Patent Publication Number: US-11660046-B2

Title: Systems and methods of identifying motion of a subject

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims the benefit of pending U.S. Provisional Application No. 62/089,130, filed Dec. 8, 2014, and U.S. Provisional Application No. 62/152,519, filed Apr. 24, 2015. The foregoing applications are incorporated herein by reference in their entireties. 
    
    
     TECHNICAL FIELD 
     The present technology relates generally to identifying motion of a portion of a subject&#39;s body and associated methods and systems. In particular, several embodiments are directed to methods of tracking motion of a subject&#39;s body for use in identifying sleep apnea, although these or similar embodiments may be used in identifying chronic obstructive pulmonary disease (COPD), monitoring infant respiration and/or detecting other movements of the subject. 
     BACKGROUND 
     Sleep apnea is a common medical disorder that occurs when breathing is disrupted during sleep. Sleep apnea is estimated to affect nearly 1 in 20 American adults and is linked to attention deficit/hyperactivity disorder, high blood pressure, diabetes, heart attack, stroke and increased motor vehicle accidents. Sleep apnea is commonly diagnosed in a dedicated sleep clinic that administers polysomnography tests. In a polysomnography test, a trained technician attaches and monitors sensors on the subject for the duration of the subject&#39;s sleep over a single night. Polysomnography tests, however, can be expensive, time-consuming and labor-intensive, and subjects may have to wait several weeks to receive a polysomnography test due to long wait lists. Alternatively, a home sleep apnea test (HSAT) may be performed using a portable recording system in a subject&#39;s home, typically during a single night&#39;s sleep. During an HSAT, the subject still typically wears several measurement instruments connected to the portable recording system. Such home tests can also be problematic. For example, improper attachment of one or more of the measurement instruments may affect the accuracy of a home sleep test. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic diagram of a device shown adjacent a human subject and configured in accordance with embodiments of the present technology. 
         FIG.  2    is a block diagram of a system configured in accordance with embodiments of the present technology. 
         FIG.  3    is a flow diagram of a process configured in accordance with an embodiment of the present technology. 
         FIG.  4 A  is a graph depicting a motion waveform acquired in accordance with an embodiment of the present technology. 
         FIG.  4 B  is a graph depicting peaks detected in a motion waveform in accordance with an embodiment of the present technology. 
         FIG.  5 A  is a graph depicting a prior art method of acquiring data. 
         FIG.  5 B  is a graph depicting a method of acquiring data in accordance with an embodiment of the present technology. 
         FIG.  6    is a flow diagram of a process configured in accordance with an embodiment of the present technology. 
         FIG.  7    is a flow diagram of a process configured in accordance with an embodiment of the present technology. 
         FIGS.  8 A- 8 C  are graphs showing examples of apnea and hypopnea events in accordance with an embodiment of the present technology. 
     
    
    
     DETAILED DESCRIPTION 
     The present technology relates generally to identifying motion of a portion of a subject&#39;s body and associated methods and systems. In one embodiment of the present technology, for example, a method of identifying sleep apnea events in a subject includes transmitting sound energy toward the subject using a first transducer (e.g., a loudspeaker) and receiving echoes from the subject corresponding to the transmitted sound energy using a second transducer (e.g., a microphone). Electrical signals corresponding to the echoes are used to generate a waveform and a plurality of peaks can be detected in the waveform. Individual peaks in the waveform can have corresponding amplitudes indicative of a breathing motion of the subject. An indication of a sleep apnea event can be output for each occurrence of a period of time between successive individual peaks in the waveform exceeding a predetermined threshold time. In some aspects, transmitting the sound energy comprises emitting a plurality of audio chirps from the first transducer that linearly sweep from a first frequency (e.g., about 18 kHz) to a second, higher frequency (e.g., about 20 kHz or higher) over a predetermined time duration (e.g., between about 5 ms and about 15 ms, about 10.75 ms). 
     In another embodiment of the present technology, a method of operating an electronic device to monitor movements of a subject proximate the electronic device includes emitting a plurality of audio sweep signals toward the subject from a loudspeaker operatively coupled to the electronic device. The individual audio sweep signals linearly sweep from a first frequency less than 20 kHz (e.g., about 18 kHz) to a second, higher frequency (e.g., about 20 kHz or higher) over a predetermined time duration (e.g., between about 5 ms and about 15 ms, about 10.75 ms). The method further includes acquiring audio data at a microphone operatively coupled to the electronic device. The audio data can include echo signals corresponding to individual audio sweep signals backscattered by the subject toward the microphone. The acquired audio data is processed to generate a motion waveform. One or more peaks detected in the motion waveform are indicative of movements of the subject. The method also includes outputting an indication of movement of the subject (e.g., motion of the subject&#39;s chest or abdomen) based one or more of the detected peaks. In some aspects, for example, at least a portion of the plurality of the audio sweep signals comprise frequency-modulated continuous-wave sound signals. In some aspects, the method also includes calculating a plurality of frequency domain representations of the echo signals that are calculated over a time period lasting a predetermined multiple (e.g., 10) of the predetermined time duration (e.g., 10.75 ms) of the individual audio sweep signals. In some aspects, the method can include determining a frequency shift in the individual frequency domain representations relative to the first frequency. 
     In yet another embodiment of the present technology, a computer program product comprising computer usable program code executable to perform operations for outputting an indication of a sleep apnea event in a subject. The operations include transmitting a plurality of chirp signals to a first transducer (e.g., a loudspeaker) operatively coupled to a mobile device. The individual chirp signals linearly sweep from a first frequency less than 20 kHz (e.g., 10 kHz, 16 kHz, 18 kHz) to a second, higher frequency (e.g., 19 kHz, 20 kHz, 22 kHz, 30 kHz) over a predetermined time duration (e.g., 5 ms, 10 ms, 20 ms, 30 ms). The operations further include acquiring echo data from a second transducer (e.g., a microphone) operatively coupled to the mobile device. The echo data includes data corresponding to individual chirp signals reflected by the subject toward the second transducer. The operations also include demodulating the acquired echo data to obtain a motion signal indicative of respiratory motion of the subject, and detecting one or more amplitude peaks in the motion signal. The operations further comprise outputting an indication of a sleep apnea event if a period of time between successive individual amplitude peaks in the motion signal exceeds a predetermined threshold time. In some aspects, the operations can further include repeating the transmitting and acquiring for a predetermined number of transmit/acquisition cycles. In some aspects, the demodulating the acquired echo data can include performing a single Fourier transform over the predetermined number of transmit/acquisition cycles. 
     These and other aspects of the present disclosure are described in greater detail below. Certain details are set forth in the following description and in  FIGS.  1 - 8 C  to provide a thorough understanding of various embodiments of the disclosure. Other details describing well-known systems and methods often associated with motion tracking and/or identification have not been set forth in the following disclosure to avoid unnecessarily obscuring the description of the various embodiments. 
     In the Figures, identical reference numbers identify identical, or at least generally similar, elements. To facilitate the discussion of any particular element, the most significant digit or digits of any reference number refers to the Figure in which that element is first introduced. For example, element  110  is first introduced and discussed with reference to  FIG.  1   . Many of the details, dimensions, angles and other features shown in the Figures are merely illustrative of particular embodiments of the disclosed technology. Accordingly, other embodiments can have other details, dimensions, angles and features without departing from the spirit or scope of the disclosure. In addition, those of ordinary skill in the art will appreciate that further embodiments of the invention can be practiced without several of the details described below. 
     Devices and Methods for Detecting Motion of a Subject 
       FIG.  1    is a schematic diagram of a device  110  configured in accordance with embodiments of the present technology. The device  110  is positioned near a human subject  101  lying on a bed  104  such that the subject&#39;s abdomen  102  and chest  103  are approximately a distance D (e.g., 1 meter) from the device  110 . A first transducer  115  (e.g., a loudspeaker) is configured to emit acoustic energy (e.g., sounds between about 20 Hz and 20 kHz or higher), including sound  105 . A second transducer  116  (e.g., a microphone) is configured to receive acoustic energy including reflected sound  106  received from the subject&#39;s body  102 . A communication link  113  (e.g., an antenna) communicatively couples the device  110  to a communication network (e.g., the Internet, a cellular telecommunications network, a WiFi network). A user interface  118  is configured to receive input from the subject  101  and/or another user, and is further configured to provide visual output to the subject  101  and/or another user. In the illustrated embodiment of  FIG.  1   , the user interface  118  comprises a touchscreen display. In some embodiments, the user interface  118  may include, for example, one or more keypads, touchpads, touchscreens, trackballs, mice and/or additional user interface devices or systems (e.g., a voice input/output system). Moreover, in some embodiments, one or more additional speakers  125  and one or more additional microphones  126  may optionally be positioned near the bed  104  separate from the device  110 , and communicatively coupled to the device  110  via the communication link  113  and/or another communication link. In some other embodiments, the device  110  may include one or more additional speakers and/or microphones (not shown). 
     In the illustrated embodiment of  FIG.  1   , the device  110  is a depicted as a mobile phone (e.g., a smartphone). In other embodiments, however, the device  110  may comprise any suitable electronic device such as, for example, a tablet, a personal display assistant, a laptop computer, a desktop computer, a set top box and/or another electronic device configured to transmit and receive sound. In certain embodiments, the device  110  may comprise a component of one or more systems and/or devices (e.g., a baby monitor, a security system, an automobile entertainment system, a stereo system, a home intercom system, a clock radio). Moreover, in the illustrated embodiment of  FIG.  1   , the subject  101  (e.g., a human adult, a human child, an animal) is shown lying asleep on the bed  104  (e.g., a bed in the subject&#39;s bedroom, a bed in a medical facility, a bed in a sleep laboratory). In other embodiments, however, the subject  101  may be awake and/or upright. In some embodiments, the device  110  may be configured to emit the sound  105  toward and receive the reflected sound  106  from one or more additional subjects (not shown). 
     In operation, the device  110  generates audio signals—including, for example, frequency modulated continuous wave (FMCW) audio signals—that sweep from a first frequency (e.g., about 18 kHz) to a second frequency (e.g., about 20 kHz). The first transducer  115  transmits the generated audio signals as the sound  105  toward the subject  101 . A portion of the sound  105  is reflected and/or backscattered by the subject&#39;s chest  103  and/or abdomen  102  toward the second transducer  116  as the reflected sound  106 . The second transducer  116  receives the reflected sound  106  and converts it into one or more reflected audio signals. As discussed in further detail below in reference to  FIGS.  3 - 5  and  6 B , the device  110  can be configured to detect peaks in the reflected audio signals that correspond to movements of the subject&#39;s chest  103  and/or abdomen  102 . And as discussed in further detail below in reference to  FIGS.  3  and  7 - 8 C , the device  110  can be further configured to identify and/or disambiguate one or more apnea events (e.g., a central apnea event, an obstructive apnea event, a hypopnea event) in the subject based on the detected peaks. In some embodiments, the device  110  is also configured to identify movements of the subject&#39;s chest  103  and/or abdomen  102  that correspond to movements associated with chronic obstructive pulmonary disease (COPD) or infant respiration. 
     As those of ordinary skill in the art will appreciate, conventional approaches to the identification of sleep disorders and/or other medical disorders can include overnight stays at a medical facility using dedicated (and often expensive) medical equipment. One conventional approach is a clinical polysomnography (PSG) test, which is traditionally used to diagnose sleep apnea and other sleep disorders. A PSG is typically conducted overnight in a sleep laboratory where a trained technician monitors a subject&#39;s sleeping patterns. The technician attaches a number of sensors to the subject including, for example, a chest and abdomen belt to measure breathing movements, a nasal pressure transducer and thermistor, a snore microphone, a pulse oximeter to measure oxygen saturation, a movement sensor on each leg to detect movements, a sensor to determine muscular tone of the chin, sensors to monitor eye movements and/or EEG sensors to measure brain activity. The sensors are all connected using wires and the technician monitors the live data stream from the sensors throughout the sleep duration. 
     One metric used for sleep apnea identification is the Apnea-Hypopnea Index (AHI), which represents a rate at which apnea and hypopnea events occur during a sleep period. Physicians can classify the sleep apnea level using AHI values. For example, AHI values ranging from 0 to 5 are typically classified as no-apnea; AHI values between 5 and 15 are typically classified as mild-apnea; AHI values between 15 and 30 are typically classified as moderate-apnea and AHIs of 30 or higher are typically classified as severe apnea. The apnea-hypopnea index can computed as follows: 
     
       
         
           
             
               
                 
                   AHI 
                   = 
                   
                     
                       
                         # 
                         ⁢ 
                             
                         central 
                         ⁢ 
                             
                         apnea 
                       
                       + 
                       
                         # 
                         ⁢ 
                             
                         hypopnea 
                       
                       + 
                       
                         # 
                         ⁢ 
                             
                         obstructive 
                         ⁢ 
                             
                         apnea 
                       
                     
                     
                       total 
                       ⁢ 
                           
                       sleep 
                       ⁢ 
                         
                       time 
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     In equation 1 above, central apnea, hypopnea, and obstructive apnea correspond to the parameters that are tracked during a typical PSG study. To compute these parameters, the sensor data collected during the sleep period (typically 6-8 hours) is split into 30-second intervals called epochs. The scoring process of analyzing these epochs may involve two steps. A first step is staging, which identifies whether the subject is awake or asleep in each epoch and if asleep, what sleep stage is present. This is achieved by examining the brain activity obtained from the EEG sensors and the chin tone and eye movement sensor information. At the end of this step, each epoch can be marked as being in either a wake or sleep stage. A second step involves identifying the number of central apnea, hypopnea, and obstructive apnea events, using American Academy of Sleep Medicine (AASM) guidelines. For example, a central apnea event can occur when the subject holds her breath for a non-negligible duration. A hypopnea event can occur, for example, when the subject&#39;s chest motion drops by more than 30% with an accompanying 4% oxygen desaturation. A hypopnea may also be determined by presence of a 3% desaturation or an “arousal” (abrupt frequency change) on the EEG. An obstructive apnea event can occur, for example, when the subject makes an increased effort to pull air into the lungs but only a minimal amount of air reaches the lungs due to blockage. 
     As those of ordinary skill in the art will appreciate, polysomnography procedures for sensor data collection and processing can be both labor and time intensive. For example, it may take about an hour for the technician to fit each subject with sensors typically employed in a PSG measurement. Further, throughout a sleep duration (e.g., an eight-hour sleep duration), the technician may continue to monitor the sensors and confirm the sensors remain properly attached to the subject&#39;s body. Sensor data is typically processed manually to tag every epoch with the sleep apnea events. Moreover, while an HSAT may be performed in a subject&#39;s home, the test still requires attaching sensors to the subject that include, for example, chest and abdomen belts, nasal pressure sensors, transducer and thermistors, EKG sensors, pulse oximetry sensors, and/or pulse arterial tonometry sensors. Home testing can have a high failure rate (e.g., 33%) due to signal loss resulting from detachment of wires and cables 
     In contrast to these conventional approaches outlined above, the disclosed technology is expected to be considerably less labor intensive and time consuming. For example, the disclosed techniques for detecting movement of at least a portion of the subject&#39;s body (e.g., a chest, an abdomen) use sound waves without sensors in contact with the subject. The disclosed technology accordingly eliminates the use of wires or cables that may cause test failure due to improper attachment and/or signal loss. The disclosed technology is also expected provide a benefit of identifying one or more medical conditions (e.g., sleep apnea. COPD) while the subject sleeps or rests in his or her own bed and uses a relatively inexpensive device (e.g., the subject&#39;s own smartphone or another personal electronic device, a computer, an off-the-shelf mobile device, etc.). As a result, the disclosed technology can reduce or eliminate the time and/or expenses associated with a technician monitoring the subject during an entire sleep duration. The disclosed technology is further expected to allow concurrent monitoring and movement detection of multiple subjects via a single device. 
     In some embodiments, the disclosed technology can also be utilized in the identification of a potential presence of COPD in a subject. As those of ordinary skill in the art will appreciate, COPD is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. Symptoms of COPD can include breathing difficulty, coughing, sputum production and wheezing. COPD exacerbations can involve an acute worsening of the patient&#39;s condition and can be a major cause of morbidity and mortality associated with this disease. Increased respiratory frequency and reduced tidal volume are common physiological characteristics of COPD exacerbations. The disclosed technology can assess the frequency and depth of breathing in real time to identify COPD exacerbations in the early stages. Such early detections and corresponding treatment are expected to help prevent worsening of this condition. 
     Suitable Systems 
     The following discussion provides a brief, general description of a suitable environment in which the technology may be implemented. Although not required, aspects of the technology are described in the general context of computer-executable instructions, such as routines executed by a general-purpose computer. Aspects of the technology can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions explained in detail herein. Aspects of the technology can also be practiced in distributed computing environments where tasks or modules are performed by remote processing devices, which are linked through a communication network (e.g., a wireless communication network, a wired communication network, a cellular communication network, the Internet, a short-range radio network (e.g., via Bluetooth)). In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     Computer-implemented instructions, data structures, screen displays, and other data under aspects of the technology may be stored or distributed on computer-readable storage media, including magnetically or optically readable computer disks, as microcode on semiconductor memory, nanotechnology memory, organic or optical memory, or other portable and/or non-transitory data storage media. In some embodiments, aspects of the technology may be distributed over the Internet or over other networks (e.g. a Bluetooth network) on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave) over a period of time, or may be provided on any analog or digital network (packet switched, circuit switched, or other scheme). 
       FIG.  2    is a block diagram of a system  210  configured in accordance with embodiments of the present technology. The system  210  includes several components including memory  211  (e.g., one or more computer readable storage modules, components, devices). In some embodiments, the memory  211  comprises one or more applications installed and/or operating on a computer and/or a mobile device (e.g., the device  110  of  FIG.  1   , a tablet, a smartphone, a PDA, a portable media player, or other “off-the-shelf” mobile device). The memory  211  can also be configured to store information (e.g., audio data, subject information or profiles, environmental data, data collected from one or more sensors, media files). A processor  212  (e.g., one or more processors or distributed processing elements) is coupled to the memory  211  and configured to execute operations and/or instructions stored thereon. 
     A speaker  215  (e.g., the first transducer  115  and/or the speaker  125  of  FIG.  1   ) operatively coupled to the processor is configured to receive audio signals from the processor  212  and/or one or more other components of the system  210  and output the audio signals as sound (e.g., the sound  105  of  FIG.  1   ). In some embodiments, the speaker  215  includes a conventional dynamic loudspeaker disposed in a mobile device (e.g., a smartphone or tablet). In some embodiments, the speaker  215  includes an earphone transducer and/or a standalone loudspeaker. In other embodiments, the speaker  215  includes a suitable transducer configured to output acoustic energy in at least a portion of the human audible frequency spectrum (e.g., between about 20 Hz and 20 kHz). 
     A microphone  216  (e.g., the second transducer  116  and/or the microphone  126  of  FIG.  1   ) operatively coupled to the processor is configured to receive sound, convert the sound into one or more electrical audio signals and transmit the electrical audio signals to the memory  211  and/or the processor  212 . In some embodiments, the microphone  216  includes a microphone disposed in a mobile device (e.g., a smartphone or tablet). In some embodiments, the microphone  216  is located on an earphone and/or along a cable connected to one or more earphones. In other embodiments, the microphone  216  includes another suitable transducer configured to receive acoustic energy in at least a portion of the human audible spectrum. Moreover, in some embodiments, the speaker  215  and the microphone  216  are spaced apart by a distance (e.g., 2 cm or greater, between about 2 cm and 10 cm, between 4 cm and 8 cm, or at least about 6 cm). In other embodiments, however, the speaker  215  is immediately adjacent the microphone  216 . In certain embodiments, a single transducer can transmit sound energy and receive sound energy. In further embodiments, the speaker  215  and/or the microphone  216  comprise one or more additional transducers to form one or more transducer array(s). The transducer array(s) can be configured to transmit and/or receive beamformed audio signals. 
     Communication components  213  (e.g., a wired communication link and/or a wireless communication link (e.g., Bluetooth. Wi-Fi, infrared and/or another wireless radio transmission network)) communicatively couple the system  210  to one or more communications networks (e.g., a telecommunications network, the Internet, a WiFi network, a local area network, a wide area network, a Bluetooth network). A database  214  is configured to store data (e.g., audio signals and data acquired from a subject, equations, filters) used in the identification of movements of a subject. One or more sensors  217  are configured to provide additional data for use in motion detection and/or identification. The one or more sensors  217  may include, for example, one or more ECG sensors, blood pressure monitors, galvanometers, accelerometers, thermometers, hygrometers, blood pressure sensors, altimeters, gyroscopes, magnetometers, proximity sensors, barometers and/or hall effect sensors. 
     One or more displays  218  (e.g., the user interface  118  of  FIG.  1   ) provide video output and/or graphical representations of data acquired and processed by the system  210 . A power supply  219   a  (e.g., a power cable connected to a building power system, one or more batteries and/or capacitors) provides electrical power to components of the system  210 . In embodiments that include one or more batteries, the power supply  219   a  can be configured to recharge, for example, via a power cable, inductive charging, and/or another suitable recharging method. Furthermore, in some embodiments, the system  210  optionally includes one or more other components  219   b  (e.g., one or more microphones, cameras, Global Positioning System (GPS) sensors, Near Field Communication (NFC) sensors). 
     As explained in further detail below in reference to  FIGS.  3 - 8 C , the system  210  is configured to transmit sound toward a subject and receive sound reflected by the subject. The transmitted and received sound can be used by the system  210  to detect movement of the subject and identify one or more medical conditions (e.g., sleep apnea, COPD) in the subject. In some embodiments, for example, the memory  211  includes instructions for generating audio signals (e.g., FMCW audio signals that sweep from about 18 kHz to about 20 kHz or higher) and providing the generated audio signals to the speaker  215 . The speaker  215  transmits the audio signals as sound (e.g., acoustic energy comprising one or more waveforms) and directs at least a portion of the transmitted sound toward a subject (e.g., the subject  101  of  FIG.  1   ) proximate the speaker  215 . A portion of the sound is reflected or backscattered toward the microphone  216 , which converts the sound into electrical audio signals. The memory  211  can further include instructions for processing the electrical audio signals to detect motion of the subject (e.g., movement of the subject&#39;s chest and/or abdomen), to disambiguate between periodic motion (e.g., respiratory motion) and non-periodic motion, and to identify one or more medical conditions (e.g., an apnea event, COPD) in the subject based on the detected motion of the subject. In some embodiments, an indication of the identified medical condition can be output to the display  218  and/or can be transmitted via the communication component  213  to a medical professional (e.g., a nurse, a doctor). In certain embodiments, the system  210  can be configured to determine baseline breathing information (e.g., breathing frequency) about a subject and store the baseline breathing information. The baseline breathing information can be compared to subsequent breathing measurements to identify a respiratory disorder. 
     Suitable Methods 
       FIG.  3    is a flow diagram of a process  300  configured to detect an apnea event in accordance with an embodiment of the present technology.  FIG.  4 A  is a graph  401  depicting an example of a motion waveform acquired by the process  300  in accordance with an embodiment of the present technology.  FIG.  4 B  is a graph  402  depicting peaks detected in the motion waveform of  FIG.  4 A  in accordance with an embodiment of the present technology. 
     Referring first to  FIG.  3   , the process  300  can comprise a set of instructions stored on memory (e.g., the memory  211  of  FIG.  2   ) and executed by one or more processors (e.g., the processor  212  of  FIG.  2   ). In some embodiments, the process  300  comprises one or more smartphone applications stored on a device (e.g., the device  110  of  FIG.  1   ). The process  300  begins at block  305  after the device and/or transducers are positioned proximate a subject (e.g., 1 m away from the subject, between about 0.5 m and 10 m from the subject, between about 1 m and 5 m from the subject) and/or the subject&#39;s bed (e.g., the bed  104  of the subject  101  of  FIG.  1   ). At block  305 , the process  300  monitors the subject to determine whether the subject is asleep. In some embodiments, for example, the process  300  may monitor movements of the subject to detect random, non-periodic motions that the process  300  determines are not associated with breathing motion of the subject. For example, if the process  300  detects a predetermined number of occurrences (e.g., two, three, four or higher) of non-periodic motion within a predetermined time period, (e.g., 5 minutes, 10 minutes, 20 minutes), the process  300  may determine that the subject is awake for the duration of the predetermined time period. Conversely, if the process  300  does not detect the predetermined number of occurrences of non-periodic motion within the predetermined time period, the process  300  may determine that the subject is asleep during the entire predetermined time period. Collectively, a sum of a plurality of predetermined time periods that do not include the predetermined number of occurrences of detected non-periodic motion may form the basis of an overall measurement of sleep time during a session or test. Such an overall measurement of sleep time may be used, for example, in the denominator of equation 1 discussed above. In some embodiments, the process  300  is configured to wait a predetermined amount of time (e.g., one hour, two hours, four hours) before proceeding to the next step. 
     In some embodiments, the process  300  can detect an orientation of the device and, based on this detection, prompt a user to take corrective action. For example, the process  300  may provide more accurate detection if a predetermined side of a measurement device (e.g., a front facing portion of the device  110  shown in  FIG.  1   ) is oriented at a predetermined orientation relative to the subject. In some embodiments, for example, it may be preferable to have a side of the measurement device on which the speaker is located or most closely positioned to be oriented toward the subject. In embodiments in which the speaker and a microphone are not on the same side of the measurement device, however, it may be desirable to acquire audio from the subject if a side of the measurement device on which a microphone is positioned is facing upright and/or substantially oriented toward the subject. 
     The process  300  can be configured to determine an orientation of the measurement device using, for example, one or more sensing mechanisms (e.g., one or more gyroscopes, accelerometers, compass sensors). In some embodiments, for example, the one or more sensing mechanisms include one or more of the sensors  217  discussed above with reference to  FIG.  2   . In some embodiments, the process  300  can generate one or more audible and/or visible indications instructing the subject and/or another user to take a corrective action based on the determined orientation. The corrective actions may include, for example, moving and/or orienting the measurement device toward the location of the subject. In some embodiments, the process  300  may not proceed until one or more corrective actions are detected. Alternatively, the one or more audible and/or visible indications may persist while other blocks are executed in process  300 . In some embodiments, the process  300  can be configured to adjust detection thresholds based on a detected orientation. 
     At block  310 , the process  300  generates one or more audio signals. In some embodiments, the audio signals include FMCW signals having a sawtooth waveform that includes a plurality of sweep audio signals or “chirps” that linearly sweep from a first frequency to a second, higher frequency. In some embodiments, the chirps sweep from a first audible frequency (e.g., about 18 kHz) to a second audible frequency (e.g., 20 kHz or higher). As those of ordinary skill in the art will appreciate, the frequency spectrum of a typical human ear ranges from 20 Hz to about 20 kHz, and many transducers are configured for playback over this spectrum. As humans age, however, the sensitivity of the ears to higher frequencies typically diminishes such that sounds having frequencies greater than about 18 kHz are effectively inaudible for a typical adult human. Accordingly, selecting the first and second audible frequencies to have a frequency equal to or greater than about 18 kHz allows for transmission of sound over a conventional loudspeaker configured for playback over the human audible frequency range while not disturbing most adults as they sleep. In other embodiments, the chirps sweep from a first audible frequency (e.g., 18 kHz) to a second inaudible frequency (e.g., a frequency greater than about 20 kHz and less than about 48 kHz, a frequency between about 22 kHz and about 44 kHz). In further embodiments, the chirps sweep between two frequencies outside the human audible range (e.g., greater than about 20 kHz and less than about 48 kHz). Moreover, in some embodiments, the process  300  generates audio signals comprising FMCW signals having a sine waveform, a triangle waveform and/or a square waveform. In other embodiments, the process  300  generates audio signals comprising pulse-modulated waveforms. In some embodiments, the process  300  generates audio signals using another suitable modulation method. 
     At block  320 , the process  300  provides the generated audio signals to a transducer (e.g., the first transducer  115  of  FIG.  1    and/or the speaker  215  of  FIG.  2   ) configured to convert the audio signals to acoustic energy (e.g., the sound  105  of  FIG.  1   ) and further configured to direct at least a portion of the acoustic energy toward the subject. At block  330 , the process  300  acquires echo data from a microphone (e.g., the second transducer  116  of  FIG.  1    and/or the microphone  216  of  FIG.  2   ) or another transducer. The acquired echo data includes data corresponding to a portion of the sound transmitted toward the subject and reflected or backscattered toward the microphone and converted by the microphone to electrical signals. 
     Referring now to  FIGS.  3 ,  4 A and  4 B  together, at block  340  the process  300  constructs a motion waveform using the echo data. The process  300  analyzes the generated audio signals and the received echo data and detects frequency shifts therebetween that are indicative of movement of a portion of the subject&#39;s body (e.g., the subject&#39;s chest and/or abdomen). As explained in further detail below in reference to  FIGS.  5 A,  5 B and  6   , the frequency shifts can be used to generate the motion waveform as a function of time. One example of a motion waveform constructed by the process  300  at block  340  is shown in the graph  401  of  FIG.  4 A . The graph  401  includes a motion waveform  440  having a plurality of peaks  444  and a plurality of valleys or nulls  446 . 
     At block  350 , the process  300  detects one or more of the peaks  444  in the waveform  440  of  FIG.  4 A . The graph  402  of  FIG.  4 B  shows one example of the peaks  444  detected by the process  300 . Additional aspects of construction of a motion waveform and detection of the peaks in the waveform are described below in reference to  FIG.  6   . 
     At block  360 , the process  300  analyzes the peaks (e.g., the peaks  444  of  FIGS.  4 A and  4 B ) detected in the motion waveform (e.g., the waveform  440  of  FIG.  4 A ) to identify one or more sleep apnea events. For example, if an amplitude of a particular peak in the waveform is less than or equal to a predetermined threshold amplitude, the process  300  may determine that the particular peak corresponds to a hypopnea event in the subject. If, for example, successive peaks in the waveform are separated by a predetermined time (e.g., 10 seconds or greater), the process  300  may determine that the peak separation corresponds to a central apnea event. Further, if the process  300  detects a spike or a predetermined increase (e.g., 50%) in an amplitude between successive peaks, the process  300  may determine that the peak increase corresponds to an obstructive apnea event. 
     In some embodiments, the process  300  may compare a frequency of the detected peaks to a predetermined breathing frequency (e.g., a prior measurement of the patient&#39;s breathing frequency). The process  300  may further determine a possible presence of a COPD exacerbation in the subject if the frequency of the detected peaks is greater than equal to a predetermined percentage (e.g., between about 105% and about 125%, or about 115%) of the predetermined breathing frequency. In some embodiments, the predetermined breathing frequency generally corresponds to a measured breathing frequency determined in a first portion or duration of a test, such as a predetermined period of time during a sleep measurement (e.g., an initial 30 minutes of the sleep measurement). The process  300  can use the measured breathing frequency as the subject&#39;s baseline breathing frequency. In other embodiments, however, the process  300  may use other predetermined percentages (e.g., about 130% or higher) and/or other predetermined periods of time (e.g., between about 15 minutes and about 30 minutes, between about 30 minutes and about 60 minutes, between about 60 minutes and about 120 minutes). 
     At block  370 , the process  300  outputs an indication of one or more of the apnea events. In some embodiments, for example, the process  300  may store one or more indications of apnea events in a memory or database (e.g., the memory  211  and/or the database  214  of  FIG.  2   ). In some embodiments, the process  300  may output an indication of one or more apnea events to a display (e.g., the user interface  118  of  FIG.  1    and/or the display  218  of  FIG.  2   ). 
       FIG.  5 A  is a graph  501  depicting a conventional data acquisition approach in accordance with the prior art.  FIG.  5 B  is a graph  502  depicting a method of acquiring data in accordance with an embodiment of the present technology. Referring first to  FIG.  5 A , the graph  501  includes a plurality of transmit signals  548  and a plurality of corresponding received signals  549 . A fast fourier transform (FFT) is computed for each transmit/receive cycle. 
     Referring next to  FIG.  5 B , the graph  502  includes a plurality of transmitted signals  550  (identified individually as a first transmitted signal  550   a , a second transmitted signal  550   b , and an nth transmitted signal  550   n ) and a plurality of corresponding reflected signals  552  (identified individually as a first reflected signal  552   a , a second reflected signal  552   b , and an nth reflected signal  552   n ). The plurality of transmitted signals  552  comprise FMCW signals that linearly sweep between a first frequency f 0  (e.g., 18 kHz) and a second, higher frequency f 1  (e.g., 20 kHz or higher) over a time T sweep  (e.g., between about 5 ms and about 15 ms, between about 10 ms and about 11 ms or about 10.75 ms). 
     The individual transmitted signals  550  are emitted from a loudspeaker (e.g., the first transducer  115  of  FIG.  1   ) and a corresponding one of the reflected signals is received at a microphone (e.g., the second transducer  116  of  FIG.  2   ) a period of time. For example, the first transmitted signal  550   a  is emitted from a loudspeaker and the corresponding first reflected signal  552   a  is received a time delay Δt later. The time delay Δt is given by: 
                     Δ   ⁢   t     =       2   ⁢   d     Vsound             (   2   )               
in which d is the distance between the loudspeaker and the subject and V sound  (i.e., approximately 340 m/s at sea level). Since the transmitted frequency increases linearly in time, time delays in the reflected signals translate to frequency shifts in comparison to the transmitted signals. The frequency shift Δf between individual transmitted signals and the corresponding reflected signals is given by the following:
 
     
       
         
           
             
               
                 
                   
                     Δ 
                     ⁢ 
                     f 
                   
                   = 
                   
                     
                       
                         
                           f 
                           1 
                         
                         - 
                         
                           f 
                           0 
                         
                       
                       
                         T 
                         sweep 
                       
                     
                     ⁢ 
                     Δ 
                     ⁢ 
                     t 
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     With multiple reflectors at different distances from the receiver, their reflections translate to different frequency shifts in the signal. An FMCW receiver can extract all these frequency shifts (or demodulate the reflected signals) by performing a Fourier transform over one or more chirp durations. The chirp duration, T sweep , is selected so that the reflections from all points within an operational distance (e.g., the distance D of  FIG.  1   ) preferably start arriving before the chirp ends. In one particular embodiment, for example, the operational distance is approximately 1 meter, and a chirp duration of T sweep  10.75 ms is selected. The act of breathing creates minute chest and abdomen motion that can be captured by monitoring a corresponding bin in the Fourier transform as a function of time. One challenge, however, is that breathing movements are relatively small and thus may cause a very small frequency shift. A 2 cm breathing displacement, for example, may result in an 11.7 Hz frequency shift. Given a speed of sound of 340 m/s, a 48 kHz sampling rate translates to a resolution of 0.71 cm per sample. Further, a 10.7 ms chirp duration corresponds to 512 samples. With 18-20 kHz FMCW chirps, each sample corresponds to a 3.9 Hz frequency shift. Thus, a displacement of 0.71 cm can translate to a 3.9 Hz change in the frequency domain. Consequentially, a 2 cm breathing movement can create an 11.7 Hz frequency shift. 
     A frequency shift of 11.7 Hz can present a challenge because at a distance of 1 m and with a chirp duration of 10.75 ms, the width of each FFT bin is 93.75 Hz, which is much greater than the frequency shifts created due to breathing. To extract the minute frequency shifts created by breathing motion, an FFT is computed over an integer number of chirp durations as shown in  FIG.  5 B . This is in contrast to a traditional FMCW receiver that computes a Fourier transform over the duration of a single FMCW chirp as shown, for example in  FIG.  5 A . Computing an FFT over N chirps decreases a width of each FFT bin by a factor of N. In one embodiment, an FFT computed over ten chirps results in an FFT bin width of 9.37 Hz, allowing the capture of the 11.7 Hz frequency shifts resulting from the breathing movements. 
       FIG.  6    is a flow diagram of a process  600  configured to identify motion in accordance with an embodiment of the present technology. The process  600  begins at block  610  with monitoring a plurality of transmit/receive cycles as described above in reference to  FIG.  5 B . The process  600  receives a plurality of reflected signals (e.g., the reflected signals  552  of  FIG.  5   ) and computes a plurality of primary frequency transforms over a predetermined number N (e.g., 5, 10, 20, 40, 50) of chirps or transmit/receive cycles. As those of ordinary skill in the art will appreciate, a frequency transform converts and/or demodulates a signal from a first domain (e.g., a time domain) to a frequency domain. The primary transforms computed by the process  600  at block  610  represent frequency spectra of the reflected signals in a plurality of frequency bins. Each bin represents a discrete portion (e.g., about 1 Hz to about 100 Hz, about 5 Hz to about 50 Hz, about 8 Hz to about 12 Hz, about 9 Hz to about 10 Hz) of the frequency spectrum of the reflected signals. In some embodiments, for example, the process  600  computes a plurality of 5120-point FFTs over every series of 10 reflected signals received by the process  600 . In one particular embodiment, for example, each bin of the primary transforms has a bandwidth of approximately 9.37 Hz. 
     At block  620 , the process  600  computes a secondary frequency transform (e.g., an FFT) of an individual bin of each the primary transforms computed at block  610  over a predetermined time duration (e.g., 5 s, 10 s, 30 s, 60 s, 5 minutes, 10 minutes). When the process  600  initially proceeds to block  620 , an index value m is set to 1. Accordingly, the process  600  performs an FFT of the 1 st  bin of a plurality of the primary transforms as a function of time. In some embodiments, for example, the process  600  computes a 24,000-point FFT of the 1 st  bins of a plurality of primary transforms over time duration of 30 seconds. 
     At decision block  630 , the process  600  analyzes the secondary transform calculated at block  620  to determine whether the second transform includes one or more peaks associated with breathing frequencies. In some embodiments, for example, the process  600  analyzes the secondary transform from block  620  to determine if any peaks are detected between about 0.1 Hz or about 0.5 Hz (e.g., between about 0.2 Hz and about 0.3 Hz), which is a range that includes typical human breathing frequencies. If no peaks are detected at or near these frequency values, then the process  600  returns to block  620  and adds 1 to the index value m (i.e., m+1). The process  600  computes a new secondary transform at block  620  at the next bin m of the primary transforms over a predetermined period of time. The process  600  continues to iteratively compute secondary transforms until the process  600  detects peaks corresponding to breathing frequencies and/or until a predetermined value of m (e.g., 58, 60, 100, 200) is reached. If the process  600  detects a peak between about 0.1 Hz and about 0.5 Hz, the process  600  stores the index m corresponding to the bin number in which the peak is detected as m peak , and proceeds to block  640 . 
     At block  640 , the process  600  extracts motion data from the reflected audio signals. In some embodiments, the process  600  continues to compute a plurality of the primary transforms of the reflected audio and compute a secondary transform of bin m peak  of the primary transforms as a function of time. The process  600  can also compute a distance D between a measurement device (e.g., the device  110  of  FIG.  1   ) and the subject using the m peak  index obtained by the process  600  at block  640 . For example, if the bandwidth of each bin is approximately 9.37 Hz. and bin index m peak  obtained at block  630  is 58 (i.e., breathing motion detected in the 58 th  bin of the primary transform of block  610 ), the resulting frequency shift caused by movement of the subject is approximately 1,087 Hz (9.37 Hz*58*2). Using equation 2 above, the time delay can be obtained as approximately 5.8 ms, which corresponds to a distance of about 1 m from the subject. 
     At block  650 , the process  600  constructs a motion waveform (e.g. the motion waveform  440  of  FIG.  4 A ) of movement of the subject&#39;s chest and/or abdomen as a function of time using the secondary transform computed at block  640 . At block  660 , the process  600  ends. 
       FIG.  7    is a flow diagram of a process  700  configured to identify an apnea event in accordance with an embodiment of the present technology. At block  710 , the process analyzes peak in a motion waveform (e.g., the peaks  444  detected in the motion waveform  440  of  FIG.  4 A ). In some embodiments, the process  700  is configured to determine a pose (e.g., supine, prone, non-prone, sitting up, lying down) of the subject corresponding to one or more of the detected peaks. The process  700  can be configured, for example, to monitor a distance (e.g., the distance D of  FIG.  1   ) and/or an orientation between a measurement device (e.g., the device  110  of  FIG.  1   ) and the subject. In certain embodiments, for example, the process  700  can detect one or more aperiodic portions of the motion waveform. The process  700  can associate the one or more detected aperiodic portions of the subject&#39;s motion waveform with one or more non-breathing motions (e.g., rolling over, sitting up). If, for example, the subject rolls from one side of her body to another, the resulting motion waveform can arrive from a slightly different distance. By tracking the motion and the distance from which the breathing signal appears, the process  700  can determine an orientation of the subject. The process  700  can be further configured to use the subject&#39;s orientation information to detect positional sleep apnea in the subject. In some embodiments, the process  700  can be configured to distinguish between sleep apnea in, for example, a supine position (i.e., the subject lying with her face generally upward or away from a bed), a prone position (i.e., the subject lying with her face generally downward or toward the bed), and/or another position or pose. In some embodiments, the positional information determined by the process  700  at block  710  can be used in subsequent blocks discussed below. In additional embodiments, the process  700  may determine a position or orientation of the subject relative to the measurement device at one or more other blocks of the process  700 . 
     At decision block  720 , the process  700  determines whether one or more peaks in the motion waveform are less than a predetermined threshold (e.g., an amplitude 30% less than other peaks in the motion waveform) over a predetermined time duration (e.g., between about 5 s and 60 s, or about 10 s). If the process  700  determines that plurality of peaks are in the motion waveform are less than the predetermined threshold over the predetermined time, the process  700  outputs an indication of a hypopnea event at block  725 . Otherwise, the process  700  proceeds to block  730 . 
     At block  730 , the process  700  determines whether successive peaks in the motion waveform are separated by a time duration greater than a predetermined threshold time (e.g., 10 seconds). If the process  700  detects successive peaks in the motion waveform separated by the predetermined threshold time or greater, the process  700  outputs an indication of a central apnea event at block  735 . Otherwise, the process  700  proceeds to block  740 . 
     At decision block  740 , the process  700  determines whether successive peaks in the motion waveform include a first peak and a second, following peak in which the amplitude of the second peak is a predetermined percentage (e.g., 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100% or higher) greater than an amplitude of the first peak. If the process detects successive peaks in the motion waveform in which the second peak has an amplitude greater than the predetermined percentage of the first peak, the process  700  outputs an indication of an obstructive apnea event at block  745 . In some embodiments, the process  700  may instead detect a first peak and a second, following peak in which the second peak is a predetermined percentage (e.g., 30%, 40%, 50%, 60%, 70%, 80%, 90%) less than the first peak. At decision block  750 , the process  700  determines whether there are additional peaks in the motion waveform. If there are additional peaks in the motion waveform, the process  700  returns to block  710 . Otherwise, the process  700  ends at block  760 . 
       FIGS.  8 A- 8 C  show examples of apnea-hypopnea events that may be identified by the process  700  ( FIG.  7   ) in accordance with an embodiment of the present technology.  FIG.  8 A , for example, is a graph  801  depicting one example of a central apnea event described above with reference to block  730  of  FIG.  7   . A chest motion waveform  840   a  includes a pair of successive peaks  844   a  and  844   b  separated by time T 1  (e.g., about 15 s) greater than a predetermined central apnea threshold time (e.g., about 10 s). 
       FIG.  8 B  is a graph  802  depicting one example of hypopnea event described above with reference to block  720  of  FIG.  7   . A motion waveform  840   b  includes a plurality of peaks, including a first peak  845   a , a second peak  845   b , and a third peak  845   c . The first peak  845   a  and the second peak  845   b  comprise a plurality of peaks in the waveform  840   b  having amplitudes less than a predetermined threshold amplitude (e.g., 30% less than an amplitude of the peak  845   c ) during a predetermined time duration T 2  (e.g., about 35 s). 
       FIG.  8 C  is a graph  803  depicting one example of an obstructive apnea event described above with reference to block  740  of  FIG.  7   . A motion waveform  840   c  includes a plurality of peaks, including a first peak  846   a  and, a second peak  846   b . The second peak  846   b  has an amplitude that is ΔL (e.g., 40%, 50%, 75%) greater than the first peak  846   a  or any other peaks in the waveform  840   c  preceding the second peak  846   b  by a time T 3  and/or following the second peak  846   b  by a time T 4 . 
     The disclosure may be defined by one or more of the following examples: 
     1. A method of operating a device to identify sleep apnea events in a subject, the method comprising:
         transmitting sound energy toward the subject using a first transducer on the device, wherein the transducer is configured to generate sound energy over a range of frequencies that includes frequencies less than 20 kHz;   receiving echoes from the subject corresponding to the transmitted sound energy using a second transducer on the device, wherein the second transducer is configured to produce electrical signals corresponding to the received echoes;   generating a waveform using the electrical signals; detecting a plurality of peaks in the waveform, wherein individual peaks have a corresponding amplitude and frequency, and further wherein individual peaks are indicative of breathing motion of the subject; and   outputting an indication of a sleep apnea event for each occurrence of a period of time between successive individual peaks in the waveform exceeding a predetermined threshold time.       

     2. The method of example 1 wherein transmitting the sound energy comprises emitting a plurality of audio chirps from the first transducer, and wherein individual audio chirps linearly sweep from a first frequency to a second, higher frequency over a predetermined time duration. 
     3. The method of example 2 wherein the first frequency is about 18 kHz and the second frequency is 20 kHz or greater. 
     4. The method of examples 2 or 3 wherein at least a portion of the plurality of audio chirps comprise frequency-modulated continuous-wave sound signals emitted from the first transducer. 
     5. The method of any of examples 2-4 wherein generating the waveform comprises performing a Fourier transform of the emitted audio chirps and the corresponding received echoes over a period of time longer than the predetermined time duration of the individual chirps. 
     6. The method of example 5 wherein the period of time is approximately 10 times the predetermined time duration of the individual chirps or longer. 
     7. The method of any of examples 1-6, further comprising repeating the transmitting and receiving for a plurality of transmit/receive cycles, wherein generating the waveform further comprises determining a plurality of frequency shifts between the transmitted sound energy and the corresponding received echoes for each of the plurality of transmit/receive cycles. 
     8. The method of any of examples 1-7 wherein generating the waveform comprises filtering out signals having a frequency less than about 18 kHz. 
     9. The method of any of examples 1-8, further comprising outputting an indication of a sleep apnea event for each occurrence of an individual peak in the waveform having an amplitude less than or equal to a predetermined threshold amplitude and time period. 
     10. The method of example 9, further comprising outputting an indication of a sleep apnea event for each occurrence of an increase of 50% or greater of the amplitudes of successive individual peaks in the waveform. 
     11. The method of example 10, further comprising outputting the subject&#39;s apnea-hypopnea index, wherein outputting the subject&#39;s apnea-hypopnea index comprises determining a ratio of a total number of sleep apnea events during a sleep cycle of the subject and a duration of the sleep cycle of the subject. 
     12. The method of any of examples 1-11 wherein transmitting sound energy comprises transmitting sound energy having a wavelength greater than one half of a distance between the first transducer and the second transducer. 
     13. A method of operating an electronic device to monitor movements of a subject proximate the electronic device, the method comprising:
         emitting a plurality of audio sweep signals toward the subject from a loudspeaker operatively coupled to the electronic device, wherein individual audio sweep signals linearly sweep from a first frequency less than 20 kHz to a second, higher frequency over a predetermined time duration;   acquiring audio data at a microphone operatively coupled to the electronic device, wherein the audio data comprises echo signals that correspond to individual audio sweep signals backscattered by the subject toward the microphone;   processing the emitted audio sweep signals and the acquired audio data to generate a motion waveform; detecting one or more peaks in the motion waveform, wherein individual peaks are indicative of movements of the subject; and   outputting an indication of movement of the subject based one or more of the detected peaks.       

     14. The method of example 13 wherein the first frequency is about 18 kHz and the second frequency is 20 kHz or greater, and further wherein at least portion of the plurality of the audio sweep signals comprise frequency-modulated continuous-wave sound signals. 
     15. The method of examples 13 or 14 wherein the processing further comprises:
         calculating a plurality of frequency domain representations of the emitted audio sweep signals and the echo signals, wherein the frequency domain representations are calculated over a time period lasting a predetermined multiple of the predetermined time duration of the individual audio sweep signals; and   determining a frequency shift in the individual frequency domain representations relative to the first frequency.       

     16. The method of any of examples 13-15 wherein the individual peaks are indicative of movement of the chest and/or abdomen of the subject, wherein the individual peaks have a corresponding amplitude, and wherein outputting an indication of movement of the subject further comprises outputting an indication of a sleep apnea event for each occurrence of a period of time between successive individual peaks in the motion waveform exceeding a predetermined threshold time. 
     17. The method of example 16 wherein outputting an indication of movement of the subject further comprises outputting an indication of a sleep apnea event for each occurrence of an individual peak in the waveform having an amplitude less than or equal to a predetermined threshold amplitude. 
     18. The method of examples 16 or 17 wherein outputting an indication of movement of the subject further comprises outputting an indication of a sleep apnea event for each occurrence of an increase of 50% or greater of the amplitudes of successive individual peaks in the waveform. 
     19. The method of any of examples 13-18, further comprising:
         comparing a frequency of the detected peaks to a predetermined breathing frequency, wherein outputting an indication of movement of the subject comprises outputting an indication of a possible presence of chronic obstructive pulmonary disease in the subject if the frequency of the detected peaks is greater than equal to 115% of the predetermined breathing frequency.       

     20. A computer program product comprising a non-transitory computer readable storage medium storing computer usable program code executable to perform operations for outputting an indication of a sleep apnea event in a subject, the operations comprising:
         transmitting a plurality of chirp signals to a first transducer operatively coupled to a mobile device, wherein individual chirp signals linearly sweep from a first frequency less than 20 kHz to a second, higher frequency over a predetermined time duration;   acquiring echo data from a second transducer operatively coupled to the mobile device, wherein the echo data includes data corresponding to individual chirp signals reflected by the subject toward the second transducer;   demodulating the acquired echo data to obtain a motion signal indicative of respiratory motion of the subject;   detecting one or more amplitude peaks in the motion signal; and   outputting an indication of a sleep apnea event if a period of time between successive individual amplitude peaks in the motion signal exceeds a predetermined threshold time.       

     21. The computer program product of example 20 wherein the operations further comprise repeating the transmitting and acquiring for a predetermined number of transmit/acquisition cycles, wherein demodulating the acquired echo data comprises performing a single Fourier transform of the predetermined number of transmit/acquisition cycles. 
     The above detailed descriptions of embodiments of the technology are not intended to be exhaustive or to limit the technology to the precise form disclosed above. Although specific embodiments of, and examples for, the technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the technology, as those skilled in the relevant art will recognize. For example, while steps are presented in a given order, alternative embodiments may perform steps in a different order. The various embodiments described herein may also be combined to provide further embodiments applicable to a wide range of human physiological behaviors and illnesses. 
     Moreover, unless the word “or” is expressly limited to mean only a single item exclusive from the other items in reference to a list of two or more items, then the use of “or” in such a list is to be interpreted as including (a) any single item in the list, (b) all of the items in the list, or (c) any combination of the items in the list. Where the context permits, singular or plural terms may also include the plural or singular term, respectively. Additionally, the term “comprising” is used throughout to mean including at least the recited feature(s) such that any greater number of the same feature and/or additional types of other features are not precluded. It will also be appreciated that specific embodiments have been described herein for purposes of illustration, but that various modifications may be made without deviating from the technology. Further, while advantages associated with certain embodiments of the technology have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages to fall within the scope of the technology. Accordingly, the disclosure and associated technology can encompass other embodiments not expressly shown or described herein.