: "Methods and Apparatus f or Monitoring Chronic Disease”, International Patent Application No. PC T / AU2014 / 059311 (Application date: October 6, 2014, Title: "Fat igue Monitoring and Management System”, country International Patent Application No. PCT / AU2013 / 060652 (filing date: September 19, 2013) Title: “System and Method for Determining S sleep Stage”, International Patent Application No. PCT / EP2016 / 058789 (filing date :April 20, 2016, Title: "Detection and Identifi cation of a Human from Characteristic Si gnals”, International Patent Application No. PCT / EP2016 / 069496 (filing date: 2016 April 17th, Title: "Screener for Sleep Disorder "ed Breathing", International Patent Application No. PCT / EP2016 / 069413 (issued Application date: August 16, 2016, Title: "Digital Range Gated Radio Frequency Sensor”, International Patent Application No. PCT / EP201 6 / 070169 (Application date: August 26, 2016, Title: "Systems and dMethods for Monitoring and Management of Chronic Disease,” and U.S. Patent Application No. 15 / 079,339 (Application date: March 24, 2016, Title: "Detection of Perio Thus, in some instances, the detected motion Processing (e.g., respiratory activity) as a criterion for determining any one or more of the following: It can function as: (a) a sleep state indicative of sleep, (b) a sleep state indicative of wakefulness, and (c) a deep sleep state. (d) sleep stages indicating light sleep, and (e) sleep stages indicating REM sleep. In this regard, the audio-related sensing techniques of the present disclosure utilize different mechanisms / Processes (e.g., using speakers and microphones and processing audio signals) Although providing a method for detecting radar or RF sensing technology as described in these incorporated references, In comparison, a respiratory signal (e.g., obtained using the audio sensing / processing methods described herein) After the initial processing of respiratory or other movement signals for extraction of sleep state / stage information, The process can be carried out by determining the extent to which these incorporated references are used.

[0036] 5.1.1 Mobile Devices 100 The mobile device 100 may be configured to: When used during sleep, the The mobile device 100 and associated methods detect the user's breathing and determine sleep stages, sleep state, and and for identifying transitions between sleep, disordered breathing, and / or other respiratory states. When used during waking hours, the mobile device 100 and its associated methods may, for example, The subject's respiratory movements (inspiratory, expiratory, resting, and expiratory rates) and / or ballistocardiogram waveforms and Such parameters can be used in the detection of the game and the subsequent derived heart rate. (To guide the user to reduce their breathing rate for relaxation purposes) of) game control or chronic diseases (e.g., COPD, asthma, congestive heart failure (CHF) F)) may be used to assess the respiratory status of a subject prior to the occurrence of a deterioration / decompensation event. Direct respiratory parameter(s) vary over time. The respiratory waveform is a temporal representation of the respiratory Arrest (e.g., central apnea or chest obstruction due to airway blockage (as occurs in obstructive apnea) small movements of the lungs) or hypopnea (e.g., shallow breathing and / or breathing associated with hypopnea) It can also be processed to detect slowdowns.

[0037] The mobile device 100 may include integrated chips, memory and / or other control instructions, data, and For example, the assessment / signal processing described herein may include a computer or information storage medium. The programmed instructions containing the method form an application specific integrated chip (ASIC) The information may be coded on an integrated chip in the memory of the device or apparatus. The instructions may additionally or alternatively be stored as software or flash memory using a suitable data storage medium. Optionally, such processing instructions may be loaded as firmware. Downloaded from a server via a network (e.g., the Internet) to a mobile device The instructions may be loaded into the processing device so that, when executed, the processing device Acts as a monitoring device.

[0038] Thus, the mobile device 100 may include multiple components as shown in FIG. The device 100 may include, among other components, a microphone or audio sensor 3 02, processor 304, display interface 306, user control / input interface interface 308, speaker 310, and memory / data storage 312 (e.g., (using the processing instructions of the processing method / module described in the specification).

[0039] One or more of the components of the mobile device 100 may be integrated into the mobile device 100. For example, a microphone or sound sensor may be connected to the The server 302 may be integrated into the mobile device 100 or may be connected to a wired router, for example. Mobile devices via a wired or wireless link (e.g., Bluetooth, Wi-Fi, etc.) The device may be coupled to a vise 100.

[0040] The memory / data storage unit 312 includes a plurality of processor controls for control of the processor 304. For example, memory / data storage 312 may include instructions for controlling the processing methods described herein. Processor control for executing application 200 by means of method / module processing instructions It may include commands.

[0041] 5.1.2 Movement and Breathing Detection Process Examples of the present technology may be configured to use one or more algorithms or processes. These algorithms or processes may affect the ability of the user to fall asleep while using the mobile device 100. The application detects movement, breathing, and optionally sleep characteristics while asleep. For example, the application 200 may be implemented as several sub-applications. As shown in Figure 2, an application can be characterized by a process or module. The process 200 includes an audio signal generation and transmission subprocess 202, a motion and biophysical The characteristic detection subprocess 204, the sleep quality characterization subprocess 206, and the result output subprocess Process 208 may be included.

[0042] 5.1.2.1 Audio signal generation and transmission According to some aspects of the present technology, the audio signal may include one or more of the tones described herein. Tones can be generated and transmitted to a user using a medium (e.g. For the purposes of this description, the generated tone (or audio signal) A signal (or audio signal) can be generated as an audible pressure wave (e.g., by a speaker), It may be called "voice," "acoustic," or "sound." However, as used herein, such pressure The modifications and timbre(s) are not used in conjunction with the terms "voice," "acoustic," or "audio." Regardless of whether they are characterized as audible or inaudible, Thus, the generated audio signal can be audible or inaudible and is suitable for the human population. The frequency threshold of audibility changes with age. Typical "voice frequency" standard range The threshold for high-frequency hearing is approximately 20 Hz to 20,000 Hz (20 kHz). For middle-aged people, sounds with frequencies above 15-17 kHz tend to decrease with age. In most cases, teenagers can hear 18kHz. The most important frequencies for a typical consumer smartphone are approximately 250 to 6,000 Hz. The speaker and microphone signal response of smartphones often ranges from 19 to 20 kHz. Designed to roll off above 23kHz Some are designed to do this (especially at sampling rates above 48 kHz (e.g. For example, for devices that support 96kHz. can remain inaudible while using signals in the 17 / 18 to 24 kHz range. Yes. For younger people who can hear 18kHz but cannot hear 19kHz, For some domestic pets, a higher frequency band of 21 kHz may be used. They can also hear frequencies (for example, up to 60 kHz for dogs and 79 kHz for cats). Please note that the

[0043] The audio signal may include, for example, a sinusoidal waveform, a sawtooth chirp, a triangular chirp, etc. For illustrative purposes, the term "chirp" as used herein refers to a short-term non-stationary signal. signal, which may have, for example, a sawtooth or triangular shape, and the profile may be linear or non-linear. Several types of signal processing methods are used for generating and sensing audio signals. (e.g., continuous wave (CW) homodyne, pulse-driven CW homodyne, frequency-modulated C W (FMCW), Frequency Hopping Range Gating (FHRG), Adaptive FHRG (A FHRG), Ultra-Wideband (UWB), Orthogonal Frequency Division Multiplexing (OFDM), Adaptive CW, Frequency Phase Shift Keying (FSK), Phase Shift Keying (PSK), Biphasic Phase Shift Keying Quadrature Phase Shift Keying (BSPK), Quadrature Phase Shift Keying (QPSK) and Generalized QPSK This is called QAM (quadrature amplitude modulation).

[0044] According to some aspects of the present technology, a calibration function or module, a mobile device It may be provided for characterization of hardware, environment or user setup. Any body movement within the sensing area, if a frequency is required, is suggested by the calibration function. on user-acceptable spread spectrum signals while enabling active detection of Encoding can be overlaid, e.g., audible audio (e.g., music, TV, streaming) a breathing source or other signal) (e.g., a comfort signal that may optionally be synchronized with the detected breathing signal) audible perception during loud, repetitive sounds (e.g., waves crashing on shore, which can be used to aid sleep) This allows the user to "hide" a signal or an inaudible signal. This is the basis for audio steganography. In audio steganography, the message that needs to be made perceptually indistinguishable is The message may be encoded using techniques such as phase encoding (where the phase elements are adjusted to represent the encoded data). It is concealed in the audio signal (by

[0045] 5.1.2.2 Motion and biophysical signal processing 5.1.2.2.1 Technical issues Considers several room acoustic issues to improve motion signal to environment-related noise ratio Such issues include reflexes, bedclothes usage, and and / or other room acoustic issues. In addition, certain surveillance device characteristics (e.g., mobile The directionality of the device or other monitoring devices must also be taken into consideration.

[0046] 5.1.2.2.1.1 Reflection When sound energy is confined within a space (e.g., a room) that contains reflective walls, reflections occur. Typically, when a sound is first produced, the listener perceives the sound as coming from the source itself. The user then hears the voice bouncing off the walls, ceiling, and floor of the room. Discrete echoes caused by the voice can be heard. Over time, individual reflections become indistinguishable. As a result, what the listener hears is a succession of reflexes that weaken over time.

[0047] At the frequencies of interest, the energy absorption due to wall reflections is often very small. Therefore, sound attenuation occurs due to the walls and not the air. Typical attenuation can be <1%. The time it takes for audio attenuation of audio frequencies to reach 60 dB in a typical room. is about 400ms. The attenuation increases with frequency. For example, at 18kHz ,Typical indoor reflection times drop to 250ms.

[0048] Reflections can cause some side effects, such as indoor mode. Due to the reflective energy conservation mechanism, when an input of acoustic energy enters a room, it is reflected back Standing waves are generated at the vibration modal frequencies or preferred modal frequencies (nλ=L). For an ideal three-dimensional room with dimensions Lx, Ly and Lz, the dominant mode is given by You get this.

number

[0049] Due to these standing waves, the volume of a particular resonant frequency will vary at different locations in the room. As a result, the signal level may change, and the sensing component receiving the audio signal (e.g., Fading can occur in connection with radio waves (e.g., RF) (e.g., This is similar to the multipath interference / fading experienced by Wi-Fi (e.g., Wi-Fi). However, it also includes reflections from rooms and and related artifacts (e.g., fading) become more severe in audio.

[0050] Indoor mode can present some sonar system design issues. These include: For example, there are fading, 1 / f noise and whole body motion signals. Typically, large physiological movements (e.g., turning over in bed or the user getting out of bed) In one realization, the action refers to the presence or absence of an event. can be considered as a binary vector (with or without motion) and associated The activity index refers to the duration and intensity of the movement (e.g., PLM, RLS, or bruxism). The pain is associated with whole body movements (such as clenching of the limbs or jaws) (This is an example of movement activity that is not a sound, but rather a whole-body movement when changing position in bed.) In the signal received by the voice sensor 302 (e.g., microphone), the signal strength Fading of the signal and / or reflected signals from the users may occur. The cause of this fading may be standing waves caused by reflections, and the result of this fading is This can lead to problems with varying signal amplitude. The signal with a power spectral density inversely proportional to the wave number is the room airflow that disturbs the room modes. This can occur due to an increase in the noise floor, resulting in a decrease in the respiration frequency. Signal / noise reduction occurs. Whole body motion signals from any movement in the room are filtered out by the room mode amplifier. This can result in energy disturbances, leading to perceived / received signal strength and phase changes. However, this is a problem, and by intentionally setting the indoor mode, you can get a useful whole-body It can detect (gross) motion and more subtle activity, and is the single main source of motion in a room. It is understood that actual breath analysis can be performed in the presence of only one person in the bedroom, for example. do.

[0051] As used herein, the term "sonar" refers to an acoustic signal for ranging and motion detection. , acoustic, sonic, ultrasonic and low frequency ultrasonic signals. can be DC to 50 kHz or higher. Processing techniques (e.g., FMCW physiological signal extraction) Some of them are actually short distances (e.g., in a living room or bedroom) Up to 3 meters) RF (electromagnetic) radar sensor (e.g., 5.8GHz, 10.5GHz) This can also be applied to other oscilloscopes (operating at frequencies up to 24 GHz).

[0052] 5.1.2.2.1.2 Bedclothes When bedclothes (e.g., quilts, comforters, blankets) are used, the sound in the room In some embodiments, the audio signal may be transferred from the sleeping person to the sleep When the person inside returns, the duvet may cause the audio signal to be attenuated. If breathing is observed on the surface of the duvet, the sound signal will be reflected. The signal can be used to monitor respiration.

[0053] 5.1.2.2.1.3 Telephone characteristics Placing a speaker and microphone on a smartphone is beneficial for sonar-like applications. The typical smart microphone is not necessarily optimal for detecting sound in the Smartphone speakers have low directivity. Generally, smartphone audio is generated by human conversation. and are not specifically designed for sonar-like sound sensing applications. The placement of the speaker and microphone varies depending on the smartphone model. For example, some smartphones have a speaker located on the back of the phone and a speaker located on the side of the phone. As a result, the microphone is redirected from the reflection. The speaker signal can only be easily "seen" after it has been received. The directivity of both the head and the microphone increases with frequency.

[0054] Indoor mode can improve the omnidirectionality of smartphone microphones and speakers. Reflections and associated room modes cause a wide range of noise throughout the room at a particular frequency. The motion disturbs all the room mode nodes, so the motion The movement of the smartphone microphone can be observed at these nodes. When provided at a node or anti-node, the audio path (microphone and speaker) Since the antenna is omnidirectional (even if the target is directional), an omnidirectional characteristic is required.

[0055] 5.1.2.2.2 Resolving technical issues According to aspects of the present disclosure, specific modulation and demodulation techniques address identified technical challenges and other This can be applied to reduce or eliminate the impact of the technical challenges.

[0056] 5.1.2.2.2.1 Frequency Hopping Range Gating (FHRG) and Adaptation Frequency Hopping Range Gating (AFHRG) Frequency hopping range-gated signals are a modulation and demodulation technique that occupies a specific frequency range. Each tone in the tone sequence has its own tone / frequency sequence. A tone or tone pulses are sent for a specific duration defined by the range request. Changes in the specific duration of such tone pulses or tone pairs can cause the detection range to change. The range of the instrument frame or slot also changes depending on the instrument pair or sequence. A frame occurs. Each slot can be considered as a tone slot. A room may contain multiple slots, and each slot may contain a tone or tone pair. In order to enhance audibility, each tone pair must be within the duration of the slot of the frame. The frames may have equal duration between them, but this is not necessary. The slots can have uniform or non-uniform width (duration). The color pairs may have uniform or non-uniform duration within a frame. To achieve better perceived / received tone quality, Guard bands (quiet periods) may be included between tones and then between frames to allow for separation. The RG can be used on available voice frequencies. The FHRG allows frequency hopping. It may be applied together with frequency dithering and / or timing dithering. Dithering means, for example, a reduction in the risk of room mode accumulation and a reduction in the environmental To reduce the risk of interference between other sonar signals, the frame and / or internal sensors Lot timing may vary (e.g., slot duration may vary or frame (The start time of a program or the start time of a slot in a time series may vary.) For example, in the case of a four slot frame, the frame width is constant but the time dithering is The tagging allows for at least one smaller slot width ( For example, a shorter duration tone pair) and at least one larger slot width ( For example, longer duration tone pairs may be possible within a frame. , allowing for slight variations in the specific range detected by each slot of the frame. Frequency dithering allows for multiple "sensors" (e.g., two or more sensors in a room) to be It may be possible for multiple systems (e.g., frequency dithering) to coexist in close proximity. By using The possibility of interference between the "sensors" in the operation of the sensor becomes statistically significant. Both sonar and frequency dithering reduce the risk of interference from non-sonar sources in the room / environment In synchronous demodulation, for example, after using a special training sequence / pulse The frame is aligned using a pattern detection / matching filter to determine the actual offset. Recovering the data requires an accurate understanding of the alignment between sequences.

[0057] FIG. 4 shows an example FHRG sonar frame. As shown, eight individual (ray) A pulse-driven sonar signal (similar to a radar system) is transmitted in each frame. Each pulse may include two orthogonal pulse frequencies, and each pulse may be 16 ms long. As a result, 16 distinct frequencies are placed into each 128ms transceiver frame. The block (frame) is then repeated. Orthogonal frequencies in OFDM are used to optimize frequency usage and improve signal / noise. The quadrature Dirac Comb frequencies can be used for frame timing dithering and This tone pair also follows the tone frequency dithering to assist noise reduction. , the resulting pulse shape is obtained and this shaping aids audibility.

[0058] The Adaptive Frequency Hopping Range Gating (AFHRG) system The frequency change of the tone frequency sequence is performed using a tone of length (e.g., 16 or 8 ms). It is held over time and then switched to another tone (for a total of four tones). This generates blocks of tone, and the blocks (frames) are repeated at 31.25 Hz. In the case of the AFHRG system, the timbre pattern shifts over each frame. These patterns can always shift, so a frame can be any other in a series of frames. The frequency pattern of the frame may be different from that of the frame of the The wave number pattern can change within a frame (e.g., within a slot). There may also be differences for different frequencies or different slots. For fading mitigation, the frequency can be adjusted within a fixed band. An example of a single 32 ms transceiver frame for an HRG system is shown in Figure 5. Four individual homodyne pulse-driven sonar signals are transmitted in each frame. The flight time of each signal is 8 ms. The inclusive range is 2.7 meters, so the effective actual frame is 1.35 meters, The repetition frequency is 31.25 Hz. Furthermore, each pulse is divided into two pulses as shown in Figure 5. 8 separate pulse-driven The homodyne signal is placed into a single 32 ms transceiver frame.

[0059] The AFHRG optimizes the available bandwidth by adjusting the Dirac Comb frequency and and quadrature pulse pairs. Each "pair" is within a slot within a frame. Therefore, multiple frames can be placed in a repeating or non-repeating pattern. A separate frequency pair may be used for each time slot, linear or Alternatively, Costas code frequency hopping may be used. The time slots are determined by the desired range detection. For example, as shown in FIG. 5, if the detection range is 1.3 m, t ts = 8ms. The frequency pair is

number

number

[0060] Each frequency pair is assigned to a desired bandwidth (e.g., 1 kHz bandwidth (18 kHz to 19 kHz)). Optimize the frequency separation, maximize the separation between frequencies, maximize the separation between pulses and / or minimize intermediate pulse transients. These optimizations can be achieved for n frequencies, each of which is a frequency division Including the distance df and the slot width tts, we get:

number

number

[0061] The time slot duration is 8 ms and the bandwidth is 1 kHz (e.g., f n =18, 12 5Hz and f n-1 = 18,000 Hz), the frequency separation df is as follows: becomes:

number

number

[0062] The zero crossing is achieved by the following trigonometric formula:

number

number

[0063] 6A, 6B, and 6C show the 5x32ms performance for an exemplary AFHRG system. Graph 602 in FIG. 6A shows an example of a pulse-driven frame. The graph 602 in FIG. 6A has an x-axis of time and a y-axis of amplitude. Graph 602 shows five 32ms pulse frames on an axis. of these frames, each frame has a total of 5x4=20 It contains four slots for a tone pair. The tone pair is represented by slots 606-S1, 606- This envelope is set in S2, 606-S3, and 606-S4. However, "real world" speakers and mics can be slightly less sensitive at higher frequencies. The slots forming the frame shown in FIG. 6A are combined in graph 6 of FIG. Graph 604 shows the time domain of a single 32 ms frame from FIG. The frequency domain of the system is shown in Fig. 1. It consists of four slots 606-S1, 606-S2, 606-S3, 6-S3 and 606-S4, each slot has two tones 608T1 and 608 T2 (timbre pair). Graph 604 has an x-axis of frequency and an arbitrary y-axis. In one embodiment, the tones of the tone pair of a frame are in the range of 18000 Hz to 18875 Hz. Other frequency ranges have different audio timbres (e.g., different frequencies). (e.g., non-audible audio) may be performed as discussed in more detail herein. Each tone pair of a frame is generated sequentially (consecutively) within the frame. The tones of a color pair are generated substantially simultaneously within a common slot of a frame. 6B, the tone pair of the slots in FIG. 6B is examined with reference to the graph 610 in FIG. 6C. Graph 610 shows the relationship between a single tone pair (e.g., tones 608T1, 608T2) in a frame. 8T2) showing its simultaneous generation during the slots of a frame. The graph 610 has an x-axis of time and a y-axis of amplitude. In this example, the amplitude of the tone is In the example graph of 610, The tone amplitude ramp portion begins with the slot start time at zero amplitude and Thus, the slot has the same width at the beginning and end. By having the beginning slot and the end slot amplitude characteristics arrive with a time-zero amplitude tone, The audibility trend between pairs of tones in adjacent slots with the same slot amplitude characteristics The above becomes possible.

[0064] Hereinafter, the FHRG or AFHRG for processing audio signals (both are referred to as "(" in this specification) A) Regarding the architecture aspect (referred to as "FHRG"), the processing module shown in FIG. As shown in FIG. 7, the reflected signal is filtered by a high-pass filter 7 02 module, and the Rx frame buffer 70 4. The buffered Rx frames are then input to an IQ (in-phase and quadrature) demodulator 70. According to some embodiments of the present disclosure, n (where n is, for example, The frequencies (e.g., 4, 8, or 16) are recovered to baseband as I and Q components. The baseband may be modulated to include sound reflections (e.g., transmitted / generated and received changes in perceived distance detected by the perceived / perceived sound signal or tone pair The intermediate frequency (IF) stage 708 processes the corresponding motion information (e.g., breathing / body movement). 710 is a processing module for outputting a single I and Q component from multiple signals. After optimization in the module "IQ Optimization", a single combined output is produced.

[0065] The multiple IQ input information in 710 is used to generate a single IQ base map for the algorithm input stage. Optimized and shortened for a single IQ output (effectively I and Q components) The combined signal from the Q and Q components is chosen to be the one with the best signal quality (e.g., the clearest The IQ may be derived based on selected candidate IQ pairs based on the patient's IQ (e.g., based on respiratory rate). For example, the respiration rate can be calculated from the baseband signal (candidate 1) described in International Application WO2015006364. signal or combined signal). The single IQ output may be an average of the input signals or may actually be derived. Therefore, in 710, such a respiration rate may be calculated as the average or median of the respiration rates. Such modules may include summing and / or averaging processes.

[0066] The AFHRG architecture allows for the optional addition of an IF stage. There are two possible IF stage methods: (i) an initial IF stage, e.g., 4 ms (0.5 m (ii) comparing the first (e.g., 4 ms) (0.5 m range) signal with a later (e.g., 4 ms) signal; Compare the phase of the first tone with the phase of the second tone in the tone pair. The level or phase change of the sensed audio signal within the time of flight (ToF) is compared. Therefore, this stage is sensitive to common mode signal artifacts (e.g., motion and 1 / f noise). The phase foldover recovery module 712 may function to eliminate the phase foldover problem. , 710 receives the I and Q component outputs of the optimization stage, and after processing, If there is a foldover in the demodulated signal, the breathing It is desirable to minimize such foldovers, as they significantly complicate detection. Foldover minimization can be achieved by I / Q combining techniques (e.g., reverse polarity). using real-time center tracking estimation or more standard This can be done by dimensionality reduction methods (e.g., principal component analysis). This occurs when chest movements span a half wavelength of approximately 9 mm (e.g., at a temperature of 20°C). At a CW frequency of 18 kHz at 1000 Hz, the speed of sound is approximately 343 m / s, The length is 19mm.) Auto-correct foldover (e.g., when this behavior occurs, To reduce the severity or probability of a problem, a dynamic center frequency strategy may be used. If frequency doubling or a significant sawtooth breathing pattern (morphology) is detected, The system moves the center frequency to a new frequency, pushing the I / Q channels out of balance. It can be returned to a balanced state and exit the foldover situation. Each time a masking tone is used, reprocessing is often required. When the movement is at lambda / 4, one If the movement exceeds lambda / 2, both It is highly likely that the information will be considered to be on the channel.

[0067] In the FHRG implementation, the frame / timbre pair frame modulator in the 714 is controlled by the AFH (e.g., with implicit multi-frequency operation using frames). In adaptive implementations such as RG or AToF, the system is not present during the FHRG implementation. It further includes modules for fading detection and explicit frequency shift operation. The feedback mechanism provided by the aging detector 718 module is: System parameters (e.g., frequency shift, modulation type, frame parameters (e.g., , number of tones, time and frequency intervals) to adjust the behavior in different channel conditions. Fading can be optimally detected by the sensed audio signal (e.g., breathing). and / or from changes in amplitude modulation (extracted via envelope detection) in The fading detector can then detect the change in the tone pair. Secondary information from the baseband signal processing of the This can then be used to demonstrate the actual extracted respiratory signal quality / morphology. It can better adapt to the current channel conditions to maximize the useful signal. The fading detector may also process I / Q pairs (pre-baseband breathing). / Heart Rate Analysis). By using a non-fade Tx (transmit) waveform(s) configuration, Therefore, the limited output signal power of the speaker is also optimized / best used to generate the sound sensor / receiver R x to maximize the useful information received (received) and demodulated / processed to baseband In some cases, the system may need to generate a desired demodulated respiration rate (e.g., time scale similar to respiratory rate (which can cause "noise" / artifacts in the intensity band) More stable over longer periods (to avoid multipath variations in fading above) If this is found to be the case, the system will operate at a slightly suboptimal Tx (in terms of short-term SNR). , and may select a frequency set for

[0068] Of course, if one or more individual tones are used instead of a tone pair, adaptive CW(A A similar architecture can be used for the implementation of a CW system.

[0069] 5.1.2.2.2.2 FHRG Modulation and Demodulation Module Details 5.1.2.2.2.2.1 FHRG Sonar Equation The transmitted sound pressure signal generated by the smartphone speaker is reflected by the target. Then, back to the smartphone microphone, FIG. 8 shows an example of an isotropic omnidirectional antenna 802 versus a directional antenna 804. For an omnidirectional source, the sound pressure (P(x)) level scales with distance x as follows: Decreases:

number

[0070] Smartphone speakers are directional at 18kHz, so:

number

[0071] A target (e.g., a user in bed) has a specific cross section and reflection coefficient. The reflection is also directional.

number

[0072] As a result, a transmitted signal of sound pressure P0, when reflected at a distance d, is attenuated by Return to your smartphone with your decay level:

number

[0073] The smartphone microphone then sees a portion of the reflected sound pressure signal. -Percentage is the effective microphone area A e Depends on.

number

[0074] In this way, a small portion of the transmitted sound pressure signal is reflected from the target and It is detected back on the microphone.

[0075] 5.1.2.2.2.2.2 FHRG Movement Signal(s) A person within range of the FHRG system at a distance d from the transceiver must be in active It reflects the sonar transmit signal and produces a receive signal.

[0076] Any frequency f nm Regarding the modem signal generated by the smartphone The sound (pressure wave) sounds like this:

number

[0077] For any individual frequency, when the signal arrives at the target at distance d, R:

number

[0078] The reflected signal re-arrives at the smartphone microphone for any individual frequency. When worn, it looks like this:

number

[0079] If the target distance moves with a sinusoidal variation around d, then:

number

[0080] Maximum respiratory displacement A b Since is smaller than the target distance d, its effect on signal amplitude reception can be ignored. The resulting smartphone microphone breathing signal looks like this:

number

[0081] An ideal respiratory motion signal would be similar to the cardiac motion signal regardless of different frequencies and displacements. Therefore, it can be considered as a sine function of a sine function, and for the same displacement peak-to-peak amplitude It has areas of maximum and minimum sensitivity.

[0082] To properly recover this signal, a quadrature receiver must be used so that the sensitivity null is mitigated. It is useful to use a transmitter or other device.

[0083] Next, the I and Q baseband signals are used as phasors I+jQ. , you can do the following: 1. To recover the RMS and phase of the respiratory signal. 2. To restore the direction of motion (phasor direction). 3. Recover foldover information by detecting phasor direction changes thing.

[0084] 5.1.2.2.2.2.3 FHRG Demodulator Mixer (705 and 706 in Figure 7) Module in FHRG SoftModem (Sound Sensing (Sonar) Detection - Software Based Modulator / Demodulator The front end for the device transmits a modulated audio signal through a speaker to The echo reflection signal from the receiver is sensed through a microphone. An example is shown in Figure 7. A soft modem receiver that processes the sensed reflection as a received signal can: It is designed to perform a variety of tasks such as: High-pass filtering the received signal at audio frequencies. · Synchronizing modulated audio with frame timing. Demodulate the signal to baseband using a quadrature phase synchronous demodulator. Demodulating the (intermediate frequency) components (if present). The resulting in-phase and quadrature baseband signals are passed through a low-pass filter (e.g., a Sinc filter). (filter). Regenerate I and Q baseband signals at different frequencies from various I and Q signals To make something.

[0085] The soft modem demodulator (e.g., modules 705 and 706 in FIG. 7) Local oscillator (shown as "ASin(ω1t)" in module 706) and a frequency synchronized with the frequency of the received signal to recover the phase information. A module (frame demodulator) selects each tone pair for subsequent IQ demodulation of each pair. Select and separate (i.e., define each (ωt) for IQ demodulation of the tone pair). Below we explain the in-phase (I) signal recovery. The quadrature component (Q) is the same, but at a 90 degree angle. With the phase change, the local oscillator (706) is placed in the module as "ACos(ω1t)". (shown below) is used.

[0086] As an equivalent digital signal and an input high-pass filter (e.g., filter 702) If the pressure signal sensed at the microphone reproduced in the smart The audio signal received by the phone softmodem demodulator is:

number

number

number

number

[0087] The in-phase demodulator output signal for any received signal, when properly synchronized, is R:

number

[0088] The demodulation operation follows a trigonometric formula:

number

[0089] If the local oscillator and received signals have the same angular frequency w1, then:

number

[0090] If 2w1t is removed after the low pass filter (shown as LPF in Figure 7), It will look like this:

number

[0091] In this way, low-pass filtered synchronous phase demodulation of any received signal is achieved. The output signal of the instrument, when properly synchronized, will look like this:

number

[0092] 5.1.2.2.2.2.4 FHRG Demodulator Sinc Filter The smartphone soft modem demodulator uses oversampling and averaging Sinc frame The filter is used as a "LPF" in the demodulator module in 706 to remove unwanted components. and decimates the demodulated signal to the baseband sampling rate.

[0093] The received frame is used to transmit both tones of a tone pair (e.g. These hop sounds include: is transmitted during the subsequent burst. When demodulating the return signal, for example, ij Unwanted demodulated signals need to be removed. These include: Baseband signals spaced apart in frequency: |f ij -f nm | ·|2f nm -f s | and |f nm -f s |The sampling process in both The resulting alias component. Demodulated audio frequency components at: 2f nm f nm The audio carrier at may pass through the demodulator due to nonlinearities.

[0094] To achieve this, a low pass filter (LPF) (e.g., a Sinc filter) is used. The Sinc filter has a filter response that is zero for all unwanted demodulation components. This is almost ideal because it is designed to The transfer function for

number

[0095] Figure 9 shows an example of a Sinc filter response, and Figure 10 shows a graph of 384 samples over an 8 ms period. Due to the averaging of samples, the worst case attenuation of the 125Hz Sinc filter is show.

[0096] 5.1.2.2.2.2.5 Demodulator Baseband Signal The smartphone soft modem transmits the modulated tone frame. Multiple tone pairs transmitted in one burst (e.g., a 2-tone set with four tones) It includes two tone pairs, as well as the hop tone sent during the subsequent burst tone.

[0097] First, demodulation of a tone pair will be explained. Transmission, reflection, and reception of a tone pair are performed simultaneously. Therefore, the only demodulation phase difference is due to the frequency difference between the tone pairs.

[0098] Before "flight time" hours:

number

[0099] After a "time of flight" period, this DC level receives contributions from migrating target components. Here, the demodulator output accepts: For the in-phase (I) demodulator signal output for each tone pair frequency, we obtain:

number

number

[0100] The sum Σ of each of these samples in the demodulated burst is the sum of the Sinc " accepts the signal resulting from filtering. This filter rejects all of these frequencies. To improve signal-to-noise, nm -f 11 Unnecessary items spaced apart The required number of transfer functions is then calculated to produce zeros at the demodulated burst frequency. It is designed to average out incoming bursts.

[0101] This sum occurs during the full burst of sound, but the component due to movement is not included in the timbre. This occurs only after the time-of-flight period, until the end of the pair burst period, i.e.:

number

[0102] As a result of the time of flight, an additional signal attenuation factor is introduced into the signal recovery. Become:

number

[0103] Decoding a speech signal at the frame rate where a frame contains n consecutive tone pairs After modulation and subsequent averaging, the baseband signal is Produces:

number

[0104] 5.1.2.2.2.2.6 Demodulator I mn and Q mn As a result of this demodulation and averaging operation, the tone pair audio frequencies are sampled as follows: Four separate baseband signal samples (i.e., I 11 , I 12 , Q 11 , Q 22 ) is demodulated to:

number

[0105] In the subsequent frame period,

number

[0106] This sampling procedure is repeated for each frame to generate a variety of baseband signals. In the given example, a 2x4xI baseband signal and a 2x4xQ baseband signal are generated. Signals are generated, each of which has a phase characteristic due to a trigonometric formula. R:

number

[0107] An example of the phase change due to the first tone pair at 1 m for a moving target is shown in Figure 11 .

[0108] The AFHRG architecture allows for tight timing synchronization across time, frequency and timbre. Facilitated by paired envelope amplitude characteristics (using frame synchronization module 703) Voice Tx and Rx frames can be asynchronous. It can be expected that the mobile device will maintain perfect Tx-Rx synchronization. According to an embodiment, as shown in FIG. 12, initial synchronization is achieved by a training sound frame. The training sound frame may include a nominal frame timbre, as shown in 1202. As shown above with respect to FIG. 7, the Rx signal is IQ demodulated by the IQ demodulator 706. Then, as shown in 1204, the envelope (e.g., after calculating the absolute value) can be (by low-pass filtering or by using the Hilbert transform) and As shown, the timing is extracted using level threshold detection. The shape of the in(ω2t) pulse aids autocorrelation and is important for precision and accuracy. Next, the Rx circular buffer index can be configured to increase the In some aspects of the present disclosure, No training sound frames may be used, and Rx may be activated, followed by TX after a short period of time. is activated and after a short recorded silence period, the threshold detects the start of a TX frame. This threshold can be used to make the signal robust to background noise. can be chosen to offset from the true Tx start using prior knowledge of the rise time of the Tx signal. This can cause the threshold detection that occurs during the rise time to be corrected. A detection algorithm that can adapt based on relative signal levels when considering the envelope of the signal. Using the , the peaks and "troughs" (troughs are the absolute values ​​taken around the zero line) The trough is more likely to contain noise due to reflections. Therefore, optionally only the peaks may be processed.

[0109] In some cases, the device (e.g., a mobile device) may be busy with other activities or Processing can cause loss of synchronization, resulting in jitter or degradation of audio samples. Therefore, a resynchronization strategy can be used, for example, in conjunction with the frame synchronization module 703. According to some aspects of the present disclosure, to periodically verify that synchronization is good / true, , the introduction of periodic training sequences can be performed. Another approach (which is Optionally, such periodic training sequences may also be utilized, but such (without using known Tx frame sequences) until a maximum correlation exceeding a threshold is detected. Repeatedly crosses a sequence (typically a single frame) along a segment of the Rx signal. The threshold is determined by the expected noise and multipath interference in the Rx signal. The exponent of this maximum correlation can then be used to estimate the synchronization correction coefficient. Desynchronization in the demodulated signal can be used as an index to measure small or Note that this can appear as a large step in the base line (usually the latter). In contrast to the roughly graded response seen in real large motion signals, desynchronized segments does not carry any useful physiological information and is removed from the output baseband signal (as a result, A few seconds of signal may be lost).

[0110] Therefore, synchronization typically relies on prior knowledge of the transmitted signal, and initial alignment The detection is performed through techniques (e.g., envelope detection), and then the received signal Rx The correct burst is selected using cross-correlation between the signal and the known transmit signal Tx. Loss checks should ideally be performed periodically and optionally include data integrity testing. Includes:

[0111] When a new synchronization occurs, in this example of a consistency test, A check is made to compare the timing of the preceding synchronization(s). The time difference between the candidate synchronization times in samples can be determined to be within acceptable timing tolerances. If this is not the case, synchronization may have been lost. In such a case, the system may initiate a reinitialization (e.g., local using a new training sequence (unique during Tx), or the introduction of some other marker). In such cases, the sensed time after a possible desynchronization event The collected data may be flagged as suspect and discarded.

[0112] Such desynchronization checks can be performed continuously on the device to provide useful trends. For example, if periodic desynchronization is detected and corrected, the system may: Adapting the audio buffer length to stop or handle this undesirable behavior Or by varying the memory load (e.g., by waiting for some processing to finish the sleep session). deferred (complex algorithms in real-time or near real-time) This resynchronization approach buffers data for such operations (instead of executing them). The approach is particularly applicable to various types of Tx (e.g., FHRG, AFHRG, FMCW). (e.g., when using complex processors such as smart devices) As described, the envelope of the reference frame and the estimated envelope of the Rx sequence are Although correlations can be made between the reference frame and the Rx sequence, such correlations are Such correlation may be performed directly between the time domain and the frequency domain. (e.g., as a cross-spectral density or cross-coherence measurement) (Triggered by user interaction (e.g., another application on the smart device or an incoming call) There may be situations where Tx signal generation / regeneration is stopped for a period of time (when regeneration is expected due to In that case, resynchronization is paused until the Rx sees the signal exceed a minimum level threshold. Potentially, the device's main processor and audio CODEC are out of sync. If desynchronization is suspected to be of prolonged duration, A mechanism for generating and regenerating the ring sequence may be used.

[0113] Desynchronization can result in a DC shift in the signal and / or a signal that appears to be a step response. Note that the mean (average) level or trend may change after a desynchronization event. Furthermore, desynchronization can also occur due to external factors (e.g., loud noise). (e.g., a short impulse or duration in the environment) (e.g., a loud bang) , something extremely loud, knocking on a device including a microphone or a nearby table If there are any noises (snoring, coughing, sneezing, shouting) the Tx signal may be drowned out (no The noise source has a frequency and content similar to Tx, but with a higher amplitude) and / or Rx may be inundated (infiltration, hard or soft clipping, or potential (Essentially, activating automatic gain control (AGC)).

[0114] FIG. 13 shows an example of a 32 ms frame including an intermediate frequency. According to the IF stage, the early 4 ms (0.5 m range) signal is compared with the late 4 ms signal. According to another aspect of the present disclosure, the IF stage may be configured to: , the phase of the second tone. In the IF stage, The level or phase change of the received signal at the artifacts (e.g., motion and 1 / f noise) can be reduced or eliminated.

[0115] Room reflections can generate room modes at resonant frequencies. The structure allows for a shift in fundamental frequency after the frame frequency separation is maintained. The pulse pair frequency can be shifted to a frequency that does not generate room modes. In this case, the frame pulse frequency is hopped to reduce the modal energy accumulation. In another embodiment, to avoid the recognition of reflections by the homodyne receiver, Frame pulse versus frequency produces a long, non-repetitive pseudo-random sequence with frequency-specific reflections. The frame can be hopped over time to reduce interference from other modes. The frequency may be dithered or a sinusoidal frame shift may be used.

[0116] There are many advantages to using an AFHRG system. For example, The system allows for adaptive transmit frequencies for indoor mode mitigation. For example, the typical "quiet period" required in pulse-driven continuous wave radar systems In contrast to this, for SNR improvement, a repeated pulse mechanism and different frequencies are used. In this regard, active transmission is used. Subsequent tone pairs at the same frequency will result in a "quiet period." Therefore, during the operation (propagation) of the subsequent tone pair, This allows the propagation of reflected sound to settle. Time slots allow the reflection to reach first order. Furthermore, to further improve the SNR, Dual frequency pulses are used in the oscilloscope. By providing an intermediate frequency phase, The architecture allows for integrated and / or programmable range gating. ,To reduce indoor reflections, Costas or pseudorandom frequency hopping codes are used. In addition, the quadrature Dirac Co mb frequency enables frame timing dithering to further aid noise reduction It also allows for gradation and tonal frequency dithering.

[0117] Note that wider or narrower bandwidths can also be selected in the AFHRG. For example, a particular handset may be capable of transmitting and receiving with good signal strength up to 21 kHz. If the people using the system can hear up to 18 kHz, then 19 A 2 kHz bandwidth of 21 kHz may be selected for use by the system. These tone pairs are: may be hidden in other transmitted (or detected) audio content, It will also be appreciated that the changes may be adaptively masked from the user. By transmitting a tone pair or an inaudible tone pair (for example, about 250 Hz), If the system is operating at values ​​above 18 kHz, for example, any The generated music source may be low-pass filtered below approximately 18 kHz. Conversely, if the audio source cannot be processed directly, the system tracks the audio content and predicts it. A measured number of tone pairs are injected and the system is adapted to both maximize SNR and minimize audibility. In this case, elements of the existing audio content can mask the tone pair. The pair(s) are adapted to maximize the masking effect, i.e. In auditory masking, the perception of tonal pairs is reduced or eliminated.

[0118] (A) For use with FHRG, FMCW, UWB, etc. (with or without masking) Another approach used in generating or reproducing acoustic signals (e.g., music) is the use of In such cases, the signal content can be adjusted (see details below). Specifically, by varying the amplitude of the signal, the sensed signal is directly reflected in the reproduced signal. Subsequent processing involves mixing and subsequent encoding to obtain the baseband signal. Low-pass filtering filters incoming (received) signals vs. outgoing signals. This involves demodulating the (generated / transmitted) signal. The phase change of the carrier signal is It is proportional to the breathing or other movements in the vicinity of the sensor. One caveat is that the underlying beat If the amplitude or frequency of the music changes naturally at the breathing frequency due to This can lead to increased noise during quiet periods during playback. To continue respiration detection, an extra signal (e.g., amplitude modulated noise) must be injected. Alternatively, the system may need to operate the baseband during such quiet playback periods. to ignore the signal (i.e., to avoid the need to generate a modulated signal as "filler" Instead of amplitude modulating an audio signal (e.g., a music signal), In addition, to recover the baseband signal, this signal is divided into defined phase segments. After both are phase-encoded, the received signal is demodulated and the phase change of the received signal during the encoding interval is It is also understood that it is possible to track

[0119] 5.1.2.2.2.3 Adaptive Flight Time According to some aspects of the present disclosure, adaptive time-of-flight (A AToF architecture can be used. AToF architecture is similar to AFHRG. As with AFHRG, AToF improves SNR by pulse repetition. For example, four individual homodyne pulse driven sonars are used. A signal may be sent in each frame, with each signal having a time of flight of 8 ms. At a flight time of 8 ms, the range in both directions is 2.7 meters, so the effective The actual frame is 1.35 m and the pulse repetition frequency is 31.25 Hz. Additionally, each pulse may contain two pulse frequencies as shown in FIG. 14, resulting in eight distinct pulses. Pulsed homodyne signals are obtained within a single 32ms transceiver frame. In contrast to the AFHRG architecture, AToF requires 1 ms instead of 8 ms. A pulse lasting over the entire period is used.

[0120] As with AFHRG, AToF uses Dirac to assist in pulse shaping. Comb features and orthogonal pulse pairs can be used. In AToF, each time slot Separate frequency pairs may be used for linear or Costas code frequency hopping. The time slots can be determined by the required range detection. The frequency pairs are ,

number

number

[0121] In summary, a frequency pair (e.g., for multiple individual tones at different frequencies) Some advantages include: Frequency flexibility: any frequency at any time slot

number

[0122] 5.1.2.2.2.4 Frequency Modulated Continuous Wave (FMCW) In yet another aspect of the present disclosure, in order to alleviate some of the above technical problems, A frequency modulated continuous wave (FMCW) architecture may be used. Using FMCW: FMCW signals are localized because they allow distance estimation and range gating. In everyday life, Examples of audible sawtooth chirps are similar to chirps from birds, and triangular chirps are e.g. Similar to the sound from a police siren. FMCW is transmitted through built-in or external loudspeakers ( A typical smart device using a microphone(s) and a RF sensors running on a device (e.g., a smartphone or tablet) and audio sensors.

[0123] Audible prompt(s) (e.g., when recording starts or when the smart device is moved) sometimes played) can be used to convey the relative "volume" of non-audible sounds. Please note that on a smart device with one or more microphones, (e.g., to disable echo cancellation, noise reduction, and automatic gain control) Minimize any extra processing in the software or hardware CODEC (and The profile is selected so that it is not necessary (ideally not needed). If the microphone or The primary microphone gives good results, or it can be used for e.g. virtual reality. Selection of "raw" microphone feeds that may be used for entertainment applications or music mixing. Depending on the handset, for example, some Android OS revisions The "voice recognition" mode available in the (preferred) microphone and preprocessing may be disabled.

[0124] FMCW allows tracking across a spatial range, thus determining the location of a person breathing. If a person is breathing, the location of the person can be determined. ... (i.e., within the range of the sensor) (The respiratory waveform from each subject can be removed.)

[0125] FMCW can have chirp (e.g., sawtooth, triangular, or sinusoidal shapes). In contrast to the pulses in (A) FHRG-type systems, FMCW systems are frequency-varying. If possible, generate a repeating waveform (e.g., inaudible) of the signal at the zero crossings. Matching frequency changes in the Discontinuities in the generated signal that can cause unwanted harmonics (which can lead to unwanted stress) It is important to avoid the situation where the minute jumps. Similar to the shape of a minute, it may slope upward (from low to high frequencies). When repeated, The sloped portion repeats a waveform (sawtooth) from a lower portion to a higher portion. However, such a sloped portion (from higher frequencies) When repeated, the sloped portion may slope downwards (towards lower frequencies). By repeating the waveform (sawtooth) such as repeating the lower part from the top, a downward sawtooth shape is formed. Furthermore, such ramping is approximately linear using the linear function shown. However, in some versions, the increase in frequency is in the form of a curve (increasing or decreasing). (e.g., between the lows and highs (or highs and lows) of the frequency change of the waveform) (using polynomial functions between

[0126] In some versions, an FMCW system uses one or more pulses in the form of a repetitive waveform. For example, the system may be configured to vary any one of the following parameters: One or more of the following may be varied: (a) the frequency peak position of the repetitive portion of the waveform (e.g., (Early or late peaks in the waveform). Such parameter variations occur during the slope portion of the waveform. This may be a change in the slope of the frequency change (e.g., an ascending slope and / or a descending slope). Such a parameter variation may be a change in the frequency range of the repetitive portion of the waveform.

[0127] Such inaudible signals used on smart devices with FMCW triangular waveforms Specific methods for achieving this are outlined below.

[0128] For FMCW systems, the theoretical range resolution is defined as: V / (2* BW), where V is the speed of speech, for example, and BW is the bandwidth. For example, at room temperature, V = 340 m / s and 18-20 kHz chirp FMCW system In this case, a distance resolution of 85 mm is possible for target separation. Each object is spaced at least 85 mm apart within the sensor's field. A much finer resolution of the individual (similar to CW systems) assuming they are deployed In addition to the above, one or more moving targets (e.g., subject breathing motion) can be detected, respectively. do.

[0129] Depending on the relative frequency sensitivity of the speaker and / or microphone, the system The system corrects each frequency to have the same amplitude (or corrects for nonlinearities in the system). Other modifications of the transmitted signal chirp (such as emphasis on the Tx FMCW waveform) For example, if the speaker response decreases with increasing frequency, The 19 kHz component of the chirp is generated at a higher amplitude than the 18 kHz component, which In reality, the Tx signal has the same amplitude across the frequency range. Of course, it is important that the system is free from distortion (e.g., clipping, unwanted harmonics, sawtooth waves) (i.e., to maximize SNR, the Tx signal is (adjusting the Tx signal to be as close to linear as possible with as large an amplitude as possible) The system as deployed may check the received signal for the transmitted signal. The waveforms are adjusted to meet the target SNR for respiratory parameter extraction while maintaining viable shape. The shape may be scaled down and / or the volume reduced (maximizing SNR and reducing the loudspeaker There is a trade-off between large drive(s).

[0130] Depending on the channel conditions, the chirp may be adjusted, e.g. (adapted to the bandwidth used). The system can be adapted to be robust against (stronger) interferers.

[0131] 5.1.2.2.2.5 FMCW Chirp Types and Inaudibility FMCW is a type of radio wave that can be transmitted at audio frequencies without further digital signal processing steps. and a period of 1 / 10 ms = 100 Hz (and related The human ear can detect frequencies as low as -40 dB through high-pass filtering. Components removed (filtering of unwanted components below -90dB may be required) However, it is surprising that it can distinguish extremely low amplitude signals (especially in quiet environments such as bedrooms). For example, the ability to use Hamming, Hanning, Blackwell for chirps (To generate a window as per Kuman et al.) It can be suppressed and then re-emphasized for correction in subsequent processing.

[0132] Isolating chirps (e.g., between chirps rather than a single chirp in duration) It is also possible to repeat the same sequence for 100-200 ms each to create a quiet period (much longer). This has the advantage that the detection of reflection-related standing waves can be minimized. The disadvantage of this approach is that the available transmit energy is limited for successive repetitions of the chirp signal. The trade-off is reduced audible clicks and a continuous output signal. If reduced, the device (e.g., smart device, such as a phone loudspeaker) loudspeaker Certain benefits are obtained in that the difficulty of driving the amplifiers (speakers and amplifiers) is reduced. , in the case of coil loudspeakers, when driven at maximum amplitude for long periods, transients It can be affected.

[0133] Figure 15 shows an example of an audible version of an FMCW gradient sequence. Figure 15(b) shows a typical chirp in a graph showing width versus time. Figure 15(c) shows the frequency spectrum of a standard audio chirp. .

[0134] As noted above, the FMCW tone may also use a sinusoidal profile rather than a ramp portion. Figure 16 shows an example of an FMCW sinusoidal profile with high-pass filtering applied. Figure 16A shows the signal amplitude versus time. Figure 16B shows the frequency spectrum of the signal. 16C shows the spectrogram of the signal. In the following example, 18 kHz is used as the starting frequency. A 20 kHz end frequency is used. A 512 sample "chirp" is generated, Generated with a wave profile. The intermediate frequency is 19,687.5Hz and the deviation is + + / - 1,000 Hz. The sequence of these chirps is Having a continuous phase helps to make the sequence inaudible. , can be optionally applied to the obtained sequence. However, due to this operation, the phase The possibility of discontinuity can arise, in which case, paradoxically, the desired audibility can be reduced by a signal with a lower Note that the resulting signal is more audible (rather than shifted). FFT of the shifted received signal is multiplied by the conjugate of the FFT of the transmitted sequence. The phase angle is The best-fit line is found.

[0135] As stated, if a sine wave signal needs to be filtered to make it inaudible, This is not ideal because it can lead to distortion of the phase information. This can be attributed to the continuity of the signal. Therefore, inaudible FMCW sequences using triangular waveforms are used. obtain.

[0136] To prevent clicks from being generated by the speaker or speakers that generate the waveform A triangular waveform signal, which is a phase continuous waveform, can be generated continuously across the sweep. Despite this, there may be phase differences at the beginning of each sweep. The equation ensures that the phase at the beginning of the next sweep starts at a multiple of 2π The looping of a single section of the waveform can be phase continuous, since the This becomes possible.

[0137] A similar approach can be applied to tilted chirp signals (when the chirp is 18 kHz or (Assuming that the frequency is 20 kHz) If the frequency jump discontinuity is 20 kHz to 18 kHz, This can put stress on amplifiers and loudspeakers in commercial smart devices. In the case of a triangular waveform, the addition of a downward sweep provides more information than the sloped portion, The angular waveform has a more "gradual" change in frequency, so the discontinuous jumps in the slope are This should be less harmful to the telephone hardware than in the previous case.

[0138] Examples of implementations that can be implemented in the signal generation module for generating this phase-continuous triangular waveform are: The general equation is: The phase of the triangular chirp is given by and can be calculated for the time index n from the following closed-form equation:

number

number

[0139] The final phase at the end of the downward sweep is given by

number

[0140] To bring the sine of the phase back around zero at the start of the next sweep, we get :

number

number

[0141] For example, assume that N and f2 are fixed. Therefore, f1 should be as close to 18 kHz as possible. Choose m so that it is close to:

number

[0142] Demodulation of this triangular waveform can be achieved by demodulating the upper and lower sweeps of the triangle separately or It is possible to process upward and downward sweeps simultaneously or actually frame multiple triangle sweeps. If only the upward sweep is processed, the slope Note that this is equivalent to processing a single frame of (sawtooth).

[0143] Upward and / or downward sweeps can be used, for example, in breath sensing, to detect breathing patterns. Separation of inspiration from expiration (i.e., whether inspiration or expiration is occurring) An example of a triangular signal is shown in Figure 17, and the demodulation of the detected respiration waveform is shown. 18A and 18B. As shown in FIG. 17, the smart device microphone The app also recorded an inaudible triangle emitted from the loudspeaker of the same smart device. In FIG. 18A, the graph shows the reflection of the triangular signal (associated with the upward sweep of the signal). ) demodulation results, showing the extracted respiratory waveform at 50.7 cm. ,The graph shows the demodulation results of the reflection of the triangular signal (related to the downward sweep of the signal),5 18B. As shown in these figures, the respiratory waveform extracted at 0.7 cm from the The demodulated signal is the demodulated unwrapped signal of the corresponding upward sweep due to the phase difference. It appears to be upside down.

[0144] Instead of a symmetrical triangular slope, an asymmetrical "triangular" slope may be considered. In this case, the upward sweep lasts longer than the downward sweep (or vice versa). The shorter duration is due to (a) transients, e.g., in the slope portion of the upward sweep only; (b) to maintain inaudibility that may be compromised by the signal; and (b) to provide a reference point in the signal. Demodulation is possible with a longer upward sweep (for sawtooth slopes including quiet periods, but (The "quiet period" is a downward sweep of shorter duration.) This potentially allows for maximizing the Tx signal used and maintaining an inaudible phase-continuous Tx signal. This allows for a reduction in processing load (if necessary).

[0145] 5.1.2.2.2.6 FMCW Demodulation An exemplary flow of the sonar FMCW processing method is shown in Figure 19. The blocks or modules shown in this figure are The module provides front-end transmit signal generation at 1902 and Provides signal reception, provides synchronization at 1906, and downconverges at 1908 provides "2D" analysis (e.g., activity, movement, presence / absence and These data / signals provide a means for the patient to: From the number, wake and / or sleep staging at 1912 may be provided.

[0146] Especially in the 1906, when checking the synchronization in the module, the transmitted chirp is received. As an example, a sharp peak after the correlation operation can be observed. The resulting n arrow pulses can be useful to determine fine details of the signal. There is generally an iterative relationship between the breadth of the vector and the breadth of the correlation function (e.g., relatively When a wide FMCW signal is correlated with an FMCW template, it leads to n arrow correlation peaks. As an aside, if you correlate a standard chirp with the response at the receiver, you get an echo-in It becomes possible to estimate the pulse response.

[0147] In FMCW, the system sums all responses across the frequency range to find the "strongest" overall effectively considered as a dynamic response (e.g., some frequencies in the chirp are severely faded). The system may consider the above totals because some users may encounter some problems, but others may provide good responses. (This is the case.)

[0148] Baseband signals (e.g., whole-body movement or breathing) are recovered as FMCW systems.

[0023] An exemplary method for one or more processors (e.g., in a mobile device) to According to some aspects of the present disclosure, the transmission of the chirp sequence may be For example, an FMCW transmitter activating one or more speaker(s) at 1902 The chirp sequence can be generated using a signal generation module, for example a non-audible triangular tone. It can be an incoming received signal (e.g., operating with one or more microphones). The synchronization of the transmitted signal with the received signal (as received at 1904 by the receiving module) is For example, this can be done using the peak correlation described above. Continuous resynchronization checks for the chirps (e.g., for the synchronization Then, a mixing operation (e.g., by multiplication or summation) can be performed. For tuning (e.g., using a down-conversion module in 1908) may be performed, where one or more sweeps of the received signal are compared with one or more sweeps of the transmitted signal. The result is that the frequency in the sum and the difference between the transmit and receive frequencies The following is obtained.

[0149] A triangle as an example of a signal transmitted from the phone using the transmission module at 1902. The chirp is phase continuous (to ensure inaudibility) and A processor controls processes according to modules executed on a device (e.g., a mobile device). It is designed to allow looping audio data through the speaker(s). An example triangular chirp is shown at 48 kHz for both the upward and downward sweeps. 1500 samples are used. The resulting upward sweep time is 31.25 ms (which is , and the same can be said for the downward sweep), so that both the upward and downward sweeps are continuous. When used in 2Hz and both the upper and lower sweeps are averaged (or alternate sweeps) The frequency is 16 Hz when the frequency is 16 Hz (when only the frequency is used).

[0150] By moving one sweep per iteration (i.e., introducing overlap), If more than 1 sweep (e.g., 4 sweeps) are used, the calculation is performed for each block. can be repeated. In this case, the extraction of the correlation envelope, a method (e.g., filtering , maxhold) or other methods (Hilbert transform) to find the correlation peak. can be optionally estimated, for example, as a standard deviation from the average of the relative peak positions. It is also possible to remove outliers by removing values ​​that deviate from the normal deviation. During each sweep of the waveform, the relative peak position mode (with outliers removed) is used to The initial index can be determined.

[0151] The reason why some correlation metrics are lower than others is because some of the signals affect the transmit waveform. Intermittent unwanted signal processing or or indeed large signals in the indoor environment (causing the signal to be "drowned out" for a short period of time). There can be audio.

[0152] Synchronization module in 1906 allows positioning in degree increments up to 360 degrees By checking the correlation with the phase-shifted template, This allows for more precise phase level synchronization (without requiring phase level synchronization). The offset is inferred.

[0153] Unless you have extremely precise control of the timing of your system (i.e., sample level (unless the system delay between the This is desirable and does not result in noise or errors in the output.

[0154] As mentioned above, the down-conversion process is performed in the module 1908. Once the data is received, a baseband signal can be generated for analysis. Once synchronized, the down-conversion can be performed. In this case, the transmitted and received signals (generated and reflected sounds) and various A "beat" signal is extracted by synchronizing the signal with a sub-module or process. An example of this audio processing is shown in Figure 20. In this figure, the I transmit signal and the Q transmit signal are The phase offsets determined using the fine-grained synchronization method described above are then generated for mixing. For example, as in the case of the module or process in 2004, A "nested loop" can be repeated for this mixture, i.e., the outer loop is Repeated for each sweep in the current received waveform held in memory. The access process is applied to the mix in 2004 (each upper and lower chart (each loop is considered a separate sweep), then the inner loop follows the I channel. This is repeated for the Q channel.

[0155] In another example of a system (not shown in FIG. 20), multiple receive sweeps are optionally The buffer is then used to calculate the median, resulting in a moving median graph for the buffer containing multiple sweeps. This median is then multiplied by the current sweep to provide another way to detrend the signal. Subtract from the group.

[0156] For each sweep (up or down), the phase-shifted transmit chirp and 2004 The relative position of the received waveform (using the sync sample index as a reference) is mixed at Once both the transmit and receive sections are generated, these waveforms are 004. For example, in the downward sweep, the received Mix the downward sweep with the TX downward sweep and add the flipped received downward sweep to the T X Mixes the upward sweep or flips the received downward sweep It can be seen that mixing with a TX downward sweep is possible.

[0157] The output of the mixing operation (e.g., the multiplication of the received waveform with a reference alignment waveform (e.g., a reference chirp)) When low-pass filtering is performed on the As in the case of a signal processor module, higher sum frequencies are removed. In this implementation, a triangular chirp of 18-20kHz is used with a sampling rate of 48kHz. For such frequency bands, the higher sum frequency in the mixing operation is actually undersampled, so there are alias components at about 11-12 kHz. (When Fs=48kHz, the total is about 36kHz.) The low-pass filter is , this alias component (if it occurs in a particular system implementation) is reliably removed. The remaining signal components can be configured to determine whether the target is stationary or moving. It depends on the target motion (e.g., breathing signal) at a given distance. In this case, the signal includes a "beat" signal and a Doppler component. due to the difference in frequency position between the transmit sweep and receive sweep (due to the delay in the reflection time of the Doppler is the frequency shift caused by a moving target. Essentially, this beat is the time from the point at which the received signal arrives at the end of the transmitted sweep. Only by selecting the section of the sweep, calculations are necessary. This is difficult to do in complex cases (e.g., humans). According to some aspects of the present disclosure, more sophisticated systems may be used. In the adaptive algorithms that can be used in the By extracting the appropriate portion of the loop, the accuracy of the system is further increased.

[0158] In some cases, in mixed signals, detrending in (2010 Optionally, the mean (average) may be removed (mean detrended), or and / or for the removal of unwanted components that may ultimately be present in the baseband. Other detrending operations can be high-pass filtered, such as the HPF process in , can be applied to mixed signals (e.g., linear detrending, median detrending). 200 The level of filtering applied in 8 depends on the transmitted signal quality, echo strength, and environmental It may vary depending on the number of interferers, static / multipath reflections, etc. In higher quality conditions, Respiratory and pulmonary function signals are present in the low frequency envelope of the received signal (and in the resulting demodulated baseband signal). Since the image may contain other motion information, the amount of filtering may be reduced. In problematic situations, noise in the mixed signal can be significant, so filtering It is desirable to increase the number of times this filtering module is performed in 2008. To select an appropriate filtering level in , signs of respiratory signal quality) as feedback signals / data to these filtering processes. This can be fed back to the process / module.

[0159] After down-conversion in 1908, breathing, presence / absence, whole body movement and activity For motion extraction, a two-dimensional (2D) analysis of the complex FFT matrix is ​​performed in 1910. Thus, the downconverter can generate a frequency domain transformation matrix For this kind of process, (e.g., Hanning window module in 2014) A block of mixed (and filtered) signal is windowed (using Next, for the estimation and generation of the "2D" matrix 2018, Perform a Fourier transform (e.g., Fast Fourier Transform or FFT) on the The FFT of a group of FFTs, each row is an FFTbin. Since it is a FT bin, it is called a "range bin." Next, (each matrix is ​​I channel a set of orthogonal matrices (based on Q-channel information) are processed by the 2D analysis module.

[0160] An example of such processing is illustrated with reference to the modules shown in Figure 21. As part of this 2D analysis, body (including limb) movement and activity detection are performed on these data. The operation of the processing module(s) in 2102 and In the estimation of activity, the subject's motion and the complex (I+jQ) mixture signals in the frequency domain are From the analysis, information about the activity is extracted. This includes whole body movements (e.g., rotation or The findings were that the movements of the limbs (i.e., the movements of the hands and feet) were uncorrelated and much larger than the chest displacement during breathing. Independently of (or depending on) this motion processing, multiple signal components may be configured. A quality value is calculated for each range bin. These signal quality values ​​are for the I channel and Both amplitude and unwrapped phase across both Q channels are calculated.

[0161] The processing at 2102 generates an “activity inference” signal and a motion signal (e.g., a body motion frame). The “body movement frame” generated by the activity / movement processing in 2102 The "BMF" (Best Before Lag) is a binary flag that indicates whether motion has occurred (output at 1 Hz). 2104, the "activity count" is generated with the counter process module. , which is a measure of activity from 0 to 30 in each epoch (output in 1 / 30 seconds). If you do, the "activity count" variable will be updated every 30 seconds for the specified period of the 30 second block. The amount (severity) and duration of body movement in the event of a crash are captured, and the "Movement" flag is set to It's a simple "yes" / "no" response to movement and updates every second. Activity count generation The raw "activity estimate" used in this study is that the mixed chirp signals are mutually correlated in the absence of motion. , which is an estimated measurement based on the fact that there is no correlation during the movement period. Since the loop represents a complex frequency domain, it is necessary to take the modulus or absolute value of the signal before Human correlation can be used to analyze the four chirps across the range bin of interest. The calculation is performed on a series of spaced mixed upward sweeps. This corresponds to 125 ms for a transmit waveform of 1500 samples at kHz. By spacing the chirps in this way, the velocity profile being investigated is determined, This correlation output can be inverted, for example by subtracting from 1, so that it can be adjusted as needed. Therefore, a decrease in correlation is related to an increase in the metric. After the maximum value per second is calculated, the short-term 3-time The signal is sent through a step-by-step FIR (finite impulse response) boxcar averaging filter. For devices like this, the response to motion calculated using this approach is essentially non-linear. It may be linear. In such cases, the metric is remapped through a natural logarithm function. The signal is then calculated based on the maximum correlation observed over the preceding N seconds (corresponding to the maximum correlation observation). After being detrended by subtracting the smallest value, the logistic regression is performed with a single weight and bias term. The resulting raw activity estimates are fed into a 1 Hz regression model. A binary motion flag is generated by applying a threshold to the raw activity estimation signal. The count generation is based on the raw activity estimates of 1Hz above threshold and the values ​​(from 0 to 2.5) every second. This is done by comparing it with a non-linear mapping table (selected from the following table). Sum for 0 seconds and limit at a value of 30 to generate activity counts in each epoch. .

[0162] Therefore, a 1-second (1 Hz) motion flag is generated along with the activity intensity estimate. The activity / movement module processes and the activity counter in 2104 Generated with an associated activity count for the value 30 (total activity in a 30-second epoch) .

[0163] Another aspect of the 2D processing in 1910 is the distance of the object from the sensor (or If there are two or more objects within range of the object, the calculation of several distances is This can be done by a range bin selection algorithm. In the next step, the resulting two-dimensional (2D) matrix 2018 is processed to generate the target bins (single 1D matrix(es) in the number or numbers of pixels are obtained. Although the module output of such a selection process is not available, the processing module for 2D analysis Notification to the rule may be made (e.g., extraction process in 2016, calculation in 2108). (either the process in 2110 or the respiration determination process in 2110). If the phase is unwrapped at the beat frequency, the phase will be unwrapped (if the phase unwrap problem does not occur). The desired vibrational movement of the target is returned (assuming the target is not moving). Optional phase unwrap error detection to mitigate possible jump discontinuities in the signal (and possible collection) can be made. However, in practice, no useful information can be derived from these "errors." These "errors" may be related to the movement in the signal (normal overall (This can be used to detect larger movements such as body movements.) If the input signal is only very low amplitude noise, unwrapping may be ineffective, so this A flag can optionally be set for the duration of the "below threshold" signal .

[0164] The start and end times and the position (i.e., range) of the movement are combined into a 2D unwrapped It can be detected in the phase matrix through various methods. For example, This can be achieved using thresholding after 2D envelope extraction and normalization. The normalization time scale is large enough to exclude the altered phases due to movements such as breathing. configured to be as large as possible (e.g., for a minimum respiratory rate defined as 5 bpm) , >>k*12 seconds). Such motion detection is an input trigger for the bin selection process. For example, the bin selection algorithm may be used to select the leading range during the motion period. A bin can be "locked" so that the next valid respiratory signal is in or near that bin. The bin may be held until the subject is detected, so that the subject may be able to breathe quietly and then lie down on the bed. If a fish moves in and then lies down quietly, these behaviors will all occur within the same range bin. Therefore, the computational overhead can be reduced. The start and / or end positions can be used to limit the search range for bin selection. This can also be applied to multiple target monitoring applications (e.g., two targets in a bed). When subjects are in a room and monitored simultaneously, the movement of one subject can affect the respiratory signal of the other subject. Although the number may be tainted, using such lock-in bin selection Even if the subject is moving significantly, the valid range bins (and this respiratory signal) of both subjects Recovery of the original (signal extraction) can be supported.

[0165] This approach results in a phase-unwrapped vibration-generated signal. This complex output allows the detection of live human or animal body movements for further processing. A demodulated baseband IQ signal containing (e.g., respiratory) data is obtained. In an example, the system may process the linear upward and downward sweeps separately (e.g., as separate IQ pairs (e.g., for increased SNR and / or separation of inspiration from expiration) In some realizations, possible coupling effects in the Doppler signal Optionally, each (upward sweep and downward sweep) can be used for averaging or range estimation improvement. The beat frequencies from the sweep (up and down sweep) can be processed.

[0166] The baseband SNR metric is based on the 0.125-0.5 Hz respiratory noise band (which is , equivalent to 7.5br / min to 30br / min, but approximately 5-40br depending on use case / min - i.e., primary respiratory signal content) and 4-8 Hz movement noise. The time domain may be configured to compare the time domain with a band (i.e., a band that primarily contains non-respiratory motion). Here, the baseband signal(s) are sampled at 16 Hz or higher. High-pass if baseband content is below 0.083 Hz (equivalent to 5 breaths / minute) Heart rate information can be filtered out (25 beats per minute to 200 beats per minute). The frequency band may be within the range of approximately 0.17 to 3.3 Hz (equivalent to 100 Hz / min).

[0167] During sonar FMCW processing (i.e., by subtracting adjacent guesses) The difference between the beat estimates can be optionally taken to filter out (remove) static signal components. Cut.

[0168] In this approach, the range bin selection algorithm selects one or more In this case, the user may be configured to track range bins corresponding to the target's position. Depending on the data provided about positioning from the possible range bins, partial or A comprehensive search may be required, for example, to determine how much data a user collects from their device while sleeping (or sitting). If you mention how far away something is, you can narrow the search. The blocks or epochs of data under consideration can be 30 seconds in length and must be non-overlapping. (i.e., one range bin per epoch). In other implementations, longer epochs are used. The length of the range bins may be used, and overlap may be used (i.e., multiple range bins per epoch). (Guessed value).

[0169] Detection of valid respiration rates (or probability of respiration rates across a specified threshold) is used, The minimum possible window length is limited because it must be at least one respiratory cycle. should be included in the epoch (e.g., if the respiratory rate is extremely slow, 5 breaths / min, (implying one breath occurs every 12 seconds). Therefore, the typical limit is 45-50 br / min. For example, removing the mean, optionally creating a window, and then performing an FFT. , the peak(s) associated with the desired breathing band (e.g., 5-45 br / min) The power in this band is extracted and compared to the overall signal power. The relative power (or maximum peak / average ratio) is compared to a threshold to determine whether a candidate breathing frequency exists. In some situations, several candidate bi- ... These can occur due to room reflections and are clearly visible in several ranges. This can lead to irregular breathing (which can also be related to FFT side lobes).

[0170] Processing of the triangular waveform eliminates such range uncertainties (e.g., due to Doppler coupling). This can help reduce the impact of reflections that may be better than a direct path for a period of time. If so, the system can select it. If the room has soft furnishings (e.g., bed and curtains), In this case, it is unlikely that the indirect reflections will result in a higher signal-to-noise ratio than the direct component. The primary signal may be a reflection from the surface of the quilt, so the actual estimated range must be taken into account. This may appear slightly closer than the human chest.

[0171] To reduce processing time, knowledge of the estimated range bins of the previous epoch is used to estimate the range bins of the subsequent epochs. It can be seen that it can be used for search notifications. For systems where analysis of the timescales is not required, longer timescales can be considered.

[0172] 1.1 Signal Quality The respiration signal quality (i.e., detection and association quality) is measured using the 200 in FIG. 20 as described above. 8, and the filtering of the module 2108 of FIG. Various signal qualities calculated as part of the FFT2D metric, as shown in the computational process These metrics can be used in the respiration determination process at 2110. Optionally, the 2D analysis process in FIG. and a procedure for determining absence / presence or sleep staging based on some intermediate value. It may include a logic or module.

[0173] For computational processing at 2108, various metrics (e.g., respiration estimates) are determined. In this example, four metrics are shown in FIG. 1. "I2F" - In-band squared (I) over the full band 2. "Ibm" - In-band (I) metrics only 3. "Kurt" - a metric based on the kurtosis of the covariance 4. "Fda" - Frequency Domain Analysis

[0174] 1. "I2F" The I2F metric can be calculated as follows (a similar approach is used for inBand This can be applied to the Q (quadrature) channel, which can be called PwrQ):

number

[0175] "inBandPwrI" is the overall output from, for example, about 0.1Hz to 0.6Hz. (e.g., within a selected target respiratory band). "fullBandPwrI" is the This metric is used to compare the full-band and in-band outputs. Similar to the previous method, the major difference is that it uses the full-band output for noise-in-band estimation. It is clear that this is a premise.

[0176] 2. IBM The following metrics do not make the same assumptions as I2F and are based on the estimated direction of signal and noise in-band. This allows for the peak power inband to be found (e.g., on each side of the peak). This is done by computing the output around this (by taking FFTbins). Then, this signal output and the value obtained by dividing the peak value itself by the next peak value (widely used for sine wave type If breathing contains strong harmonics, reevaluate your bin selection. This prevents harmonics from being confused with noise components. The noise estimates are all those outside the signal subband that are within the breathing band:

number

[0177] 3.Kurtosis (“KURT”) Kurtosis provides a measure of the "tail" of the distribution, thereby helping to distinguish breathing from other non-breathing signals. For example, the signal quality output can be DC filtered (using an IIRHPF). is the inverse function of the kurtosis of the covariance (within a certain distance from the peak covariance) of the signal In poor signal quality conditions, this metric is set to "invalid."

[0178] 4. "Fda" "Fda" (Frequency Domain Analysis) can be performed. Such statistics are available for 64 seconds. can be calculated using overlapping data windows with one second step length The calculation uses retrospective data to provide a causal link. The process is performed within a specific respiration rate window. For example, the respiration rate can be detected in the 64, which is incorporated herein by reference in its entirety. For example, the respiration rate would be 6 to 40 breaths per minute (bpm), which corresponds to 0.1 to 0.67 Hz. This frequency band corresponds to a realistic human breathing rate. Therefore, "in-band" refers to the frequency range of 0.1 to 0.67 Hz. Each 64 second window contains 1024 (64 seconds at 16 Hz) data points. Therefore, the algorithm performs the following steps for each (I and Q) data window: Calculate the 512-point (N / 2) FFT for As will be described, the in-band spectral peaks (which can then be used to determine respiration rate) The in-band frequency range is used in the calculations for each 64-second window, as described below. Used to calculate respiration rate for cormorants.

[0179] Other types of "Fda" analysis are described below.

[0180] Other frequency bands may be considered for typical heart rates (e.g., 45 beats / min to 18 A HR of 0 beats / min corresponds to 0.75-3 Hz).

[0181] The spectral peak ratio may also be determined: maximum in-band peak and outside-band Peaks are identified and used to calculate the spectral peak ratio, which is the maximum intensity. This can be understood as the ratio of the maximum outside band peak to the maximum outside band peak.

[0182] In-band dispersion can also be determined. In-band dispersion quantifies the power in a frequency band. This may also be used for subsequent presence / absence detection in some cases. .

[0183] Spectral peaks are identified in the frequency band of interest through performance figures of merit. Therefore, the spectral power level in each bin, the frequency difference between the adjacent peaks and the bin, The distances are combined into the highest value bin of the figure of merit above.

[0184] Within the mobile device's sensing vicinity as part of 2D analysis in 1910 (Outline of) a computer process in 2108 to identify one or more living humans. The four metrics are calculated based on the distance from the sensor. Or, actually leaving the sensing space (to go to the bathroom) or returning to the sensing space. As a result, inputs for absence / presence detection and whether there is an interference source within the sensing range are determined. Next, for example, in 2110, in the process of the breath determination processing module, These values ​​may be further evaluated to determine the last respiratory rate for one or more subjects monitored by the system. A guess is generated.

[0185] 1.2 Absence / Presence Inference As shown in Figure 19, 2D analysis at 1910 allows for absence / presence inference and It may also be possible to process such inferential output signs as: detection is used to determine whether an object is absent or present in the sensor range. Depending on the signal quality detection, the "absence" may be detected within 30 seconds or less. triggered (clearly distinguished from apnea). In some versions, absence / presence Presence detection can be viewed as including methods of feature extraction and subsequent absence detection. A flow diagram of such a process is shown in Figure 22. This flow diagram shows the process in 1910. Some of the computed features / metrics / signals of the process may be used, but the Others listed above may also be calculated, such as human presence (Prs) including one or more of the following: In determining the absence of a person (Abs), various types of features can be considered. 1. Non-zero activity count 2. The current 2D signal (breathing vs. range) and the preceding window (breathing vs. target sensing range) Pearson correlation coefficient between 3. Maximum value of I / QIbm quality metrics for the current breathing window 4. Maximum variance of I / Q respiration rate channels over a 60 second window 5. Maximum variance of I / Q respiration rate channels over a 50 second window

[0186] These features are extracted from the current buffer and then analyzed (e.g., over 60 s) These features are then preprocessed by taking percentiles in the length of the Combined, we obtain a logistic regression model. This model is To limit false positives, we can use this for example with short wins. The probability can be averaged over a period (several epochs) to produce a final probability estimate. The duration of movement that is considered to be related to physical movement (human or animal movement) ,presence is asserted preferentially. In the illustrated example of FIG. 22, the activity count, IBM, The Pearson correlation coefficient and absence probability are compared with appropriate thresholds to determine whether a person is present or absent. In this example, presence is determined by whether there is at least one positive evaluation. It is sufficient that each test evaluates as negative to determine absence.

[0187] Looking at the overall system diagram, movements (e.g., limb movements) and ultimately turning over in bed occur. This can easily disrupt the respiratory signal and produce higher frequency components (however, this These components are separable when considering "2D" processing. In the example segment of 2D data (top panel) shown in 23C, two subsections ( 23B and 23C) show a computer with a microphone and a speaker. Each trace or signal on the graph represents the activity of a person within the audio sensing range of the audio device. A mobile device having the applications and modules described herein may be used. The figure shows sound detection at different ranges. It is possible to visualize the movement over time over a range of distances. and the person is positioned approximately 0 In area BB and Figure 23B, the person is breathing at 0.3m. (whole body movement), leaving the vicinity of the sensing area (e.g., leaving the room and going to the bathroom) After a while, in relation to area CC and FIG. 23C, the person moves closer to the sensing area or room. Returning to your next door (e.g., returning to your bed (full body movement)) but slightly further away from your mobile device In this situation (at about 0.5 m) with your back to the mobile device, At the simplest level, the breathing pattern is visible. and the complete absence of a breathing trace or whole body movement indicates absence from the room for a period of time. show.

[0188] Such motion detection is an input to sleep / wake processing (e.g., 1912 in FIG. 19). in the processing module), and sleep state can be inferred and / or wakefulness Or a sleep indicator may be generated. If there is a large movement, the range bin selection changes. It is also more likely to be a precursor to a target (e.g., if the user has just changed position). On the other hand, if an SDB event is detected from respiratory parameters (e.g., recognizable Possible apnea or hypopnea), in which breathing may be reduced or stopped for a period of time.

[0189] If a velocity signal is required (which may depend on the end use case), send an FMCW chirp signal A different approach to processing the received The signal may arrive as a delayed chirp and an FFT operation may be performed. The signal may then be divided into two parts: Multiplying can be done to cancel the signal and preserve its phase shift. can be determined using multiple linear regressions.

[0190] Graphically, this method plots phase angle in radians against frequency. This generates the FMCW gradient. The gradient finding operation needs to be robust to outliers. If the 18kHz to 20kHz sequence FMCW chirp is 10ms, The effective range is 1.8 m and the distance resolution is 7 mm. Recovered baseband signal To extrapolate the above point, overlapping FFT operations are used.

[0191] In yet another approach to processing FMCW signals as one-dimensional signals, A comb filter can be applied to the synchronization signal (e.g., a block of four chirps). is a direct path from Tx to Rx (direct speaker to mic component) and static reflections (clutter ) is removed. Next, FFT is performed on the filtered signal. Then, a window generation operation is performed. Then, after the second FFT, the maximum ratio combination is performed. can be carried out.

[0192] The purpose of this process is to identify the vibrations in the side lobes and their output velocity (rather than displacement). One advantage is that it detects 1D signals (without complex bin selection steps). directly inferred, which is possible when there is a single source of motion (e.g., a single person) within the sensor's field. There is a point where the most likely range bins can be guessed at.

[0193] Such FMCW algorithm processing techniques are used to solve the 2D complex matrix (e.g., various Radar sensors using various types of FMCW chirps (e.g., sawtooth, ramp, triangular) It can also be understood that the present invention may be applied to a signal output from a signal source.

[0194] 5.1.3 Additional System Considerations - Speakers and / or Microphones According to some aspects of the present disclosure, the speaker and microphone may be on the same device. (e.g., on a smartphone, tablet, or laptop), or , may be provided on different devices with a common or otherwise synchronous clock signal; , two or more components communicate via a voice channel or by other means (e.g., the Internet) If synchronization information can be communicated via In some solutions, the transmitter and receiver may use the same clock, or a special No synchronization techniques are required. Another method for achieving synchronization is the Costas loop or PLL. (phase-locked loop) (i.e., the transmitted signal (e.g., carrier signal) ) approach that allows you to "lock in" to

[0195] To minimize the possibility of unwanted low frequency artifacts, Any bias in the audio path is considered to understand the cumulative effect on the synchronization of the In some cases, buffering can be important to consider, e.g., in telephone headsets. Use a microphone / mic plug-in device (e.g., for hands-free calling) This allows you to place the microphone and / or speaker near the bedclothes or on the bed. In some cases it may be desirable to place it inside the cloth. For example, Apple phones and I have a device that normally ships with an Android phone. I'm having trouble with the calibration / setup process. In order to fit your system / environment setup, the system requires (If you have more microphones than 100, choose the appropriate mic, speaker, amplitude and frequency settings.) You need to be able to choose between

[0196] 5.1.3.1.1 Calibrating / Adapting the System to Composition Changes and Environment The particular phone (or other mobile device) used, the environment (e.g., bedroom) and and to optimize system parameters for the system's user(s). It is desirable to implement the technique in a way that the device can be portable (e.g., to another (can be moved to living spaces, bedrooms, hotels, hospitals, care homes) within the sensing field The system can be adapted to one or more of the following biological systems and can be compatible with a wide range of devices: It implies learning over time, which indicates equalization of the audio channels.

[0197] The system learns (or or actually pre-programmed by default) to adapt to channel conditions. Device and model specific characteristics include speakers and and microphone baseline noise characteristics at the intended frequency(ies). the ability of the mechanical components to oscillate stably in response to the target waveform; The signal response of the receiving microphone(s) is also a factor. For example, FMCW In the case of chirps, the magnitude of the received direct path chirp is used to estimate the sensitivity of the system. (for example, the performance of the speaker and microphone combination at a given phone volume) can be related to the sound pressure level (SPL) output from the speaker / transducer in automatically adjusts signal amplitude and / or phone volume to achieve desired system sensitivity As a result, the system must adapt to the large variety of smart devices (e.g., This will enable support for the large and heterogeneous ecosystem of Android phones. This allows you to check if the user has adjusted the master volume on the phone and automatically readjust this. When the system needs to be adjusted to new operating conditions It can also be used when updating to a different operating system (OS). also have different characteristics (e.g., audio buffer length, audio path latency, and Tx and / or or occasional dropouts / jitter in the Rx stream(s) It can be the cause.

[0198] Key aspects of the system are captured (e.g., ADC and DAC quantization levels ( available bits), signal-to-noise ratio, simultaneous or synchronous TX and RX clock generation, and room temperature and humidity (if available). For example, (at 44.1 kHz If the device accepts samples at 48kHz, it will select 48kHz as its optimal sampling rate. In this case, it may be detected that the optimal resampling The sampling rate is set as 48kHz. The estimation is shaped by the dynamic range of the system. and the appropriate signal combination is selected.

[0199] Other characteristics include the separation (angle and distance) between the transmitted and received components (e.g., distance between transmitter and receiver), as well as any active automatic gain control (AGC) and and / or active echo cancellation configuration / parameters for the device (especially A telephone that uses multiple active microphones is a signal that is disrupted by continuous transmission of signals. Processing measurements may be performed, which may lead to unwanted oscillations in the received signal. You may need to correct this (or disable any AGC or echo cancellation that you don't actually need). (Adjust device configurations if possible to accommodate this).

[0200] The system measures the reflection coefficient of various materials versus frequency (e.g., the reflection coefficient at 18 kHz). The case of echo cancellation will be considered in more detail. In the case of W (single tone), the signal is continuous, so Unless there is perfect acoustic isolation (which is unlikely), the TX signal will be much stronger than the RX, causing the system CW systems can be adversely affected by the built-in echo canceller in the telephone. A strong amplitude modulation may be experienced due to the activity of the C system or of the basic echo canceller. Certain handsets (Samsung S running on the "Lollipop" OS) 4) In the example of a CW system above, the raw signal contains amplitude modulation (AM) of the returned signal. One strategy to address this issue is to use very high frequency AGC on the raw samples. There are ways to go about smoothing AM components that are not related to respiratory motion.

[0201] For different types of signals (e.g. FMCW "chirps"), the implementation is tailored to the device. In fact, the echo canceller implemented in the (A)FHRG or UW The B approach is also robust to speech-oriented acoustic echo cancellers. In the case of FMCW, chirps are short-term non-stationary signals that vary in time and motion. It provides instantaneous information about the room and the activity within it, allowing you to The counter tracks this with a certain delay, but is able to see the returned signal. However, this behavior is related to the exact implementation of the third-party echo canceller. Generally speaking, any software changes over the duration of the physiological sensing Tx / Rx usage Use software or hardware (e.g. in the CODEC) echo cancellation (if available) It is advisable to disable it (if necessary).

[0202] Another approach is to use continuous wideband UWB (ultra-wideband) signals. In this UWB approach, if there is no good response at a particular frequency, It has high resilience. Wideband signals are confined to the non-audible band or are transmitted within the audible band. Such signals may be disturbing to humans or animals. The window allows for low amplitudes that are not desirable, and the sound is more pleasing to the ear. It can optionally be "shaped".

[0203] One method for generating inaudible UWB sequences is to generate audible probing sequences. Sequences (e.g., maximum length sequences) (MLS - a type of pseudorandom binary sequence) ) and modulate it up to the inaudible range. Good (e.g., if it can be masked by existing audio (e.g., music)). Because the sequence must be repeated, the resulting sound is purely flat space. It is not a vector white noise. In fact, it is a slow The pulses can be timed (while varying) to produce sounds similar to those produced by commercially available voice machines. It may be narrow in the interval or narrow in the autocorrelation function. The "magic" sequence is periodic in both time and frequency. LS has excellent autocorrelation properties. By estimating the impulse response of the room, Subsample motion for recovery of the subject's breathing signal in the room. This can be done using the group delay extraction method. As an example, the centroid (first moment) is calculated as the proxy of the filtered impulse response. (i.e., the group delay of a segment is the sum of the impulse response corresponding to the center of gravity).

[0204] (For example, when an "application" is first installed on a smart device or hardware The channel model may be generated automatically (when first used) or by the user. may be manually signaled (initiated) by the , adapt as needed.

[0205] Interrogate the room using correlation or adaptive filters (e.g., echo cancellers). Usefully, the bits that cannot be cancelled are mainly caused by movement in the room. After estimating the room parameters, the Eigenvalue (derived by optimizing the objective function) A filter can be used to transform the raw signal, which is then matched to the impulse response of the room. In the case of pseudo-white noise, the body movement data is also logged, and the environmental noise is It can shape the signal to the frequency characteristics of the noise to improve your sleep experience. For example, an automatic frequency assessment of the environment may identify unwanted noise at certain frequencies. Standing waves may be evident and such standing waves (i.e., resonant frequencies (air medium / pressure noise) In order to avoid maximum and minimum movement points during node and antinode The frequency is adjusted.

[0206] In contrast, flutter echo affects sounds above 500 Hz. This results in significant reflections from hard surfaces, parallel walls including drywall and glass. Therefore, active noise is used to reduce or cancel unwanted reflections / effects in the environment. Size cancellation may be applied. For device orientation, the optimal position of the phone (i.e. The system setup can provide a warning to the user for the purpose of maximizing the SNR. To do this, the phone's loudspeaker must be aimed at the chest (within a certain distance range). Calibration may require adjustments by manufacturers or service providers that may change the system acoustics. It also allows for the detection and correction of the presence of third-party phone covers. If interference appears to be occurring, the user may be asked to clean the microphone opening. (For example, lint, dust or other material may have accumulated in the microphone opening of the phone, causing can be cleaned).

[0207] According to some aspects of the present disclosure, a continuous wave (CW) approach may be applied. Range-gated systems using FMCW, UWB, or A (FHRG) ), in CW a single continuous sine wave tone is used. In unmodulated CW, The Doppler effect can be used when an object is moving (i.e., the return frequency is The Doppler effect is used to estimate distance, but the frequency is shifted away from the CW is used when, for example, one person is in bed and there are no other sources of motion nearby. In such cases, a high signal-to-noise ratio (SNR) can be obtained. The key scheme is a single tone (without the frame itself) for baseband signal recovery. It can be used in special cases.

[0208] Another approach is adaptive CW, which (in practice) adjusts the Tx power and room reflection. For example, the limited range of the image that detects the nearest person in bed is It is not particularly range-gated (although it may have a range), and indoor mode is available. Adaptive CW is a continuous transmitted signal in the inaudible range within the capabilities of the transmitting / receiving equipment. By scanning non-audible frequencies in the step, The algorithm provides the best possible (both in frequency content and time domain morphology (breathing shape)) Iteratively search the frequencies for available respiratory signals. If there is a gap between the two, the demodulated respiratory waveform may have a significantly different shape, and a clear morphology may not be obtained. , ideal for apnea (central and obstructive) and hypopnea analysis.

[0209] Holography involves wavefront reconstruction. Extremely stable acoustic transducers are used in holography. The coherent source is the loudspeaker, and the energy due to reflections It takes advantage of the fact that the frequency is stored in the room (i.e., it is out of mode and actually (No standing waves are generated using hopping and / or adaptive frequency selection) In contrast to other approaches, the CW signal is specifically chosen to have strong standing waves. .

[0210] For systems with more than two loudspeakers (e.g., optimal for one person in a bed), This allows the "beam" (to be detected) to be adjusted or steered in a specific direction.

[0211] Further examining Figure 7, we consider offline analysis (e.g., offline analysis that reads and discards raw audio data). For later offline analysis (for online processing systems), a high pass filter 702 If the data is later saved (e.g. HP using the 3 dB point at 17 kHz) F), the blocked (removed / filtered) data in the stop band has Since the audio contains conversational information, high-pass filtering acts as a privacy filter. It can be done.

[0212] 5.1.3.1.2 Multiple cooperating or non-cooperating devices and interference sources Indications for The system may include multiple microphones, or may be connected to a nearby system. In some cases, they may be required to work with a system (e.g., to monitor two people separately). (Two phones running the same application are located on either side of a double bed) In other words, the use of channels or other means (e.g., the Internet) to enable coexistence. In environments using multiple transmissions (data transmitted over a network or wireless signals), In particular, this is because the coding sequence Or (in the simplest case) modulating a single sine wave, Whether you choose to detect at 10 Hz and transmit at approximately 19 kHz, This means that the device can adapt its waveform to the specified band. This setup mode may include a setup mode that, when activated at power-up, Signal analysis of nearby sounds (e.g. frequency analysis of sounds received by a microphone) It checks for audio signals in the environment or in the vicinity of the device and, in response to this analysis, different frequency ranges for (e.g., non-overlapping sets of frequencies from received audio frequencies) In this way, multiple devices can be connected (each device in a different frequency range). In a common neighborhood (generating an audio signal), the audio generation and modulation techniques described herein In some cases, different frequency ranges may operate in conjunction with the low ultra-low power techniques described herein. It may still be in the sonic frequency range.

[0213] FMCW on a single device can detect multiple people (vs. CW, for example) ) It is highly unlikely that FMCW will be executed on more than one device in close proximity to one another. However, to maximize the available SNR, more than one FMCW transmission may be located nearby. If it is running on frequencies (e.g. 18-19kHz and 19.1-20.1kHz) Hz) or time-overlapping (multiple chirps occupy the same frequency band) but have non-overlapping quiet periods and guard bands to allow dispersion of reflections from other devices. The bandwidth can be adapted automatically (or through user interaction) so that .

[0214] (A) When FHRGs are used with tone pairs, they are It can be seen that the frequency dithering can be done by changing the frequency shift between frames. Time dithering indicates a time-of-flight variation (based on a change in pulse duration) This approach to one or both dithering methods means that The key is to reduce the probability of room modes being generated and / or to Sonar systems may coexist within "hearing" distance of each other.

[0215] The FHRG system releases a pseudo-random sequence that defines the tone / frame. may be switched (provided that the transition is made so as not to introduce audible harmonics into the resulting transmitted signal). (or appropriate comb filtering should be applied to remove / attenuate unwanted sub-harmonics) It is also understood that (assuming)

[0216] If the TX and RX signals are generated on different hardware, a common clock is available. Collaboration is required because two or more devices may not be able to "hear" each other. In the case of distance, it is optimal to use cooperative signal selection to avoid interference. It becomes possible to adapt the transmitted signal to optimize return from the bedclothes.

[0217] 5.1.3.1.3 Adapting to User Preferences A simple audio sweep test allows the user to determine the lowest frequency that cannot be heard (e.g., 17.5 You can select the frequency (6kHz, 19.3KHz, 21.2kHz). can be used (with a minimum guard band offset) as the start frequency of the signal generated. To check whether your pet mouse or other pets respond to the sample sounds, A backup process may be provided. If a dog, cat, or pet mouse responds to the sample sound, In this case, it may be preferable to use a low amplitude audible (to humans) audio signal together with the modulated information. "Pet Setup Mode" allows you to check (for example) if your dog responds to a specific sound. The user can record the fact of such a reaction, which This allows the system to check different sounds to find a signal that does not cause discomfort. Similarly, if the user finds it annoying, the system will suggest a different signal. Can be configured for signal type / frequency band. Masking noise ("white noise" / hiss) for sedation and / or calming is desired In this case, a white noise feature containing an active signal TX can be useful. The code may cycle through one or more test audio signals, and the code may be used to generate a test signal (which may be inaudible to humans). The user may be prompted to input whether there were any problems with the audio signal, and the frequency used may be adjusted based on the input. You can choose the number.

[0218] 5.1.3.1.4 Automatic Pause of Tx Playback When User Interacts with Device The system may be playing high amplitude inaudible signals, which may cause the user to When interacting with a mobile device (e.g., a smart device), mute this ( In particular, when the device is in a The device may need to be silenced if picked up near the user's ear. Of course, after the signal muting is removed, the system will need to be resynchronized. This can lead to battery drain. In that case, the following approach can be used: If the system is running, the system will only run if the device is powered. Therefore, the system must be able to detect whether the smart device is connected to a wired or wireless charger. The device may be designed to sleep (or not wake up) when not connected to During Tx rest (and processing) when using the service, input is taken from the user's phone interaction ( Button presses, screen touches, (built-in accelerometer if present) and / or gyroscope Phone movement (detected via the GPS and / or infrared proximity sensors) or GPS S or Assisted GPS, or incoming call) In the case of notifications (e.g., text or other messages), the device may If the device is not in "rental" mode, the system This may result in a temporary pause in Tx, in anticipation of the issue being picked up.

[0219] At startup, the system detects that the phone is face down and the user is using the phone. May wait a period of time before activating Tx to check for any interactions If a distance estimation approach (e.g., FMCW) is used, the system will If detected in close proximity to the device, the minimum possible signal to meet the desired SNR level Tx power level or silenced Tx based on acoustic power utilization needs Furthermore, when a user picks up the device, the demodulated baseband signal Proactively and smoothly reducing Tx capacity using gestures recovered from and then silence the device when the user is actually interacting with it. or (when the user removes their hand from the device's vicinity) The capacity can be increased smoothly.

[0220] 5.1.3.1.5 Data Fusion For systems that collect (receive) audio signals with an active transmitting component, other patterns It may also be desirable to process the full-band signal for rejection or utilization of Conversation, background noise or other patterns that may swamp the TX signal In the case of respiration analysis, the data flow between the extracted audio waveform characteristics of the breath can be It is highly desirable to perform a direct detection of breathing sounds in the demodulated signal. It can be combined with other devices, especially in bedrooms (which are usually quiet environments). To prevent this, dangerous sleep or breathing behaviors, including vocal components (e.g., coughing, wheezing, snoring), Respiratory sounds can originate from the mouth or nose and include snoring, wheezing, gasping, This includes groaning and squealing sounds.

[0221] The entire audio signal is used to detect movements (e.g., full-body movements when a user lies down in bed). movement) and can be inferred from other background non-physiologically generated noise. Sonar-inferred motion (from processed baseband signals) and Full-band passive acoustic signal analysis and sonar movement (and activity inferred from that movement) and can typically be combined with FMWC in non-range specific passive acoustic analysis. (A) The advantage of range gating (range detection) in FHRGToF etc. Priority is given to the placement of fans too close together or the heating or cooling system being too noisy. If too large, the duration and intensity of the detected noise may lead to unwanted desynchronization. Acoustic fan noise can also provide insight into the sonar baseband signal analysis. It can also be seen as an increase in the 1 / f signal. The actual user voice is detected based on the noise floor. When possible, the system will automatically switch to a processing mode if the sonar Rx signal quality is too low. In the processing mode, physiological The audio is mapped to activity and directly drives the sleep degradation / wake detector. , providing feedback to the user regarding optimal fan placement and / or its operation. It is also possible to adapt the frequency to a band where interference from other noise sources (e.g. Fluorescent lamps / bulbs and LED ballasts) can be detected, and the system gives them higher priority. The device adapts to the sonar frequency(ies) of operation. In addition to the demodulation processing techniques described above, conventional audio processing methods are used to The received audio signal is processed to detect the ambient audio, conversation, and user movements and related characteristics. Any one or more of speech and breath sounds can be assessed.

[0222] Conversation detection is promoted to actively abstract transient data that may contain private information. It can also be used as a privacy feature. Clock ticking, TV, media streaming on tablet, fan, air conditioning unit noise, forced heating systems, traffic / street noise) Therefore, body movement information can be extracted from the audible spectrum and extracted from the demodulated scheme. This can be combined with data extracted from the

[0223] According to some aspects of the present disclosure, a light sensor on a device (e.g., a smartphone) This may allow for separate inputs in the system, where a person is trying to sleep Whether they are using a tablet, watching TV, or reading, Temperature sensors and / or humidity sensors available on the phone (or Location-based weather data) to enhance channel estimation / propagation of transmitted signals Interaction with the phone itself can improve the user's agility level and / or This can provide further information about the patient's condition or fatigue.

[0224] Understanding the motion of the sensing device via an internal motion sensor (e.g., a MEMS accelerometer) This can be used to disable the voice sensing process when the phone is in motion. Sensor data can be fused with accelerometer data to enhance motion detection. Cut.

[0225] The systems and methods described herein may be implemented by a mobile device. Although described as such, in other aspects of the disclosure, these systems and methods Note that the above may be performed by a fixed device. The described systems and methods may be used in bedside consumer monitoring or medical devices. flow generators (e.g., CP) for the treatment of sleep disorder breathing or other respiratory conditions; It can be executed by an AP machine).

[0226] 5.1.3.1.6 Cardiac Information As mentioned above, in addition to respiratory information, various versions of the techniques mentioned above also and reflex analysis to extract other periodic information (e.g., cardiac information) from the generated movement-related signals. In the example of FIG. 21, the cardiac determination process may be performed for any Cardiac peak detection based on ballistocardiogram (BBC) can be an optional additional module. This can be applied during a 2D processing stage based on FFT (similar to absorption detection), but with higher frequency bands. I'm watching.

[0227] Alternatively (as an alternative or in addition to FFT), wavelets (e.g., I signals) can be used. and Q signals in order) or separate complex wavelets Simultaneous processing using transformations is used for the 2D processing stage to extract the whole body motion signal, the respiration signal and and cardiac signals.

[0228] Time-frequency processing (e.g., wavelet-based methods (e.g., Daube The discrete continuous wavelet transform (DCWT) using the chies wavelet is It can be used for both band removal and direct body movement, respiration and cardiac signal extraction. Cardiac activity is reflected in signals at higher frequencies, with a passband range of 0.7 to 4 Hz (every Filtering with a bandpass filter (48 beats per minute to 240 beats per minute) This allows access to this activity. Activities that involve whole body movement are typically z ~ 10 Hz. Note that there may be overlap in these ranges. The (clear) respiratory trace can lead to strong harmonics, which need to be tracked to avoid confusion For example, signal analysis in some versions of this technology is described in International Patent Application Publication No. WO 2014 / 013994. 4 / 047310, which is incorporated herein by reference in its entirety. For this purpose, methods such as the wavelet denoising method for respiratory signals are used.

[0229] 5.1.3.1.7 System example Generally, the technology of the present application relates to monitoring-related methods (e.g., those described in more detail herein). Executed by one or more processors configured with the module's algorithm or method Thus, the present technology can be implemented as an integrated chip, one or more memories and / or other controls. The method described herein may be implemented in a program, data, or information storage medium. The programmed instructions, including any of the above, may be stored on an integrated chip in the memory of a suitable device. Such instructions may additionally or alternatively be coded on suitable data storage devices. The device may be loaded as software or firmware using a storage medium. The present technology may include a processor-readable medium or These processor-implemented programs may include computer-readable data storage media. The executable instructions, when executed by one or more processors, cause the processors to implement the functions described herein. In some cases, the server or other networked computing devices may be The data storage medium may include a data readable by or otherwise accessible to such a processor. The server may be configured to access a processor-readable data storage medium. The processor-executable instructions of the server-readable data storage medium are transmitted over a network. The device may be configured to receive a request to download the data to a processing device. Therefore, the present technology provides a server with access to a processor-readable data storage medium. The server may include a processor-readable data storage medium. A request to download executable instructions (e.g., via a network) Instructions are transmitted to a processing device (e.g., a computing device) or portable computer. a request to download the application to a computing device (e.g., a smartphone) The server then responds to this request by sending a computer-readable The device may then transmit the processor-executable instructions from the data storage medium to the device. The processor-executable instructions may be executed (e.g., the processor-executable instructions may be executed by another device). (when stored on a data storage medium readable by the processor of the

[0230] 5.1.3.1.8 Other portable or electronic processing devices As noted above, the audio sensing methods described herein involve the use of one or more processor or computing device (e.g., smartphone, laptop) , portable / mobile devices, cell phones, tablet computers) These devices are typically understood as being portable or mobile. However, other similar electronic processing devices may also be used with the techniques described herein. good.

[0231] For example, many homes and vehicles emit low frequency ultrasound, e.g., just above the threshold of human hearing. Includes electronic processing devices capable of outputting and recording sound in the sonic range (e.g., smartphones, speakers, active soundbars, smart devices, voice and other virtual (or other devices that support Assistant). Smart speakers or similar devices Typically, other home devices (e.g. for home automation) and / or wired or wireless communication with a network (e.g., the Internet). Wireline means (e.g., Bluetooth, Wi-Fi, Zig Bee, mesh, peer-to-peer) It includes a communication component via a peer-to-peer network. It is designed to simply output an acoustic signal. Unlike standard speakers, smart speakers typically have one or more processors and One or more speakers and one or more microphones (mic(s)) The mic(s) are integrated into the device to allow for personalized voice control. To interface with Regent Assistant (an artificial intelligence (AI) system) Some examples include Google Home, Apple HomeP od, Amazon Echo, and "OK, Google," "Hey Siri," "A These devices use voice activation using the catchphrase "lexa." Devices can be portable and are often intended for use in a specific location. The connected sensors in these devices are part of the Internet of Things (IoT). Other devices (e.g., active sound bars (i.e., microphones) smartphones), smart televisions (which may typically be stationary devices), and Mobile smart devices) may also be used.

[0232] Such devices and systems may be implemented using the low frequency ultrasound techniques described herein. It may be adapted to perform physiological sensing.

[0233] For devices with multiple transducers, beamforming can be performed That is, the direction of the signal transmitted to and received from the sensor array (e.g., speaker) Signal processing is used to enable sensitivity or spatial selectivity. wavefront (as opposed to medical imaging, which is "near-field") becomes relatively flat for low frequency ultrasound. In a purely CW system, the sound wave However, multiple transducers move from one to the other, reaching maximum and minimum areas. If a laser is available, it is advantageous to control this radiation pattern (known as beamforming). At the receiving end, multiple microphones are also used. This allows for directional control of sound detection (e.g., emitted It is possible to sweep an area after (manipulating sound and / or received sound waves) If the user is in bed, the sensing is directed to the object (e.g., two people in bed). The sense can be manipulated to direct it towards multiple targets when people are present.

[0234] As a further example, the techniques described herein may be implemented in a wearable device. It can be implemented (e.g., by a non-invasive device (e.g., a smart watch) or even by an invasive device devices (e.g., implant chips or implantable devices). These portable Devices may also be configured with the present technology.

[0235] 5.2 Other Notes A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner reserves the right to modify, revise, or otherwise modify this patent document. If any person reproduces this patent document or this patent disclosure by facsimile, the Patent Office's patent file If it is something that is recorded in the mail or record, there is no objection if it is for a specific purpose, but if it is for any other purpose, All rights reserved.

[0236] Unless otherwise clearly indicated by the context and unless a range of values ​​is provided, the lower limit 1 / 10 of a unit, between the upper and lower limits of the range, and any other stated value in the stated range It is understood that each intervention value for the intervention range is included in the present technology. The upper and lower limits of these intervention ranges specifically exceed the limits of the stated ranges. If the stated range includes one or both of these limits, In this case, ranges exceeding either or both of these stated limits are also encompassed by the present technology.

[0237] Furthermore, when values ​​are embodied in this specification as part of the technology, unless otherwise specified, To the extent possible, such values ​​may be approximated to the extent practical technical implementation allows or requires. It is understood that such values ​​may be used to any appropriate number of significant figures.

[0238] Unless otherwise defined, all technical and scientific terms used herein belong to the art. It has the same meaning as commonly understood by those skilled in the art. and any methods and materials similar or equivalent to the materials used in the practice or testing of this technology. Although a limited number of exemplary methods and materials can be used in the It will be published.

[0239] Although certain materials are described as being suitable for use in the construction of components, their properties may vary. Similar and obvious alternative materials may be used as substitutes. Insofar as any and all components described herein are understood to be manufacturable. Therefore, they can be manufactured collectively or separately.

[0240] As used herein and in the appended claims, the singular form "a" "an" and "the" are used interchangeably unless the context clearly indicates otherwise. Please note that the term "includes multiple equivalents of" and "includes multiple equivalents of".

[0241] All publications mentioned herein are incorporated by reference in their entirety for all purposes, including, but not limited to, the methods and / or methods that are the subject of these publications. or disclosure and description of the materials, are incorporated by reference. , is provided solely for its disclosure prior to the filing date of the present application. Neither of these disclosures is an admission that the present technology did not antedate such publications by virtue of prior patents. Furthermore, the publication dates stated should not be construed as the actual publication dates. may differ and individual confirmation may be required.

[0242] The words "comprises" and "comprising" mean elements, constituent elements, The elements or steps described should be interpreted in a non-exclusive sense. An element, component, or step may be combined with other elements, components, or steps not specified. Indicates that they can exist, be used, or be combined together.

[0243] Headings used in the detailed description are for the convenience of the reader and are provided to assist in understanding the present disclosure or should not be used to limit what appears in the claims as a whole. These headings should not be used to interpret the scope of a claim or a claim limitation. should not be used in this way.

[0244] The technology herein has been described with reference to particular embodiments, but these embodiments It should be understood that these are merely illustrative of the principles and applications of the present technology. In some cases, terms and symbols may indicate specific details that are not necessary for the practice of the present technology. For example, the terms "first" and "second" (etc.) are used, but unless otherwise specified, these terms are not intended to denote any order. Furthermore, the process steps in the method Although the descriptions or examples of the groups may be presented in a sequential order, such order is not required. Those skilled in the art will recognize that such sequences can be changed and / or the manner in which they are performed simultaneously. It will be appreciated that this may be done synchronously or even more synchronously.

[0245] It is therefore to be understood that numerous modifications may be made in the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present technology. In order to maintain the disclosure matters as originally filed, the contents of claims 1 to 65 as originally filed are added below. (Claim 1) 1. A processor-readable medium having stored thereon processor-executable instructions that, when executed by a processor, cause the processor to detect physiological movement of a user, the processor-executable instructions comprising: an instruction to control generation of an audio signal in the user's vicinity via a speaker connected to the electronic processing device; instructions to control sensing of reflected audio signals from a user via a microphone connected to the electronic processing device; instructions to process the sensed audio signal; and instructions for detecting a respiratory signal from the processed audio signal. (Claim 2) 10. The processor-readable medium of claim 1, wherein the audio signal is in an inaudible range. (Claim 3) 3. The processor-readable medium of claim 1, wherein the audio signal includes a tone pair forming a pulse. (Claim 4) 4. The processor-readable medium of claim 1, wherein the audio signal comprises a sequence of frames, each frame comprising a series of tone pairs, each tone pair being associated with a respective time slot within the frame. (Claim 5) 5. The processor-readable medium of claim 4, wherein a tone pair includes a first frequency and a second frequency, the first frequency and the second frequency being different. (Claim 6) 6. The processor-readable medium of claim 5, wherein the first frequency and the second frequency are orthogonal to each other. (Claim 7) 7. The processor-readable medium of claim 4, wherein the series of timbre pairs within the frame includes a first timbre pair and a second timbre pair, and the frequency of the first timbre pair is different from the frequency of the second timbre pair. (Claim 8) 8. The processor-readable medium of claim 4, wherein a tone pair for a time slot of the frame has zero amplitude at the beginning and end of the time slot and has a ramping amplitude to and from a peak amplitude between the beginning and the end. (Claim 9) 9. The processor-readable medium of claim 4, wherein the time width of the frame varies. (Claim 10) 10. The processor-readable medium of claim 9, wherein the time width is a slot width of the frame. (Claim 11) 10. The processor-readable medium of claim 9, wherein the time span is the width of the frame. (Claim 12) 12. The processor-readable medium of claim 1, wherein a sequence of tone pairs of a frame of slots forms different frequency patterns for different slots of the frame. (Claim 13) 13. The processor-readable medium of claim 12, wherein the pattern of different frequencies is repeated for multiple frames. (Claim 14) 13. The processor-readable medium of claim 12, wherein the pattern of different frequencies is varied for different frames of slots among a plurality of frames of slots. (Claim 15) 15. The processor-readable medium of claim 1, wherein the instructions to control generation of the audio signal include a tonal pair frame modulator. (Claim 16) 16. The processor-readable medium of claim 1, wherein the instructions for controlling the sensing of the audio signal reflected from the user include a frame buffer. (Claim 17) 17. The processor-readable medium of any one of claims 1 to 16, wherein the instructions to process the sensed audio signals reflected from the user include a demodulator to generate one or more baseband movement signals including the respiration signal. (Claim 18) 20. The processor-readable medium of claim 17, wherein the demodulator generates a plurality of baseband motion signals, the plurality of baseband motion signals comprising orthogonal baseband motion signals. (Claim 19) 20. The processor-readable medium of claim 18, further comprising instructions to process the plurality of baseband motion signals, the instructions to process the plurality of baseband motion signals comprising an intermediate frequency processing module and an optimization processing module to generate a combined baseband motion signal from the plurality of baseband motion signals, the combined baseband motion signal comprising the respiration signal. (Claim 20) 20. The processor-readable medium of claim 19, wherein the instructions to detect a respiration signal include determining a respiration rate from the combined baseband motion signal. (Claim 21) 21. A processor-readable medium according to any one of claims 1 to 20 when dependent on claim 4, wherein the duration of each time slot of the frame is equal to the frequency difference between the tone pairs divided by the frequency difference between the tone pairs. (Claim 22) 3. The processor-readable medium of claim 1, wherein the audio signal comprises a repetitive waveform with varying frequency. (Claim 23) 23. The processor-readable medium of claim 22, wherein the repetitive waveform is phase continuous. (Claim 24) 23. The processor-readable medium of claim 22, wherein the variable frequency repetitive waveform comprises one of a sawtooth, a triangular, and a sinusoidal waveform. (Claim 25) 25. The processor-readable medium of any one of claims 22 to 24, further comprising instructions to vary one or more parameters of the shape of the repetitive waveform. (Claim 26) 26. The processor-readable medium of claim 25, wherein the one or more parameters include any one or more of: (a) a peak position of a repetitive portion of the repetitive waveform; (b) a slope of a ramped portion of the repetitive portion of the repetitive waveform; and (c) a frequency range of the repetitive portion of the repetitive waveform. (Claim 27) 24. The processor-readable medium of claim 23, wherein the variable frequency repetitive waveform comprises a symmetric triangular waveform. (Claim 28) 28. The processor-readable medium of any one of claims 22 to 27, wherein the instructions to control generation of the audio signal include audio data indicative of a waveform of looping the repeating waveform. (Claim 29) 29. The processor-readable medium of claim 1, wherein the instructions to control sensing of the sound signal reflected from the user include storing sound data sampled from the microphone. (Claim 30) 30. The processor-readable medium of any one of claims 1 to 29, wherein the instructions to control processing of the sensed audio signal include correlating the generated audio signal with the sensed audio signal to check synchronization. (Claim 31) 31. The processor-readable medium of claim 1, wherein the instructions to process the sensed audio signal include a downconverter to generate data including the respiratory signal. (Claim 32) 32. The processor-readable medium of claim 31, wherein the downconverter mixes a signal indicative of the generated audio signal with the sensed audio signal. (Claim 33) 33. The processor-readable medium of claim 32, wherein the downconverter filters an output of the mixture of the signal indicative of the generated audio signal and the sensed audio signal. (Claim 34) 34. The processor-readable medium of claim 32, wherein the downconverter generates a windowed filtered output of a mixture of the signal indicative of the generated audio signal and the sensed audio signal. (Claim 35) 35. The processor-readable medium of any one of claims 32 to 34, wherein the downconverter generates a frequency domain transform matrix of a windowed filtered output of a mixture of the signal indicative of the generated audio signal and the sensed audio signal. (Claim 36) 36. The processor-readable medium of any one of claims 31 to 35, wherein the instructions to detect the respiratory signal include extracting amplitude and phase information from multiple channels of a data matrix produced by the downconverter. (Claim 37) 37. The processor-readable medium of claim 36, wherein the instructions to detect a respiratory signal further comprise calculating a plurality of features from the data matrix. (Claim 38) 38. The processor-readable medium of claim 37, wherein the plurality of features comprises any one or more of: (a) full-band metrics and in-band squared metrics; (b) in-band metrics; (c) kurtosis metrics; and (d) frequency domain analysis metrics. (Claim 39) 40. The processor-readable medium of claim 38, wherein the instructions to detect a respiration signal generate a respiration rate based on the plurality of features. (Claim 40) The processor-executable instructions include: instructions to calibrate audio-based detection of body movements by evaluating one or more characteristics of the electronic processing device; 40. The processor-readable medium of any one of claims 1 to 39, further comprising instructions for generating the audio signal based on the evaluation. (Claim 41) 41. The processor-readable medium of claim 40, wherein the instructions to compute the audio-based detection determine at least one hardware, environment, or user-specific characteristic. (Claim 42) The processor-executable instructions include: 42. The processor-readable medium of any one of claims 1 to 41, further comprising instructions to activate a pet setup mode, wherein frequencies for generating the audio signals are selected based on user input, and one or more test audio signals are generated. (Claim 43) The processor-executable instructions include: 43. The processor-readable medium of any one of claims 1 to 42, further comprising instructions to stop generating the audio signal based on detection of a user interaction with the electronic processing device, the detected user interaction including any one or more of detecting movement of the electronic processing device via an accelerometer, detecting a button press, detecting a screen touch, and detecting an incoming phone call. (Claim 44) The processor-executable instructions include: 44. The processor-readable medium of any one of claims 1 to 43, further comprising instructions for initiating generation of the audio signal based on detecting an absence of user interaction with the electronic processing device. (Claim 45) The processor-executable instructions include: 45. The processor-readable medium of any one of claims 1 to 44, further comprising instructions for detecting whole body movements based on processing the sensed audio signals reflected from the user. (Claim 46) The processor-executable instructions include: 46. ​​The processor-readable medium of any one of claims 1 to 45, further comprising instructions to detect user movement by processing audio signals sensed via the microphone to evaluate any one or more of environmental audio, conversational audio, and breathing sounds. (Claim 47) The processor-executable instructions include: 47. The processor-readable medium of any one of claims 1 to 46, further comprising instructions to process the respiratory signal to determine any one or more of: (a) a sleep state indicative of sleep; (b) a sleep state indicative of wakefulness; (c) a sleep stage indicative of deep sleep; (d) a sleep stage indicative of light sleep; and (e) a sleep stage indicative of REM sleep. (Claim 48) The processor-executable instructions include: 48. The processor-readable medium of any one of claims 1 to 47, further comprising instructions for operating a setup mode in which detection of audio frequencies in the vicinity of the electronic processing device and selection of a frequency range of the audio signal that is different from the detected audio frequencies occurs. (Claim 49) 49. The processor-readable medium of claim 48, wherein the instructions to activate the setup mode select a frequency range that does not overlap with the detected audio frequency. (Claim 50) 50. A server having access to the processor-readable medium of any one of claims 1 to 49, the server being configured to receive instructions to download the processor-executable instructions of the processor-readable medium over a network to an electronic processing device. (Claim 51) 50. A mobile electronic device comprising: one or more processors; a speaker connected to the one or more processors; a microphone connected to the one or more processors; and a processor-readable medium according to any one of claims 1 to 49. (Claim 52) 50. A method of a server having access to the processor-readable medium of any one of claims 1 to 49, the method comprising: receiving at the server a request to download the processor-executable instructions of the processor-readable medium to an electronic processing device over a network; and transmitting the processor-executable instructions to the electronic processing device in response to the request. (Claim 53) 1. A processor method for detecting body movements using a mobile electronic device, comprising: accessing the processor-readable medium of any one of claims 1 to 49 by a processor; and executing, at the processor, the processor-executable instructions of the processor-readable medium. (Claim 54) 1. A processor method for detecting body movements using a mobile electronic device, comprising: controlling generation of an audio signal in the vicinity of a user through a speaker connected to the mobile electronic device; controlling detection of reflected audio signals from the user via a microphone connected to the mobile electronic device; processing the sensed reflected audio signal; and detecting a respiration signal from the processed reflected audio signal. (Claim 55) 1. A method for detecting movement and respiration using a mobile electronic device, comprising: transmitting an audio signal to a user through a speaker on the mobile electronic device; sensing a reflected audio signal via a microphone on the mobile electronic device, the reflected audio signal being reflected from the user; detecting breathing and movement signals from the reflected audio signal. (Claim 56) 56. The method of claim 55, wherein the audio signal is a non-audible audio signal. (Claim 57) 56. The method of claim 55, wherein the audio signal is modulated prior to transmission using one of an FMCW modulation scheme, an FHRG modulation scheme, an AFHRG modulation scheme, a CW modulation scheme, an UWB modulation scheme, or an ACW modulation scheme. (Claim 58) 58. The method of any one of claims 55 to 57, wherein the audio signal is a modulated low frequency ultrasonic audio signal comprising a plurality of frequency pairs transmitted as frames. (Claim 59) demodulating the reflected audio signal when the reflected audio signal is sensed; filtering the reflected audio signal; 56. The method of claim 55, further comprising: demodulating, comprising: synchronizing the timing of the filtered reflected audio signal and the transmitted audio signal. (Claim 60) generating the audio signal performing a calibration function to evaluate one or more characteristics of the mobile electronic device; and generating said audio signal based on said calibration function. (Claim 61) 61. The method of claim 60, wherein the calibration function is configured to determine at least one hardware, environment, or user specific characteristic. (Claim 62) 60. The method of claim 59, wherein the filtering operation is a high-pass filtering operation. (Claim 63) 1. A method for detecting movement and respiration, comprising: generating an audio signal directed to a user; sensing a reflected audio signal from the user; detecting breathing and movement signals from the sensed reflected audio signal. (Claim 64) 64. The method of claim 63, wherein the generating, transmitting, sensing, and detecting are performed in a bedside device. (Claim 65) 65. The method of claim 64, wherein the bedside device is a CPAP device.

Claims

1. 1. A processor-readable medium having stored thereon processor-executable instructions that, when executed by a processor, cause the processor to detect physiological movement of a user, the processor-executable instructions comprising: an instruction to control generation of an audio signal in the user's vicinity by a speaker connected to the electronic processing device; an instruction to control sensing of a sound signal reflected from a user by a microphone connected to the electronic processing device; instructions to process the sensed audio signal; and instructions to detect a breathing signal from the processed audio signal; The processor-readable medium, wherein the generated audio signal comprises a continuous wave (CW) signal using a single continuous sine wave tone upon the sensing, and the processor-executable instructions further comprise instructions for initiating generation of the audio signal based on detecting an absence of user interaction with the electronic processing device.

2. 10. The processor-readable medium of claim 1, wherein the audio signal generated is in an inaudible range.

3. 3. The processor-readable medium of claim 1, wherein the instructions to control the generating include instructions to modulate the audio signal using a continuous wave (CW) modulation scheme before generating.

4. 4. The processor-readable medium of claim 1, wherein the instructions to control the generating include instructions to modulate the audio signal before generating it using an adaptive continuous wave modulation scheme.

5. The processor-readable medium of any one of claims 1 to 3, wherein the processor-readable medium includes instructions for controlling an adaptive continuous wave modulation scheme.

6. 6. The processor-readable medium of claim 5, wherein the instructions for controlling the adaptive continuous wave modulation scheme include instructions for controlling scanning over inaudible frequencies to repeatedly search for frequencies for respiratory signals.

7. 7. The processor-readable medium of claim 6, wherein the scanning considers frequency content and time domain morphology to search for the best respiratory signal.

8. The processor-readable medium of any one of claims 1 to 7, wherein the processor-executable instructions include signal processing instructions for generating and sensing sound by continuous wave homodyne techniques.

9. 9. The processor-readable medium of claim 1, wherein the processor-executable instructions include instructions for performing high frequency automatic gain control on sensed signal samples to smooth amplitude modulation unrelated to respiratory motion.

10. 10. The processor-readable medium of claim 1, wherein the instructions to process the sensed audio signals reflected from the user include a demodulator to generate one or more baseband movement signals that include the respiration signal.

11. 11. The processor-readable medium of claim 10, wherein the demodulator generates a plurality of baseband motion signals, the plurality of baseband motion signals comprising orthogonal baseband motion signals.

12. 12. The processor-readable medium of claim 11, further comprising instructions to process the plurality of baseband motion signals, the instructions to process the plurality of baseband motion signals comprising an intermediate frequency processing module and an optimization processing module to generate a combined baseband motion signal from the plurality of baseband motion signals, the combined baseband motion signal comprising the respiration signal.

13. 13. The processor-readable medium of claim 12, wherein the instructions to detect a respiration signal include determining a respiration rate from the combined baseband motion signal.

14. 14. The processor-readable medium of claim 1, wherein the instructions to control sensing of the sound signal reflected from the user include storing sound data sampled from the microphone.

15. The processor-executable instructions include: instructions to calibrate audio-based detection of body movements by evaluating one or more characteristics of the electronic processing device; and generating the generated audio signal based on the evaluation.

16. 16. The processor-readable medium of claim 15, wherein the instructions to compute the audio-based detection determine at least one hardware, environment, or user-specific characteristic.

17. The processor-executable instructions include:

17. The processor-readable medium of any one of claims 1 to 16, further comprising instructions to activate a pet setup mode, wherein frequencies for generating the audio signals are selected based on user input, and one or more test audio signals are generated.

18. The processor-executable instructions include:

18. The processor-readable medium of claim 1, further comprising instructions to stop generating the audio signal based on detection of a user interaction with the electronic processing device, the detected user interaction comprising any one or more of detecting movement of the electronic processing device via an accelerometer, detecting a button press, detecting a screen touch, and detecting an incoming phone call.

19. The processor-executable instructions include:

19. The processor-readable medium of any one of claims 1 to 18, further comprising instructions for detecting whole body movements based on processing the sensed audio signals reflected from the user.

20. The processor-executable instructions include:

20. The processor-readable medium of claim 1, further comprising instructions to detect user movement by processing audio signals sensed via the microphone to evaluate any one or more of ambient audio, speech audio, and breathing sounds.

21. The processor-executable instructions include:

21. The processor-readable medium of any one of claims 1-20, further comprising instructions to process the respiratory signal to determine any one or more of: (a) a sleep state indicative of sleep; (b) a sleep state indicative of wakefulness; (c) a sleep stage indicative of deep sleep; (d) a sleep stage indicative of light sleep; and (e) a sleep stage indicative of REM sleep.

22. The processor-executable instructions include:

22. The processor-readable medium of claim 1, further comprising instructions for operating a setup mode in which detection of audio frequencies in the vicinity of the electronic processing device and selection of a frequency range of the audio signal to be generated that differs from the detected audio frequencies.

23. 23. The processor-readable medium of claim 22, wherein the instructions to activate the setup mode select a frequency range that does not overlap with the detected audio frequency.

24. 24. A server having access to the processor-readable medium of any one of claims 1 to 23, the server being configured to receive instructions to download the processor-executable instructions of the processor-readable medium over a network to an electronic processing device.

25. 24. A mobile electronic device comprising: one or more processors; a speaker connected to the one or more processors; a microphone connected to the one or more processors; and a processor-readable medium according to any one of claims 1 to 23.

26. 24. A method of a server having access to the processor-readable medium of any one of claims 1 to 23, the method comprising receiving at the server a request to download the processor-executable instructions of the processor-readable medium to an electronic processing device over a network, and transmitting the processor-executable instructions to the electronic processing device in response to the request.

27. 1. A processor method for detecting body movements using a mobile electronic device, comprising: Accessing the processor-readable medium of any one of claims 1 to 23 from a processor; and executing the processor-executable instructions of the processor-readable medium on the processor.

28. 1. A processor method for detecting body movements using a mobile electronic device, comprising: controlling generation of an audio signal in the vicinity of a user by a speaker connected to the mobile electronic device; controlling detection of sound signals reflected from the user by a microphone connected to the mobile electronic device; processing the sensed reflected audio signal; and detecting a movement signal from the processed reflected audio signal, wherein the generated audio signal comprises a continuous wave (CW) signal using a single continuous sine wave sound upon said sensing, and wherein the controlled generation of the audio signal is initiated based on the detection of an absence of user interaction with the electronic processing device.

29. 1. A method for detecting movement and respiration using a mobile electronic device, comprising: transmitting an audio signal to a user through a speaker on the mobile electronic device; sensing a reflected audio signal with a microphone on the mobile electronic device, the reflected audio signal being reflected from the user; detecting breathing and movement signals from the reflected audio signal, wherein the transmitted audio signal comprises a continuous wave (CW) signal using a single continuous sine wave sound upon said sensing, and transmission of the audio signal is initiated based on detection of an absence of user interaction with the electronic processing device.

30. 30. The method of claim 29, wherein the transmitted audio signal is a non-audible audio signal.

31. 30. The method of claim 29, wherein the audio signal is modulated before being transmitted using one of a continuous wave (CW) modulation scheme and an adaptive continuous wave (ACW) modulation scheme.

32. demodulating the reflected audio signal when the reflected audio signal is sensed; filtering the reflected audio signal to generate a filtered reflected audio signal; 30. The method of claim 29, further comprising demodulating, including synchronizing the timing of the filtered reflected audio signal and the transmitted audio signal.

33. The generation of the audio signal to be sent includes: performing a calibration function to evaluate one or more characteristics of the mobile electronic device; and generating the transmitted audio signal based on the calibration function.

34. 34. The method of claim 33, wherein the calibration function is configured to determine at least one hardware, environment, or user specific characteristic.

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