Motion index and posture change determination using millimeter wave radar

Abstract

System and method for monitoring motion of subject. Millimeter-wave reflection radar signal received from monitored subject supported by a support surface. Reflection radar signal sampled and signal portion extracted at range of subject, signal portion consisting of in-phase and quadrature signal components. Temporal difference in-phase and quadrature signals are derived and summed to form a summation signal. Adaptive thresholding applied to summation signal to establish threshold boundaries for at least one adaptive threshold for respective samples of summation signal. Motion index profile including motion index over time window is derived by normalization of summation signal according to each adaptive threshold. Extend of motion is determined based on motion index profile. Severe motion event, such as posture change, is determined when motion index exceeds selected index threshold for severe motion. Mild motion event is determined when motion index is above lower limit and below selected index threshold for severe motion.

Description

[0001] MOTION INDEX AND POSTURE CHANGE DETERMINATION USING MILLIMETER WAVE RADAR

[0002] TECHNICAL FIELD

[0003] The present disclosure generally relates to the fields of high frequency radar sensors, temporal signal processing, and motion detection.

[0004] BACKGROUND

[0005] The extent of motion of a person may be linked to behaviors or events of interest. For example, a significant movement of a person on a bed or other supporting surface may signify a change in posture. A person is in a lying or recumbent position when their body is aligned substantially horizontally in parallel to the ground and supported by an underlying surface, such as while sleeping, resting and / or recovering from an injury or illness. The body may take on different postures when in a recumbent position. In a supine posture, the person lies on their back, such that the back is resting against the underlying surface and the face is pointed upward. In a prone posture, the person lies on their front, with the chest resting against the underlying surface and the face pointed downwards. In a side posture, the body is supported on either the right side or left side of the body, where the back and limbs may be straight or bent (such as in a fetal position or recovery position). In many instances it may be useful to determine changes in posture of a lying person, such as to diagnose sleep disorders or physical ailments (e.g., pressure sores).

[0006] More generally, a determination of motion extent can denote circumstances for which suitable measures should be carried out. For example, certain individuals, such as those who are elderly, debilitated or suffering from an illness, may require assistance with leaving or entering a bed, such as for overcoming physical difficulties and avoiding injuries. In some cases, a person may be confined under medical supervision and may be permitted to leave the bed only under restrictions. A person may accidentally fall out of bed and require assistance but be physically incapable of calling for help. Conversely, a person may be required to exit the bed at specified intervals since remaining in a lying position for a prolonged duration may result in bedsores or other forms of physical deterioration and undesirable health complications. A person may be in a sleeping or a wakened state while being monitored. Information regarding their movement and posture changes, such as the frequency of waking up at night or leaving the bed to use the restroom, may provide an indication of sleep quality. An examination of movement and posture changes in conjunction with physiological measurements obtained over the course of a sleep session can suggest possible factors disturbing sleep and allow for mitigating the presence and effect of such disturbances in the future.

[0007] Extent of motion may be represented as a quantitative metric, also referred to as a “motion index”. Existing techniques for determining movement or posture changes generally rely on cumbersome sensors, such as accelerometers or inertial sensors, which need to be worn by or attached onto the body of the monitored person. Continuous monitoring using such body attached devices is generally not feasible. Other approaches are based on costly pressure sensitive sensors, such as load sensors, integrated with the bed. The sensor output may be used to derive physiological signals related to body movement, or to calculate pressure distribution on the underlying surface, which may serve as a predictor for the lying posture of the body. Bed integrated sensors generally require prior installation and frequent calibration.

[0008] Other sensors may allow for contactless monitoring. For example, motion determination may employ optical detection, such as using one or more visible-light cameras or infrared (IR) sensors. However, optical sensors typically require a direct line-of-sight to the body and clear visibility, and generally cannot function or provide degraded results under poor visibility conditions, or through obstructions or occlusions such as clothing or blanketing. More generally, contactless sensors may be prone to errors, malfunctioning and gradual deterioration. Furthermore, individual sensors have a limited field of view and even the deployment of multiple sensors may not fully encompass an adequately broad coverage area. For example, one or more sensors may be improperly aligned or provide an undersized sensing zone, thereby covering only a limited portion of the bed and / or the body of the monitored person, which can result in missed detections. An alternative approach is to devote a human operator to observe the subject while in the bed. This approach is labor intensive and time-consuming and is generally impractical for sustained periods.

[0009] Radar based systems for bed monitoring are known in the art. However, radar-based systems generally require a calibration process for each room, surface and / or subject prior to use. Such calibrations can be exceedingly cumbersome and arduous, particularly when monitoring multiple subjects in a given room, on different surfaces, or in changing settings that may not necessarily be known in advance.

[0010] SUMMARY

[0011] In accordance with one aspect of the present disclosure, there is thus provided a method for monitoring motion of a subject. The method includes the steps of receiving a millimeter-wave reflection radar signal from a monitored subject supported by a support surface, and sampling the reflection radar signal and extracting a signal portion at a range of the monitored subject, the signal portion consisting of an in-phase (I) signal component and a quadrature (Q) signal component. The method includes the steps of deriving a temporal difference in-phase signal from the in-phase signal component, and a temporal difference quadrature signal from the quadrature signal component, and deriving a summation signal by summing the temporal difference in-phase (I) signal and the temporal difference quadrature (Q) signal. The method includes the steps of applying adaptive thresholding to the summation signal to establish threshold boundaries for at least one adaptive threshold, for respective samples of the summation signal, deriving a motion index profile including motion index over a time window, by normalization of the summation signal according to each of the at least one adaptive threshold, and determining an extent of motion of the monitored subject based on the motion index profile. Determining an extent of motion may include determining a severe motion event when the motion index exceeds a selected index threshold for severe motion. The severe motion event may include a posture change. Determining an extent of motion may include determining a mild motion event when the motion index is above a lower limit of the motion index and below the selected index threshold for severe motion. The adaptive threshold may include a first adaptive threshold reflective of severe motion, and a second adaptive threshold reflective of mild motion, and the threshold boundaries may include a low mild motion threshold, a high mild motion threshold, a low severe motion threshold, and a high severe motion threshold. The step of deriving a motion index profile may include the sub-procedures of, for each of the respective samples of the summation signal: deriving a first motion index score based on the first adaptive threshold, deriving a second motion index score based on the second adaptive threshold, and determining a motion index from a combination of the first motion index score and the second motion index score. The method may further include determining a transient motion index based on a maximum or mean function of the motion index profile. The method may further include the step of down-sampling the temporal difference l / Q signals before deriving the summation signal. The adaptive thresholding may include a cell-averaging constant false alarm rate (CFAR) process. The method may further include the step of issuing a notification relating to a determined extent of motion. The signal portion may be sampled at a sampling rate of 500 Hz. The time window may be in the range of 5 to 30 seconds. The radar signal may be obtained using a remote non-invasive radar device including at least one radar transmitter, configured to transmit a radar signal to a body tissue of the subject, and at least one radar receiver, configured to receive a reflection of the transmitted radar signal reflected from the body tissue of the subject.

[0012] In accordance with another aspect of the present disclosure, there is thus provided a system for monitoring motion of a subject. The system includes a radar device, configured to receive a millimeter-wave reflection radar signal from a monitored subject supported by a support surface. The system includes a processor, configured to sample the reflection radar signal and extract a signal portion at a range of the monitored subject, the signal portion consisting of an in-phase (I) signal component and a quadrature (Q) signal component. The processor is configured to: derive a temporal difference in-phase signal from the in-phase signal component and a temporal difference quadrature signal from the quadrature signal component, and derive a summation signal by summing the temporal difference in-phase signal and the temporal difference quadrature signal. The processor is configured to apply adaptive thresholding to the summation signal to establish threshold boundaries for at least one adaptive threshold, for respective samples of the summation signal, derive a motion index profile including motion index over a time window, by normalization of the summation signal according to each of the at least one adaptive threshold, and determine an extent of motion of the monitored subject based on the motion index profile. The processor may be configured to determine a severe motion event when the motion index exceeds a selected index threshold for severe motion. The severe motion event may include a posture change. The processor may be configured to determine a mild motion event when the motion index is above a lower limit of the motion index and below the selected index threshold for severe motion. The adaptive threshold may include a first adaptive threshold reflective of severe motion, and a second adaptive threshold reflective of mild motion, and the threshold boundaries may include a low mild motion threshold, a high mild motion threshold, a low severe motion threshold, and a high severe motion threshold. The processor may be configured to derive a motion index profile by, for each of the respective samples of the summation signal: deriving a first motion index score based on the first adaptive threshold, deriving a second motion index score based the second adaptive threshold, and determining a motion index from a combination of the first motion index score and the second motion index score. The processor may be configured to determine a transient motion index based on a maximum or mean function of the motion index profile. The processor may be configured to apply a down-sampling of the temporal difference l / Q signals before deriving the summation signal. The adaptive thresholding may include a cell-averaging constant false alarm rate (CFAR) process. The system may further include a notification unit, configured to issue a notification relating to a determined extent of motion. The signal portion may be sampled at a sampling rate of 500 Hz. The time window may be in the range of 5 to 30 seconds. The radar device may be a remote non-invasive radar device including at least one radar transmitter, configured to transmit a radar signal to a body tissue of the subject, and at least one radar receiver, configured to receive a reflection of the transmitted radar signal reflected from the body tissue of the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present disclosure will be understood and appreciated more fully from the following detailed description in conjunction with the drawings in which:

[0014] Figure 1 is a schematic illustration of a system for monitoring motion of a subject, constructed and operative in accordance with an embodiment of the present disclosure;

[0015] Figure 2 is a schematic illustration of the radar device of the system of Figure 1 receiving reflections from a subject on a bed, constructed and operative in accordance with an embodiment of the present disclosure;

[0016] Figure 3A is a graph of a first exemplary set of adaptive thresholds for an exemplary summation temporal difference l / Q radar signal, operative in accordance with an embodiment of the present disclosure;

[0017] Figure 3B is a graph of a second exemplary set of adaptive thresholds for an exemplary summation temporal difference l / Q radar signal, operative in accordance with an embodiment of the present disclosure;

[0018] Figure 4A illustrates a first exemplary motion index profile, operative in accordance with an embodiment of the present disclosure;

[0019] Figure 4B illustrates a second exemplary motion index profile, operative in accordance with an embodiment of the present disclosure;

[0020] Figure 4C illustrates a third exemplary motion index profile, operative in accordance with an embodiment of the present disclosure;

[0021] Figure 4D illustrates a high-resolution view of a first portion of the third exemplary motion index profile of Figure 4C, operative in accordance with an embodiment of the present disclosure;

[0022] Figure 4E illustrates of a high-resolution view of a second portion of the third exemplary motion index profile of Figure 4C, operative in accordance with an embodiment of the present disclosure;

[0023] Figure 4F illustrates a fourth exemplary motion index profile, operative in accordance with an embodiment of the present disclosure;

[0024] Figure 4G illustrates a fifth exemplary motion index profile, operative in accordance with an embodiment of the present disclosure; and

[0025] Figure 5 is a flow diagram of a method for monitoring motion of a subject, operative in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The present disclosure may overcome the disadvantages of the prior art by providing a method and system for monitoring motion of a subject to determine a motion index reflecting an extent of motion and detecting substantial motion events such as posture changes, with a high degree of accuracy and in a contact free manner, and without requiring a time-consuming calibration process for different environments, for different lying surfaces, and / or for different subjects.

[0027] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed subject matter belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and claims and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and / or clarity.

[0028] It will be understood that numerical terms, such as first, second, and the like, may be used herein to describe various elements, components, regions, layers and / or sections, which should not be limited by these numerical terms. Rather, these numerical terms are only used to distinguish one element, component, region, layer and / or section, from another element, component, region, layer and / or section.

[0029] It will be understood that when an element is referred to as being “on”, “attached” to, “operatively coupled” to, “operatively linked” to, “operatively engaged” with, “connected” to, “coupled” with, “contacting”, “added to, another element, it can be directly on, attached to, connected to, operatively coupled to, operatively engaged with, coupled with, added to, and / or contacting the other element or intervening elements can also be present. In contrast, when an element is referred to as being “directly contacting” or “directly added to” another element, there are no intervening elements and / or steps present. Whenever the term “about” or “approximately” is used, it is meant to refer to a measurable value such as an amount, a temporal duration, and the like, and is meant to encompass variations (e.g., ±20%, ±10%, ±5%, ±1 %, ±0.1 %) from a specified value, as such variations are appropriate to perform the disclosed methods.

[0030] Certain features of the disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the disclosure. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

[0031] Throughout this application, various embodiments of the present disclosure may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosed embodiments. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range, regardless of the breadth of the range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as individual numbers within that range, for example, 1 , 2, 3, 4, 5, and 6. Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. For example, the phrases “ranging / ranges between” a first indicated number and a second indicated number and “ranging / ranges from” a first indicated number “to” a second indicated number are used herein interchangeably and are meant to include the first and second indicated numbers and all fractional and integral numerals there between.

[0032] Whenever terms “plurality” and “a plurality” are used it is meant to include, for example, “multiple” or “two or more”. The terms “plurality” or “a plurality” may be used throughout the specification to describe two or more components, devices, elements, units, parameters, or the like. The term set when used herein may include one or more items. Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Some of the described method embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently. The term “repeatedly” as used herein should be broadly construed to include any one or more of: “continuously”, “periodic repetition” and “nonperiodic repetition”, where periodic repetition is characterized by constant length intervals between repetitions and non-periodic repetition is characterized by variable length intervals between repetitions.

[0033] The term “subject” is used herein to refer to an individual upon which the method or system of the present disclosure is performed, such as a person undergoing a determination of motion. The subject may be any living entity, such as a person, human or animal, characterized with a functioning heartbeat associated with a cardiac cycle of the heart. The terms “user” and “operator” are used interchangeably herein to refer to any individual person or group of persons using or operating at least one aspect of a method or system of the present disclosure. The terms “motion” and “movement” may be used interchangeably herein, to refer to a change in a position of a subject over time, including positional changes of one or more body portions of the subject.

[0034] Reference is now made to Figure 1 , which is a schematic illustration of a system, generally referenced 1 10, for monitoring motion of a subject, constructed and operative in accordance with an embodiment of the present disclosure. System 110 includes a radar device 112, a processor 114, a user interface 116, a notification unit 117, and a database 118. Processor 114 is communicatively coupled with radar device 112, with user interface 116, with notification unit 117, and with database 118.

[0035] Radar device 112 is configured to transmit a radar signal 122 to a monitoring area occupied by an intended subject 120. Radar device 112 is further configured to receive a reflection radar signal 124 respective of the transmitted radar signal 122, such as a reflection signal reflected from a body part of subject 120. In one example, the monitoring area includes a supporting surface for supporting a subject 120, such as a bed or a chair. Reference is further made to Figure 2, which is a schematic illustration of radar device 112 of system 110 receiving reflections from a subject 120 on a bed 130, constructed and operative in accordance with an embodiment of the present disclosure. System 110 may be applied for monitoring movement of a subject 120 supported on a bed 130. For example, subject 120 may be in a lying or recumbent position on bed 130, with the subject body aligned substantially horizontally, such as in a prone posture, a supine posture, or a side posture, such as while subject 120 is in a sleeping or resting state. In another example, subject 120 may be in a sitting position, such that an upper body portion of the subject is substantially upright. In a further example, subject 120 may be in a reclined or inclined position, such that an upper body portion of the subject is aligned at an angle relative to a horizontal plane, such as by tilting a portion of bed 130. Bed 130 may generally encompass various types of supporting surfaces, including but not limited to: a couch; a sofa; a cot; a crib; a divan; a mattress; a box-spring; and the like. Radar device 112 may be positioned in the vicinity of bed 130, such as mounted or coupled to a structure above bed 130 (e.g., a wall, a ceiling, a post) and directed to a supporting surface of bed 130, such that a field of view (FOV) of radar device 112 encompasses at least a portion of bed 130 where subject 120 may be present. In one example, the FOV of radar device 112 encompasses substantially an entirety of bed 130. In another example, a plurality of radar devices 112 may be deployed, such that each individual radar device 112 is directed to a respective FOV in the vicinity of bed 130, where the FOVs of multiple radar devices 112 may encompass an entirety of bed 130. Radar device 112 may be positioned at a predetermined distance from bed 130, such as within a few meters.

[0036] Radar device 112 includes at least one radar transmitter and at least one radar receiver. Each radar transmitter and radar receiver may be embodied by one or more transmitting / receiving elements, such as an individual radar antenna or an array of radar antennas, for example a phased array radar, such as a multiple-input multiple-output (MIMO) radar. The transmitted radar signal 122 may be at a sufficiently high frequency to ensure that the signal is reflected and not absorbed by the body tissue, for example in the millimeter wave (MMW) frequency band (corresponding to EHF radio frequencies). According to an embodiment of the present disclosure, the transmitted and reflected radar signals 122, 124 is in the Terahertz (THz) frequency band, where the term “Terahertz (THz)” as used herein encompasses Terahertz and sub-Terahertz radiation corresponding to sub-millimeter and millimeter wave radiation, such as electromagnetic waves within the frequency band between about 0.03 to 3 THz, corresponding to radiation wavelengths between about 10 mm to 0.1 mm. In one example, transmitted and reflected radar signals 122, 124 is at a frequency above 100 GHz, such as approximately 120 GHz (or 0.12 THz).

[0037] Radar device 112 may be as described for example in PCT application publication WO2018 / 167777A1 to Neteera Technologies, entitled “Method and device for non-contact sensing of vital signs and diagnostic signals by electromagnetic waves in the sub terahertz band”, and PCT application publication W02020 / 012455A1 to Neteera Technologies, entitled “A sub-THz and THz system for physiological parameters detection and method thereof”. It is noted that radar device 112 operates in a contactless manner, which transmits and receives radar signals remotely without requiring a device component to be in direct physical contact with subject 120 or to be worn or attached to subject 120. It is further noted that radar device 112 may transit and / or receive a reflected radar signal from any direction of subject 120, such as from a front or back direction or from a non-orthogonal angle relative to subject 120. Moreover, radar device 112 may transmit and receive a reflected radar signal in low light or poor visibility conditions, as well as through certain obstructions or material barriers covering the subject body, where the radar signal may penetrate through clothing worn by subject 120, or a fabric or other material of bed 130 on which subject 120 may be positioned.

[0038] Processor 114 receives information or instructions from other components of system 110 and performs required data processing. For example, processor 114 receives and processes reflected radar signals 124 obtained by radar device 112 to determine a motion index and posture change of subject 120, as will be elaborated upon further hereinbelow.

[0039] User interface 1 16 allows a user to receive information and to control parameters or settings associated with system 110. For example, user interface 116 may include a display screen configured to present visual content. User interface 116 may include a cursor and / or a touch-screen menu interface, such as a graphical user interface, configured to enable manually entering instructions or data. User interface 116 may also include peripheral communication devices configured to provide audible communication, such as a microphone and an audio speaker, and may include voice recognition capabilities to enable the user to enter instructions or data by means of speech commands.

[0040] Notification unit 117 is configured to provide alerts or notifications relating to the operation of system 110. For example, notification unit 117 may issue a first alert when a determined motion index exceeds a first threshold value, and a second alert when a motion index exceeds a second threshold value, as well as additional indications relating to possible events or behaviors associated with a determined motion index of subject 120. Notification unit 117 may utilize various notification modes, such as visual indications (e.g., displaying text, markings and / or symbols, changing colors of graphical content) and / or audible indications (e.g., alarms, beeps, buzzers, bells, ringtones). Accordingly, notification unit 1 17 may include one or more devices or instruments configured for audio communication (e.g., an audio speaker, a speech generator) and / or visual communication (e.g., a display screen; a graphical user interface; flashing lights). Notification unit 117 may optionally be integrated with user interface 1 16 or may be a separate unit. Notification unit 117 may be configured to provide alerts or notifications to a remote location, such as by transmitting an indication to at least one of: an operator computer device (e.g., via a dedicated application); a cloud service, a remote internet application, a web-based communication network; a supervisor or central monitoring station (e.g., associated with a health care facility), and the like. A plurality of notification units 1 17 may be employed for providing alerts at different locations.

[0041] Database 118 stores relevant information to be retrieved and processed by processor 114, such as radar signal data and associated information. Database 118 may be represented by one or more local servers or by remote and / or distributed servers, such as in a cloud storage platform.

[0042] Information may be conveyed between the components of system 110 over any suitable data communication channel or network, using any type of channel or network model and any data transmission protocol (e.g., wired, wireless, radio, WiFi, Bluetooth, and the like). For example, system 110 may store, manage and / or process data using a cloud computing model, and the components of system 110 may communicate with one another and be remotely monitored or controlled over the Internet, such as via an Internet of Things (loT) network. The components and devices of system 110 may be based in hardware, software, or combinations thereof. It is appreciated that the functionality associated with each of the devices or components of system 110 may be distributed among multiple devices or components, which may reside at a single location or at multiple locations. For example, the functionality associated with processor 114 may be distributed between a single processing unit or multiple processing units. Processor 114 may be part of a server or a remote computer system accessible over a communications medium or network, such as a cloud computing platform. Processor 114 may also be integrated with other components of system 110, such as incorporated with radar device 112. System 110 may optionally include and / or be associated with additional components not shown in Figure 1 , for enabling the implementation of the disclosed subject matter.

[0043] The operation of system 110 will now be described in general terms, followed by specific examples. Radar device 112 transmits a coherent radar signal 122 in the THz frequency band, such as a continuous-wave (CW) or frequency modulated continuous wave (FMCW) radar signal, to a monitoring area occupied by at least one subject 120, such as on a bed 130. Radar device 112 may receive a corresponding reflected radar signal 124 from a body part of subject 120, where reflection radar signal 124 contains information relating to micro-displacements in the skin associated with the cardiac cycle and pulmonary activity of subject 120. Transmitted radar signal 122 may be incident on different body parts of subject 120, such as an anterior body area (e.g. , the chest), or a posterior body area (e.g. , the back). Processor 114 receives the reflected radar signal 124, obtained at a selected sampling rate and recorded with two channels consisting of an in-phase (I) component and a quadrature (Q) component. For example, reflected radar signal 124 may be sampled at a rate of 500 Hz, such that 500 times per second a complex valued signal composed of in-phase (I) and quadrature (Q) data is collected. Processor 114 may determine the distance or range traversed by the propagating radar signal, so as to extract the signal portion corresponding to reflections from subject 120 and removing noise and irrelevant signal components at other ranges. For example, if transmitted and reflected radar signals 122, 124 are FMCW radar signals, then processor 114 may apply a fast Fourier transform (FFT) to extract the signal portion at a selected range bin corresponding to the subject range. A determination of the subject range may be implemented, for example, in accordance with methods described in PCT application publication WO2013 / 275865A1 to Neteera Technologies, entitled “Radar-based range determination and validation”.

[0044] The extracted reflection radar signal portion (e.g., the output of the FFT at the subject range) collected at a selected sampling rate (e.g., 500 Hz) and consisting of an in-phase (I) waveform and a quadrature (Q) waveform, may optionally undergo pre-processing. For example, a nonlinear filtering or mapping operation may be applied, such as to produce a complex-valued (ballistocardiograph) signal providing a time-domain representation of ballistic forces associated with cardiac activity and pulmonary activity. The reflection radar signal is then processed to derive absolute temporal difference signals for each of the I and Q waveforms. In particular, for each of the in-phase (I) and quadrature (Q) channels, the difference between values of adjacent (e.g., consecutive) samples is calculated over a given time window, resulting in a vector or array of absolute temporal difference values for that time window. For example, temporal difference signals are obtained from samples collected at 500 Hz by performing incremental calculations (e.g., every second) over a selected time window, such as approximately 10 seconds. The obtained temporal difference vectors (discretized signals), in both the l / Q channels, may then undergo a resampling with a moving average, such as by applying a down-sampling operation. For example, each of the temporal difference vectors may be down-sampled to a frequency of 1 Hz. The down-sampling may be performed repeatedly at selected increments (e.g., every 1 sec).

[0045] The resampled temporal difference signals in the I and Q channels are added together to produce a summation signal. The summation signal corresponds to a single vector made up of summed l / Q temporal difference values for multiple samples during the selected time window. In one example, the summation signal is at a (down-sampled) frequency of 1 Hz. The summation signal may undergo normalization, such as by dividing each element in the vector by a maximum summation sample value.

[0046] The summation signal is then processed to establish one or more adaptive thresholds, such as using an adaptive thresholding technique. Adaptive thresholding may facilitate more precise signal detection by differentiating significant signal fluctuations from background noise and clutter. In one example, adaptive thresholds are established using constant false alarm rate (CFAR) based processing, such as a cell averaging technique. In a cell-averaging CFAR process, a threshold may be calculated by estimating a level of noise floor around a selected cell (i.e. , a “cell under test”), by determining an average level of a group of cells in the vicinity (i.e., “training cells”) while ignoring immediately adjacent cells (i.e., “guard cells”) to avoid signal components of the cell under test effecting the training cells. A target may be considered present in the cell under test if it is greater than all adjacent cells and greater than the local average level.

[0047] A plurality of adaptive thresholds may be established for each cell (sample) in the summation signal. The adaptive thresholds may be associated with different extents of motion that may be indicative of certain behaviors or events, such as to differentiate between minor or “mild motion”, such as a minor body movement, and substantial or “severe motion”, such as a posture change. In one example, an adaptive threshold reflective of substantial or severe motion, referred to herein as a “severe motion threshold”, is determined. In another example, there is determined a first adaptive threshold reflective of severe motion (i.e., a severe motion threshold), and a second adaptive threshold reflective of minor or mild motion, referred to herein as a “mild motion threshold”. Each of the first and second adaptive thresholds may be associated with threshold boundaries or limits, such as a low threshold value and a high threshold value. For example, four different thresholds may be determined for each cell: a low mild motion threshold, a high mild motion threshold, a low severe motion threshold, and a high severe motion threshold. In another example, two thresholds may be determined for each cell: a low severe motion threshold and a high severe motion threshold. The adaptive thresholding process may be applied for all cells that exceed a basic minimum threshold that is independent of calculated noise level, such as to exclude irrelevant signal portions for motion detection. For example, for a given summation signal (e.g., of sampling rate 1 Hz and having a duration of 22 seconds), a cell-averaging CFAR process is applied to each cell by defining training cells (e.g., neighboring cells over 4 seconds) and guard cells (e.g., neighboring cells over 2 seconds), and defining a first factor for a high mild motion threshold, a second factor for a high severe motion threshold, a third factor for a low mild motion threshold, a fourth factor for a low severe motion threshold, and a basic minimum threshold. These factors are then used to determine respective adaptive thresholds for each cell. The adaptive thresholds are generally dependent on the signal characteristics, such that signal portions with higher noise may have higher adaptive thresholds. The adaptive thresholding process may be applied repeatedly and / or iteratively.

[0048] Reference is made to Figures 3A and 3B. Figure 3A shows a graph, referenced 150, of a first exemplary set of adaptive thresholds for an exemplary summation temporal difference l / Q radar signal, and Figure 3B shows a graph, referenced 160, of a second exemplary set of adaptive thresholds for an exemplary summation temporal difference l / Q radar signal, operative in accordance with embodiments of the present disclosure. Each of graphs 150, 160 shows a respective exemplary summation signal 152, 162 plotted as a function of time, where each summation signal 152, 162 is derived from temporal difference I and Q waveforms of a reflection radar l / Q signal received from a subject. Graph 150 depicts a first adaptive threshold 154 and a second adaptive threshold 156 for summation signal 152, and graph 160 depicts a first adaptive threshold 164 and a second adaptive threshold 166 for summation signal 162, where the thresholds may be determined using adaptive thresholding (e.g., cell-averaging CFAR) processes. For example, first adaptive thresholds 154, 164 may represent a high threshold value of a severe motion threshold (i.e., a high severe motion threshold), and second adaptive thresholds 156, 166 may represent a low threshold value of a severe motion threshold (i.e., a low severe motion threshold). For the time period shown in graph 150, the summation signal 152 exceeds low severe motion adaptive thresholds 156 at three different times (ti , t2, ts), which is indicative of a severe motion event (such as a posture change) at each of these instants, such that a total of three severe motion events may have occurred during the depicted time period. For the time period shown in graph 160, the summation signal 162 exhibits peaks at three different times (ti, t2, ts). At the first two time instants (ti, t2) the signal peaks do not exceed the low severe motion threshold 166, such that there is not a severe motion event established, but may reflect a mild motion event at these times. At the third time instant (ts), signal 162 exceeds low severe motion threshold 166, indicating a severe motion event at time ts.

[0049] Following the establishment of the thresholds, a feature scaling process, also known as “min-max scaling” or “min-max normalization”, may be applied to the summation signal in accordance with the adaptive thresholds. Specifically, the summation signal is normalized between the adaptive thresholds to produce a normalized motion index signal (also referred to herein as a “motion index profile”) that is reflective of subject motion. If multiple adaptive thresholds have been established, where each adaptive threshold is associated with a respective motion extent, then an intermediate motion index or “motion index score” may be determined for each of the adaptive thresholds, by feature scaling of the summation signal between low and high threshold values for the respective adaptive threshold. The intermediate motion indices may then be combined to derive an overall motion index, which is reflective of a combination of the respective motion extents. Following max-min normalization, all values above an upper limit are normalized to the upper limit, and all values below a lower limit are normalized to the lower limit.

[0050] For example, the adaptive thresholding process may result in a single adaptive threshold reflective of severe motion, which includes a low severe motion threshold and a high severe motion threshold for each summation signal cell. Accordingly, the summation signal undergoes max-min normalization between the low severe motion threshold and a high severe motion threshold to derive a motion index between a normalized range of values (e.g., between 0 to 1 ). Specifically, a motion index is derived by subtracting the low severe motion threshold from the summation signal (without edge values) and dividing the result by a rescale factor corresponding to the difference between the high severe motion threshold and the low severe motion threshold:

[0051] . , (sum-siq -low-sev-thr') (sum-siq -low-sev-thr) motion-index = - - - = - - - - - rescale-sev high-sev-thr -low-sev-thr)

[0052] In another example, the adaptive thresholding process may establish a first adaptive threshold reflective of severe motion, and a second adaptive threshold reflective of mild motion, each associated with a low threshold value and a high threshold value, such that four adaptive thresholds are determined for each summation signal cell. In such a case, a first motion index score may be derived for the first adaptive threshold reflective of severe motion, by subtracting the low severe motion threshold from the summation signal (without edge values) and dividing the result by a rescale factor corresponding to the difference between the high severe motion threshold and the low severe motion threshold:

[0053] (sum-sig-low-sev-thr) > (sum-sig-low-sev-thr) motion-index-score1rescale-sev (Jiigh-sev-thr-low-sev-thr

[0054] The resultant first motion index score is normalized between a first range of values (e.g., between 0.5 to 1 ).

[0055] A second motion index score may be derived for the second adaptive threshold for mild motion, by subtracting the low mild motion threshold from the summation signal (without edge values) and dividing the result by a rescale factor corresponding to the difference between the high mild motion threshold and the low mild motion threshold:

[0056] . , (sum-sig-low-mild-thr) (sum-sig-low-mild-thr) motion-index-score = - re -s -cale-mild (high-mild-thr-low-mild-thr

[0057] The resultant second motion index score is normalized between a second range of values (e.g., between 0 to 0.5).

[0058] An overall or final motion index may then be established by combining the first motion index score with the second motion index score, such as by selecting a maximum value from each index score: motion-index = max[motion-index-score1,motion-index-score2}.

[0059] The resultant motion index is normalized between a third range of values, such as between a lower limit and an upper limit of the first and second motion index scores (e.g., between 0 to 1 ).

[0060] The final motion index defines a value within a continuous index between the lower limit and the upper limit (e.g., in a range between 0 to 1 ) and reflects an overall extent of motion of the monitored subject. Accordingly, a motion index profile is obtained from the summation signal over a selected duration, such as a time window between 5 to 30 seconds (e.g., about 20 seconds), where the motion index profile includes calculated values of motion index for that duration. A transient or overall motion index value over a given period may be determined based on characteristics of the derived motion profile during that period, such as by applying a maximum function or a mean function. For example, a maximum value or dominant peak of the motion index profile over a time window (e.g., 22 seconds) may represent an overall motion index for that time window. In another example, a mean or average value of the motion index profile over a time window is determined to represent an overall motion index.

[0061] A motion event of the monitored subject may be determined based on the motion index profile. For example, when the motion index is above a predetermined index threshold value or cutoff point, it may be indicative of a severe motion event, such as a change in posture. In one example, the index threshold for severe motion may be at approximately a midpoint between a lower limit and an upper limit of the motion index, such as approximately 0.5 for a max- min normalization between 0 and 1 . When the motion index is positive (e.g., above the normalized index lower limit) but below the predetermined index threshold for severe motion, it may be indicative of a mild motion event, such as a minor or negligible bodily movement. More generally, the value of the motion index may reflect a degree or severity of motion of the subject, such that a higher motion index corresponds to a more substantial or more severe motion, while a lower motion index corresponds to a less substantial or milder motion. A motion event determination may be based on a transient value of the motion index, such as at a given instant in time (e.g., for a given processing time window), and / or based on a prolonged value of the motion index, such as for an extended duration (e.g., within a single time window or over multiple time windows). For example, if the motion index exceeds the threshold for a predetermined frequency (e.g., at least a certain number of times) and / or for a predetermined duration over a given time window (e.g., for at least a certain number of seconds), then a motion event may be established. A motion event determination may be further based on additional characteristics of a motion index profile, such as motion index transitions, or characteristics of a motion index before and / or after a given time window.

[0062] Reference is now made to Figures 4A through 4G, directed to exemplary motion index profiles, operative in accordance with embodiments of the present disclosure. Each of the exemplary motion index profiles is obtained following normalization of a summation temporal difference signal derived from a respective exemplary reflection radar l / Q signal.

[0063] Figure 4A shows a first exemplary motion index profile 215 for an exemplary reflection radar signal 210 composed of an in-phase waveform 211 and a quadrature waveform 212. Motion index profile 215 exhibits a substantially large peak 217 at a time instant ti that exceeds a predetermined index threshold. Accordingly, a severe motion event, such as a posture change, may be determined to have occurred at time instant ti. It is noted that motion index peak 217 coincides with a change in characteristics of l / Q waveforms 211 , 212. In general, a high motion index may result from a low local adaptive threshold and / or high local summation of temporal differences of l / Q signals.

[0064] Figure 4B shows a second exemplary motion index profile 225 for an exemplary reflection radar signal 220 composed of an in-phase waveform 221 and a quadrature waveform 222 over a time window of about 8 minutes. Motion index profile 225 exhibits a first small peak 226 at a first time instant ti, and a second large peak 227 at a second time instant t2. First peak 226 may be below a predetermined index threshold for severe motion, such that a mild motion event is established at time ti , whereas second peak 227 is above the index threshold for severe motion, such that a severe motion event is established at time t2. It is noted that a very high value of motion index may increase the confidence level of a severe motion event determination. It is noted that l / Q waveforms 221 , 222 exhibit a null signal in region 224, which may reflect that no reflection radar signal received for that time period, such as if the monitored subject has left the monitoring area (e.g., exited the bed).

[0065] Figure 4C shows a third exemplary motion index profile 235 for an exemplary reflection radar signal 230 composed of an in-phase waveform 231 and a quadrature waveform 232 over a time window of about 40 minutes (i.e., between about 23:50 to about 00:30). Motion index profile 235 exhibits a plurality of peaks, some of which may exceed a predetermined index threshold and may be indicative of severe motion events (such as at times t2, ts, ts, te), and some of which may be below the predetermined index threshold and may be indicative of mild motion events (such as at times ti , t4).

[0066] Figure 4D shows a high-resolution view of a first portion of exemplary motion index profile 235 of FIG. 4C, in a selected duration of about 2 minutes of the overall 40-minute period (i.e., between about 23:59:30 to about 00:01 :45). In particular, FIG. 4D shows a zoomed-in view of peak 236 at time ti (that may be a mild motion event) and a zoomed-in view of peak 237 at time t2 (that may be a severe motion event).

[0067] Figure 4E shows a high-resolution view of a second portion of motion index profile 235 of FIG. 4C, in a selected duration of about 1 minute of the overall 40-minute period (i.e., between about 00:24:30 to about 00:25:20). In particular, FIG. 4E shows a zoomed-in view of peak 238 at time te, where peak 238 is characterized by a first crest followed by a brief minor dip, followed by a second crest. Despite having multiple portions at different amplitudes, peak 238 may be indicative of a single motion event of the subject, such as a severe motion event. A time-extended peak may increase the confidence level of a severe motion event determination.

[0068] Figure 4F shows a fourth exemplary motion index profile 245 for an exemplary reflection radar signal 240 composed of an in-phase waveform 241 and a quadrature waveform 242 over a time window of about 5 minutes. Motion index profile 245 exhibits a large peak 247 that may be indicative of a severe motion event at time instant ti. Despite l / Q waveforms 241 , 242 being characterized by a substantially high level of noise, motion index profile 245 does not appear to incorrectly indicate a motion event before or after time ti , such that there are no false positive detections.

[0069] Figure 4G shows a fifth exemplary motion index profile 255 for an exemplary reflection radar signal 250 composed of an in-phase waveform 251 and a quadrature waveform 252 over an extended time window of about 5 hours. Motion index profile 255 exhibits a plurality of major peaks at varying instances over an extended period, indicative of multiple motion events, including a number of mild motions and severe motions, throughout the extended period.

[0070] Reference is now made to Figure 5, which is a flow diagram of a method for monitoring motion of a subject, operative in accordance with an embodiment of the present disclosure. In a step 302, a reflection radar signal is received from a monitored subject. Referring to FIGS. 1 and 2, radar device 112 transmits a coherent THz radar signal 122, such as a FMCW signal, toward a monitored subject 120, such as a subject 120 positioned on a bed 130. Radar device 112 receives a corresponding reflected radar signal 124 from a body part of subject 120, such that reflection radar signal 124 contains information relating to micro-displacements in the skin associated with the cardiac cycle and pulmonary activity of subject 120.

[0071] In step 304, the reflected signal is sampled with in-phase (I) and quadrature (Q) components and a signal portion at the range of the subject is extracted. Referring to FIGS. 1 and 2, processor 114 receives and filters reflected radar signal 124 based on signal phase differences that correlate with distance, to isolate signal components at the range at which subject 120 is located. Specifically, processor 114 receives the reflected radar signal 124, obtained at a selected sampling rate (e.g., 500Hz) and recorded with two channels consisting of an in-phase (I) component and a quadrature (Q) component, and extracts a signal portion corresponding to reflections from subject 120 and removing noise and irrelevant signal components at other ranges. Pre-processing operations may optionally be applied to the resultant signal, such as to enhance or facilitate subsequent signal processing.

[0072] In step 306, absolute temporal difference signals are derived over a selected time window for each of the l / Q signal components. Referring to FIGS. 1 and 2, processor 114 processes each of the in-phase (I) and quadrature (Q) waveforms of reflection radar signal 124 to derive respective temporal difference signals based on absolute temporal difference between adjacent signal samples. Processor 114 derives a vector of absolute temporal differences between adjacent or neighboring samples over a selected time window (e.g., about 20 seconds), for each of the l / Q waveforms.

[0073] In step 308, a down-sampling operation is applied to the temporal difference signals. Referring to FIG. 1 , processor 114 resamples the absolute temporal difference signals for each of the I and Q channels, such as down-sampling the signals from 500 Hz to 1 Hz.

[0074] In step 310, a summation signal is derived by summing the temporal difference signals. Referring to FIG. 1 , processor 114 derives a summation signal by adding the resampled temporal difference signals in the I and Q channels.

[0075] In step 312, an adaptive thresholding process is applied to the summation signal to establish threshold boundaries for at least one adaptive threshold. Referring to FIG. 1 , processor 114 applies an adaptive thresholding technique, such as a cell-averaging CFAR process, to determine boundaries of one or more adaptive thresholds for each cell or sample in the derived summation signal. For example, processor 114 determines an adaptive threshold reflective of severe motion. In another example, processor 114 determines a first adaptive threshold reflective of severe motion, and a second adaptive threshold, reflective of mild motion. Each of the adaptive thresholds is associated with threshold boundaries, such as a high threshold value and a low threshold value. For example, processor 114 determines for respective cells in the summation signal a high threshold boundary and low threshold boundary for a severe motion adaptive threshold, such as high adaptive threshold 154 and low adaptive threshold 156 for summation signal 152 (shown in FIG. 3A). In another example, processor 114 determines for each summation signal cell a low mild motion threshold, a high mild motion threshold, a low severe motion threshold, and a high severe motion threshold. The adaptive threshold boundaries may be determined by applying an adaptive thresholding process for all cells above a basic minimum threshold value.

[0076] In step 314, a motion index profile over a time window is derived by normalization of the summation signal according to each adaptive threshold. Referring to FIG. 1 , processor 114 applies a feature scaling or min-max normalization to the summation signal according to each adaptive threshold, using a rescaling factor based on the difference between the threshold boundaries of the adaptive threshold. For example, processor 114 derives a normalized motion index signal according to an established severe motion adaptive threshold, by applying a min-max normalization of the summation signal between a high severe motion threshold and a low severe motion threshold. The derived motion index profile reflects an extent of motion of monitored subject 120 for a given period.

[0077] When there are multiple adaptive thresholds established, an intermediate motion index or motion index score may be determined for each adaptive threshold, by min-max normalization of the summation signal between the (low / high) threshold boundaries of the respective adaptive threshold. The motion index scores may then be combined to produce a final motion index in a normalized range of values. Accordingly, step 314 may optionally include subprocedures 316, 318, 320. In sub-procedure 316, a first motion index score is derived based on a first adaptive threshold. Referring to FIG. 1 , processor 114 determines a first motion index score for a severe motion adaptive threshold by normalizing the summation signal between the threshold boundaries of the severe motion adaptive threshold. Specifically, processor 114 derives a first motion index score in a first normalized range (e.g., between 0.5 to 1 ) by subtracting a low severe motion threshold from the summation signal without edge values and dividing the result by a rescaling factor corresponding to the difference between the high and low severe motion thresholds. In sub-procedure 318, a second motion index score is derived based on a second adaptive threshold. Referring to FilG. 1 , processor 114 determines a second motion index score for a mild motion adaptive threshold by normalizing the summation signal between the threshold boundaries of the mild motion adaptive threshold. Specifically, processor 114 derives a second motion index score in a second normalized range (e.g., between 0 to 0.5) by subtracting a low mild motion threshold from the summation signal without edge values and dividing the result by a rescaling factor corresponding to the difference between the high and low mild motion thresholds. In sub-procedure 320, a motion index is derived from a combination of the first motion index score and the second motion index score. Referring to FIG. 1 , processor 114 combines the first motion index score based on the first adaptive threshold and the second motion index score based on the second adaptive threshold to produce a final motion index signal in a normalized range, such as normalized between a lower limit and an upper limit of the first and second motion index scores (e.g., between 0 to 1 ). Processor 114 thereby derives from the summation signal a motion index profile characterizing a motion index for the time window of the summation signal, (reflecting an extent of motion of subject 120 during that time window), such as motion index profile 225 (shown in FIG. 4B) representing a motion index over a time window of about 8 minutes. Processor 114 may determine a transient or overall motion index over an extended duration based on characteristics of the motion index profile, such as by applying a maximum or mean function over the duration. For example, referring to FIG. 4B, a maximum value of peak 227 of motion index profile 225 may be established as a motion index for the time window of profile 225.

[0078] In step 322, an extent of motion is determined based on the motion index profile. Referring to FIG. 1 , processor 114 evaluates the motion index profile to determine a degree or extent of motion of monitored subject 120, where a higher motion index corresponds to a more substantial or more severe motion of subject 120, and a lower motion index corresponds to a less substantial or milder motion of subject 120. Determining an extent of motion may include determining that a motion event has occurred. Accordingly, processor 114 may determine if a motion event has occurred by evaluating the motion index in relation to one or more predetermined index thresholds. Step 322 may include sub-procedures 324, 326. In sub-procedure 324, a severe motion event is determined when a motion index is above a selected index threshold. Referring to FIG. 1 , processor 114 determines that a severe motion event has occurred, such as a posture change of subject 120, when the motion index exceeds a predetermined index threshold for severe motion. For example, referring to FIG. 4B, motion index profile 225 has a peak value at time t2 that exceeds a predetermined index threshold for a severe motion event, such that a severe motion event (e.g., a posture change) of subject 120 is established at time t2. In one example, a threshold value for establishing a positive detection of a severe motion event is set at approximately a midway point between an upper and lower limit of the motion index, such as at about 0.5 for a normalized index between 0 and 1. In sub-procedure 326, a mild motion event is determined when a motion index is above a lower limit of the motion index and below a selected index threshold. Referring to FIG. 1 , processor 114 determines that a mild motion event has occurred when the motion index is above a lower index limit (e.g., above 0) but does not exceed a predetermined index threshold for severe motion (e.g., below 0.5). For example, referring to FIG. 4B, motion index profile 225 has a peak value at time ti that is below a predetermined index threshold for a severe motion event, such that, a mild motion event of subject 120 is established at time ti , such as a minor bodily movement. In general, a higher motion index value may correspond to a greater extent of motion of subject 120. A subject may be characterized by multiple motion events over an extended duration, such as plurality of severe motion events and mild motion events exhibited in motion index profile 235 (Fig.4C) and in motion index profile 255 (Fig.4G). Processor 114 may further determine additional parameters or statistics relating to the motion index over the time window, which may provide additional information relating to behaviors or characteristics of subject 120. In step 322, a notification is issued. Referring to FIGS. 1 and 2, notification unit 117 issues a notification or alert, such as relating to a determined posture change or other motion event of monitored subject 120. For example, notification unit 117 may issue an alert when a severe motion event such as a posture change is detected. The notification may be provided as a visual indication and / or an audible indication. An issued notification may be linked to one or more conditions, which may be configured or modified by a user. For example, an alert may be issued if a subject 120 is detected to be in a changing posture state for a predefined minimum time interval. For another example, an alert may be issued if a frequency of detected posture change of subject 120 exceeds a predefined maximum limit during a certain time period (e.g., if more than 3 posture changes are detected during a period of about 30 seconds). Such conditions (i.e., a minimum number or frequency of posture changes) may be indicative of a physical ailment of subject 120, such as pressure sores. Alerts may also be provided relating to lack of motion events or overly low extents of motion, such as if a patient does not exhibit any motion for a minimum time interval. An alert may also be linked to characteristics of the subject, such as other physiological parameters of the subject and / or predefined subject limitations. For example, an alert may be issued for selected patients that are not permitted to exit bed 130 (or otherwise exhibit severe movements). The alert may be communicated to an external monitoring system and / or at least one operator, such as a dedicated clinician at a facility, and an action may be undertaken responsive to the alert, such as to physically check in on monitored subject 120. A notification or alert may be associated with a priority level reflecting a degree of importance or urgency, where different notifications or alerts may be presented differently based on their priorities. For example, a first alert having a high urgency level (e.g., reflecting a posture change or other significant motion event of a subject 120 with severe health complications) may be issued as a high volume or high frequency audible sound and / or a visually intense graphical symbol, whereas a second alert having a low urgency level (e.g., reflecting a posture change or other significant motion event of a subject 120 with minimal health issues) may be issued as a low volume or low frequency audible sound and / or a graphical symbol with low intensity. More generally, system 110 may provide a report, such as via user interface 116, relating to a motion index profile and / or motion event determination of monitored subject 120. The report may include a visual representation of determined motion events, such as posture changes over a selected monitoring period, and optional supplementary information relating to subject 120. The report may include characteristics of determined motion events, such as: duration of a posture state (i.e., between consecutive posture changes), the frequency of posture changes during a given time interval, a reliability or confidence level of a posture change detection, and types of issued alerts during the time interval. The report may further include various statistics associated with the determined motion events, such as historical data obtained at previous dates and times, or motion events determined for similar types of beds or subjects. The information and statistics may be presented over a selected duration, such as depicting dynamic changes over time. For example, a report may be compiled presenting a total number of posture changes each night by a subject 120 over a given time period (e.g., several days, weeks, or months), which may provide insight into their behaviour and general wellbeing. Furthermore, notification unit 117 may issue a notification or alert based on deviations of posture changes or other motion events relative to statistical or historical data, which may provide early warning signs of changes in a condition of subject 120.

[0079] The method of Figure 5 is generally implemented in an iterative manner, such that at least some of the steps and sub-procedures are performed repeatedly, in order to provide for a dynamic determination of a motion index and posture changes of a monitored subject in real-time.

[0080] The disclosed embodiments can be used for various applications, ranging from home healthcare to medical diagnosis. For example, the disclosed system and method may be applied for monitoring patients to evaluate and improve treatment in an eldercare facility.

[0081] While certain embodiments of the disclosed subject matter have been described, so as to enable one of skill in the art to practice the disclosed subject matter, the preceding description is intended to be exemplary only. It should not be used to limit the scope of the disclosed subject matter, which should be determined by reference to the following claims.

Claims

CLAIMS1. A method for monitoring motion of a subject, the method comprising the steps of: receiving a millimeter-wave reflection radar signal from a monitored subject supported by a support surface; sampling the reflection radar signal and extracting a signal portion at a range of the monitored subject, the signal portion consisting of an in-phase (I) signal component and a quadrature (Q) signal component; deriving a temporal difference in-phase signal from the in-phase signal component, and a temporal difference quadrature signal from the quadrature signal component; deriving a summation signal by summing the temporal difference in-phase (I) signal and the temporal difference quadrature (Q) signal; applying adaptive thresholding to the summation signal to establish threshold boundaries for at least one adaptive threshold, for respective samples of the summation signal; deriving a motion index profile comprising motion index over a time window, by normalization of the summation signal according to each of the at least one adaptive threshold; and determining an extent of motion of the monitored subject based on the motion index profile.

2. The method of claim 1 , wherein determining an extent of motion comprises determining a severe motion event when the motion index exceeds a selected index threshold for severe motion.

3. The method of claim 2, wherein the severe motion event comprises a posture change.

4. The method of claim 2, wherein determining an extent of motion comprises determining a mild motion event when the motion index is above a lower limit of the motion index and below the selected index threshold for severe motion.

5. The method of claim 1 , wherein the adaptive threshold comprises: a first adaptive threshold reflective of severe motion, and a second adaptive threshold reflective of mild motion; and wherein the threshold boundaries comprises: a low mild motion threshold, a high mild motion threshold, a low severe motion threshold, and a high severe motion threshold.

6. The method of claim 5, wherein the step of deriving a motion index profile comprises the sub-procedures of: for each of the respective samples of the summation signal: deriving a first motion index score based on the first adaptive threshold; deriving a second motion index score based on the second adaptive threshold; and determining a motion index from a combination of the first motion index score and the second motion index score.

7. The method of claim 1 , further comprising determining a transient motion index based on a maximum or mean function of the motion index profile.

8. The method of claim 1 , further comprising the step of down-sampling the temporal difference l / Q signals before deriving the summation signal.

9. The method of claim 1 , wherein the adaptive thresholding comprises a cell-averaging constant false alarm rate (CFAR) process.

10. The method of claim 1 , further comprising the step of issuing a notification relating to a determined extent of motion.11 . The method of claim 1 , wherein the signal portion is sampled at a sampling rate of 500 Hz.

12. The method of claim 1 , wherein the time window is in the range of 5 to 30 seconds.

13. The method of claim 1 , wherein the radar signal is obtained using a remote non-invasive radar device comprising: at least one radar transmitter, configured to transmit a radar signal to a body tissue of the subject; and at least one radar receiver, configured to receive a reflection of the transmitted radar signal reflected from the body tissue of the subject.

14. A system for monitoring motion of a subject, the system comprising: a radar device, configured to receive a millimeter-wave reflection radar signal from a monitored subject supported by a support surface; and a processor, configured to: sample the reflection radar signal and extract a signal portion at a range of the monitored subject, the signal portion consisting of an in-phase (I) signal component and a quadrature (Q) signal component; derive a temporal difference in-phase signal from the in-phase signal component and a temporal difference quadrature signal from the quadrature signal component; derive a summation signal by summing the temporal difference in-phase signal and the temporal difference quadrature signal; apply adaptive thresholding to the summation signal to establish threshold boundaries for at least one adaptive threshold, for respective samples of the summation signal; derive a motion index profile comprising motion index over a time window, by normalization of the summation signal according to each of the at least one adaptive threshold; and determine an extent of motion of the monitored subject based on the motion index profile.

15. The system of claim 14, wherein the processor is configured to determine a severe motion event when the motion index exceeds a selected index threshold for severe motion.

16. The system of claim 15, wherein the severe motion event comprises a posture change.

17. The system of claim 15, wherein the processor is configured to determine a mild motion event when the motion index is above a lower limit of the motion index and below the selected index threshold for severe motion.

18. The system of claim 14, wherein the adaptive threshold comprises: a first adaptive threshold reflective of severe motion, and a second adaptive threshold reflective of mild motion; and wherein the threshold boundaries comprises: a low mild motion threshold, a high mild motion threshold, a low severe motion threshold, and a high severe motion threshold.

19. The system of claim 18, wherein the processor is configured to derive a motion index profile by, for each of the respective samples of the summation signal: deriving a first motion index score based on the first adaptive threshold; deriving a second motion index score based the second adaptive threshold; and determining a motion index from a combination of the first motion index score and the second motion index score.

20. The system of claim 1 , wherein the processor is configured to determine a transient motion index based on a maximum or mean function of the motion index profile.

21. The system of claim 14, wherein the processor is configured to apply a down-sampling of the temporal difference l / Q signals before deriving the summation signal.

22. The system of claim 14, wherein the adaptive thresholding comprises a cell-averaging constant false alarm rate (CFAR) process.

23. The system of claim 14, further comprising a notification unit, configured to issue a notification relating to a determined extent of motion.

24. The system of claim 14, wherein the signal portion is sampled at a sampling rate of 500 Hz.

25. The system of claim 14, wherein the selected time window is in the range of 5 to 30 seconds.

26. The system of claim 14, wherein the radar device comprises a remote non-invasive radar device comprising: at least one radar transmitter, configured to transmit a radar signal to a body tissue of the subject; and at least one radar receiver, configured to receive a reflection of the transmitted radar signal reflected from the body tissue of the subject.

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