Gait speed assessment with risk stratification

Abstract

A sensor system assesses gait speed and, based on the gait speed, stratifies risk into a risk level for a given subject. The given subject may be an older adult or an individual with a chronic disease. The sensor system includes a sensor module that is configured to use radar- based technology to collect data related to a walking subject. A signal processing unit may process the data to obtain the gait speed of the subject. The gait speed of the subject and / or the related risk level may be communicated in real-time using a numeric screen, color-coded visual display and / or auditory feedback.

Description

Gait Speed Assessment with Risk StratificationTECHNICAL FIELD

[0001] The present disclosure relates, generally, to assessment of older adults and individuals with chronic diseases and, in particular implementations, to gait speed assessment with risk stratification.BACKGROUND

[0002] Over the next 20 years, the number of people aged 65 and older is projected to increase by 68%, reaching 1.8 million people in Canada alone. As people age, mobility limitations increasingly threaten their independence and contribute to a risk of falls that increases with age. It may be shown that falls are not only a leading cause of injury but are also a significant driver of mortality and healthcare costs. Indeed, according to the Center for Disease Control and Prevention, every 18 seconds, an older adult is treated in the emergency room for a fall and every 35 minutes, an older adult dies as a result of fall-related injuries (see www.cdc.gov).

[0003] Gait speed is now recognized as a sixth vital sign (see Fritz S, Lusardi M.“Walking Speed: The Sixth Vital Sign” J Geriatr Phys Then 2009;32(2):46-9. Erratum in: J Geriatr Phys Ther. 2009;32(3):110. PMID: 20039582.). Gait speed may be shown to be just as important as the other five vital signs: temperature; heart rate; respiratory rate; blood pressure; and oxygen saturation. Unlike traditional vital signs, gait speed provides a dynamic assessment of functional health and overall physiological resilience of an individual.Research consistently demonstrates the predictive value of gait speed. It has been shown that community-dwelling adults with the highest gait speeds have a significantly lower relative risk (RR) of falls (RR = 0.23, 95% Confidence Interval, 0.11 to 0.5) than those communitydwelling adults with the lowest gait speeds (Montero-Odasso M, van der Velde N, Martin FC, Petrovic M, Tan MP, Ryg J, Aguilar-Navarro S, Alexander NB, Becker C, Blain H, Bourke R, Cameron ID, Camicioli R, Clemson L, Close J, Delbaere K, Duan L, Duque G, Dyer SM, Freiberger E, Ganz DA, Gomez F, Hausdorff JM, Hogan DB, Hunter SMW, Jauregui JR, Kamkar N, Kenny RA, Lamb SE, Latham NK, Lipsitz LA, Liu- Ambrose T, Logan P, Lord SR, Mallet L, Marsh D, Milisen K, Moctezuma-Gallegos R, Morris ME, Nieuwboer A, Perracini MR, Pieruccini-FariaF, Pighills A, Said C, Sejdic E, Sherrington C,Skelton DA, Dsouza S, Speechley M, Stark S, Todd C, Troen BR, van der Cammen T, Verghese J, Vlaeyen E, Watt JA, Masud T; Task Force on Global Guidelines for Falls in Older Adults. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2022 Sep 2;51(9):afac205. doi: 10.1093 / ageing / afac205.Erratum in: Age Ageing. 2023 Sep l;52(9):afadl88. doi: 10.1093 / ageing / afadl88. Erratum in: Age Ageing. 2023 Oct 2;52(10):afadl99. doi: 10.1093 / ageing / afadl99. PMID: 36178003; PMCID: PMC9523684.). A gait speed of less than 0.8 m / s may be shown to be highly predictive of falls and a change in gait speed of 0.1 m / s may be shown to be clinically significant (Adam CE, Fitzpatrick AL, Leary CS, Hajat A, Ilango SD, Park C, Phelan EA, Semmens EO. Change in gait speed and fall risk among community-dwelling older adults with and without mild cognitive impairment: a retrospective cohort analysis. BMC Geriatr.2023 May 25;23(1):328. doi: 10.1186 / sl2877-023-03890-6. PMID: 37231344; PMCID: PMC 10214622.). The 2024 JAMA Clinical Guidelines on Falls Prevention recommend routine gait speed assessment in clinical practice to predict falls (Grade 1A) (Leung PB, Alexander JT, Ouchida KE. Falls Prevention for Older Adults. JAMA. 2024 Apr 23;331(16): 1409-1410. doi: 10.1001 / jama.2023.26942. PMID: 38536162.). The recommendation for routine gait speed assessment may be understood to be based on an idea that gait speed provides important information that helps diagnose, monitor and manage various medical conditions. However, despite the proven value of routine gait speed assessment, it may be considered that gait speed measurement remains underutilized in clinical care. This underutilization may be due to competing priorities, equipment requirements and workflow challenges; however, the underutilization leaves an opening for proactive patient management for falls-prevention.SUMMARY

[0004] A sensor system assesses gait speed and, based on the gait speed, stratifies risk into a risk level for a given subject. The given subject may be an older adult or an individual with a chronic disease. The sensor system includes a sensor module that is configured to use radar-based technology to collect data related to a walking subject. A signal processing unit may process the data to obtain the gait speed of the subject. The gait speed of the subject and / or the related risk level may be communicated in real-time using a numeric screen, color-coded visual display and / or auditory feedback.

[0005] According to an aspect of the present disclosure, there is provided a sensor system. The sensor system includes a sensor module configured to use radar-based technology to collect data related to a walking subject and a signal processing unit configured to process the data to obtain a gait speed and select, based on the gait speed, a risk level from among a plurality of risk levels. The sensor system further includes a feedback system configured for communicating the selected risk level.

[0006] According to an aspect of the present disclosure, there is provided a method of stratifying mobility risk. The method includes receiving data obtained at a sensor module configured to use radar-based technology to collect data related to a walking subject, processing the data to obtain a gait speed, selecting, based on the gait speed, a risk level from among a plurality of risk levels and controlling a feedback system to communicate the selected risk level.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] For a more complete understanding of the present implementations, and the advantages thereof, reference is now made, by way of example, to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0008] FIG. 1 illustrates, in a schematic diagram, an environment for obtain a gait speed test using a sensor system, in accordance with aspects of the present disclosure;

[0009] FIG. 2 illustrates an example external appearance for the sensor system of FIG. 1, in accordance with aspects of the present disclosure;

[0010] FIG. 3 illustrates example internal components of the sensor system of FIG. 1, in accordance with aspects of the present disclosure;

[0011] FIG. 4 illustrates example steps in a method of performing a gait speed, in accordance with aspects of the present disclosure;

[0012] FIG. 5 illustrates an example external appearance for the sensor system of FIG. 1, in accordance with aspects of the present disclosure;

[0013] FIG. 6 illustrates an example external appearance for the sensor system of FIG. 1, in accordance with aspects of the present disclosure;

[0014] FIG. 7 illustrates an example external appearance for the sensor system of FIG. 1, in accordance with aspects of the present disclosure;

[0015] FIG. 8 illustrates an example external appearance for the sensor system, in accordance with aspects of the present disclosure;

[0016] FIG. 9 illustrates an example external appearance for the sensor system of FIG. 8, in accordance with aspects of the present disclosure;

[0017] FIG. 10 illustrates an example external appearance for the sensor system of FIG.8, in accordance with aspects of the present disclosure;

[0018] FIG. 11 illustrates an example external appearance for the sensor system of FIG.8, in accordance with aspects of the present disclosure;

[0019] FIG. 12 illustrates an example external appearance for the sensor system, in accordance with aspects of the present disclosure;

[0020] FIG. 13 illustrates the sensor system of FIG. 1 installed in a physiotherapy clinic setting, in accordance with aspects of the present disclosure;

[0021] FIG. 14 illustrates the sensor system of FIG. 1 installed in a physician clinic setting, in accordance with aspects of the present disclosure;

[0022] FIG. 15 illustrates the sensor system of FIG. 1 installed in a community center setting, in accordance with aspects of the present disclosure; and

[0023] FIG. 16 illustrates the sensor system of FIG. 1 installed in a hospital environment, in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0024] For illustrative purposes, specific example implementations will now be explained in greater detail in conjunction with the figures.

[0025] The implementations set forth herein represent information sufficient to practice the claimed subject matter and illustrate ways of practicing such subject matter. Upon reading the following description in light of the accompanying figures, those of skill in the art will understand the concepts of the claimed subject matter and will recognize applications of theseconcepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.

[0026] Moreover, it will be appreciated that any module, component or device disclosed herein that executes instructions may include, or otherwise have access to, a non-transitory computer / processor readable storage medium or media for storage of information, such as computer / processor readable instructions, data structures, program modules and / or other data. A non-exhaustive list of examples of non-transitory computer / processor readable storage media includes magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, optical disks such as compact disc read-only memory (CD-ROM), digital video discs or digital versatile discs (i.e., DVDs), Blu-ray Disc™ or other optical storage, volatile and non-volatile, removable and non-removable media implemented in any method or technology, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology. Any such non-transitory computer / processor storage media may be part of a device / apparatus or accessible or connectable thereto. Computer / processor readable / executable instructions to implement a method, an application or a module described herein may be stored or otherwise held by such non-transitory computer / processor readable storage media.

[0027] FIG. 1 includes representations of a subject 102 walking on a walking path 108 in the presence of a sensor system 100 for measuring a gait speed of the subject 102. The sensor system 100 may be optimized to monitor movement of the subject 102 along the walking path 108. The walking path 108 may, for example, have a length of three meters. The sensor system 100 may be particularly well-suited to providing relatively precise measurements of gait speed in busy clinic settings. The sensor system 100 is illustrated, in FIG. 1, as being positioned at one end of the walking path 108. The sensor system 100 may be positioned in such a way as to establish that the entire walking path 108 is within a field of view associated with the sensor system 100. Conveniently, placing the sensor system 100 in this way encourages accurate data collection without disrupting a natural gait pattern of the subject 102. The positioning of the sensor system 100 in this way is designed to support early detection of mobility issues in the subject 102 and assist in clinical assessments of walking ability of the subject 102.

[0028] FIG. 2 illustrates an example external appearance for the sensor system 100. A plurality of LED lights 210 is visible on the external surface of the sensor system 100.Additionally, a speaker aperture 220 is visible on the external surface of the sensor system 100.

[0029] FIG. 3 illustrates an example set of internal components for the sensor system 100. The internal components include a signal processing unit 302. Connected to the signal processing unit 302 are a memory 304, a feedback system 306, a radar unit 308 and a communications unit 310. The feedback system 306 includes a feedback controller 312 connected to the LEDs 210, familiar from FIG. 2, and also connected to a speaker 314. The speaker 314 may be representative of a component positioned behind the speaker aperture 220 of FIG. 2. The radar unit 308 may include an emitter module 316 and a sensor module 318. The emitter module 316 and the sensor module 318 may cooperate on the basis of so-called mm-wave radar, an example of which is a 60-64 GHz Frequency Modulated Continuous Wave (FMCW) radar. A sensor built into the sensor module 318 may have a 120-degree azimuth and elevation field of view. A push button 320 may be associated with the sensor system 100. In one implementation, the push button 320 may maintain a wired connection to the sensor system 100. In another implementation, the push button 320 may communicate with the sensor system 100 using a wireless communication protocol, such as, for only three examples, Bluetooth™, Wi-Fi® or Zigbee™. Indeed, the communications unit 310 may facilitate communication between the push button 320 and the sensor system 100.

[0030] Instead of, or in addition to, a hardware implementation of the push button 320, the push button 320 may be implemented in software. That is, the clinician may have access to an application executed on a computing device, such as a mobile phone, a tablet computer, a notebook computer and / or a desktop computer, etc. The application may present an implementation of the push button 320.

[0031] Components of the sensor system 100 may operate in a manner that is similar to the manner of operation of components of similar systems disclosed in US Patent Application Publications 2023 / 0016640A1 and 2023 / 0013814A1, which are both incorporated herein by reference.

[0032] Aspects of the present disclosure relate to using the sensor system 100 for measuring gait speed. The sensor system 100 may determine a value for the so-called “sixthvital sign” - gait speed - within immediately after collecting relevant data. The sensor system 100 may be considered to be compact, user-friendly and privacy-preserving. In operation, the sensor system 100 adheres to a clinic wall, thereby being non-invasively integrated into a given healthcare environment. The sensor system 100 may, in an example implementation, collect data as a patient (a “subject”) walks to an assessment room to take part in visit with a clinician. Conveniently, the data collection adds no additional time to the visit. Unlike known gait assessment methods, there is no need for clinical staff to use wearable technology or to perform manual calculations. Subsequent to the collection of the data, the data may be processed to obtain a gait speed and the gait speed may be communicated to one or both of the clinician and the patient. The communication may take the form of a display and / or a speaker. The display may employ color-coded LED lights that may be understood to provide a type of feedback that may guide interventions prescribed by healthcare providers.

[0033] In operation, the emitter module 316 of the radar unit 308 emits high-frequency electromagnetic waves. It is expected that the high-frequency electromagnetic waves will reflect off the subject 102 on the walking path 108 (see FIG. 1). The sensor module 318 of the radar unit 308 receives some of the reflected electromagnetic waves and converts the received electromagnetic waves into data about the received electromagnetic waves. The data may include data regarding time of receipt of the electromagnetic waves and amplitude, frequency and phase characterizations of the received electromagnetic waves. The radar unit 308 may provide the data to the signal processing unit 302. The signal processing unit 302 may then process the data to obtain a gait speed.

[0034] There are, of course, many radar sensor brands and models available from which to select a particular model for the sensor module 318. Experimentally, it has been found that a radar sensor suitable for the present application is a radar sensor that may provide reliable regarding a subject that is positioned up to several meters away. Such a radar sensor has been found to provide relatively accurate readings in various environments, including confined spaces, such as hallways or small rooms.

[0035] Ambient radar sensors (not shown as part of the sensor module 318) may be precalibrated for general use. Notably, the ambient radar sensors may be re-calibrated and / or adjusted to suit specific clinician needs or specific environments. The sensor system 100 may be shown to implement aspects of the present application by executing, say, at the signal processing unit 302, software instructions representative of the method illustrated in FIG. 4.Periodically, as is common, there may be reason to update the software on the sensor system 100. The communications unit 310 may, for example, facilitate communication that allows the sensor system 100 to receive and install a software update. Subsequent to installing a software update, the ambient radar sensors may be re-calibrated and / or adjusted.

[0036] Conveniently, the sensor system 100 may be shown to be relatively easy to set up and may be placed in any room or hallway to monitor gait speed of any subject, with no calibration needed and operation commenced responsive to activation of the push button 320. The color-coded features of the visual aspect of the feedback system 306 may be considered to provide clear, accessible information with a numeric screen and LED lights. Similarly, the audio cue features of the audible aspect of the feedback system 306 may be considered to provide clear, accessible information with a volume control.

[0037] FIG. 4 illustrates example steps in a method, carried out at the sensor system 100, for measuring gait speed and providing real-time feedback. The sensor system 100 may be specifically tailored for busy clinical settings. The process begins with the clinician pressing the physical push button 320 to activate the sensor system 100. It follows that the signal processing unit 302 may determine (step 405) that an activation command has been received. The sensor system 100 may have a passive mode of operation, in which mode the sensor system 100 passively monitors all individuals in an environment. The sensor system 100 may also have an active mode of operation, in which mode the sensor system 100 is primed only for patients undergoing assessment. The active mode of operation may also be called a “Test Ready” state.

[0038] Responsive to receiving (step 405) the activation command, the signal processing unit 302 may switch (step 410) the sensor system 100 from the passive mode of operation to the active mode of operation.

[0039] As the sensor system 100 switches (step 410) the sensor system 100 to the active mode of operation, the signal processing unit 302 may actively prime a plurality of sensors and, in general, prepare for data collection. Indeed, the signal processing unit 302 may transmit a command to the radar unit 308 to prime the sensor module 318. The signal processing unit 302 may next determine (step 415) whether walking actions, of the subject 102, have been detected along the designated path 108. The determining (step 415) of thewalking actions may also be referenced as a step of collecting data from the sensor module 318.

[0040] Subsequent to collecting data (step 415) that is indicative of walking detection, the signal processing unit 302 may process, using various algorithms, the collected data to attempt (step 420) to determine a gait speed (see, for example, H. Abedi, J. Boger, P. P. Morita, A. Wong and G. Shaker, “Hallway Gait Monitoring Using Novel Radar Signal Processing and Unsupervised Learning,” in IEEE Sensors Journal, vol. 22, no. 15, pp. 15133-15145, 1 August 2022). The signal processing unit 302 may then determine (step 422) whether the attempt (step 420) to determine a gait speed was successful. As part of the attempt (step 420) to determine a gait speed, the signal processing unit 302 may filter out any interference caused by environmental noise, thereby establishing relatively accurate readings, even in challenging settings.

[0041] Upon determining (step 422) that the attempt (step 420) to determine a gait speed was unsuccessful, the signal processing unit 302 may provide (step 423) feedback indicative of a failure to detect a gait speed. The method may then be considered to be complete and the signal processing unit 302 may switch (step 435) back to the passive operational mode to await receipt (step 405) of another activation command, received responsive to another activation of the push button 320. Responsive to noting the feedback indicative of a failure to detect a gait speed, the clinician that initiated the test with the press on the push button 320 may ask the subject 102 to redo the test.

[0042] Upon determining (step 422) that the attempt (step 420) to determine a gait speed was successful, the signal processing unit 302 may perform (step 425) a real-time risk stratification. As part of the performing (step 425) the real-time risk stratification, the signal processing unit 302 may categorize the gait speed into one risk level among a plurality of risk levels, such as low risk, moderate risk and high risk. That is, the signal processing unit 302 may select (step 425) one risk level, among the plurality of risk levels, to be representative of the gait speed determined in step 420. The selected risk level may be subject to clinical interpretation.

[0043] Upon selecting (step 425) the risk level representative of the gait speed determined in step 420, the signal processing unit 302 may transmit (step 430) a command to control the feedback system 306 to provide feedback. That is, the signal processing unit 302may control (step 430) the feedback system 306 to provide feedback indicative of the gait speed determined in step 420. The signal processing unit 302 may also control (step 430) the feedback system 306 to provide feedback indicative of the risk level selected in step 425.

[0044] The feedback system 306 may provide feedback visually and / or auditorily.

[0045] In some visual feedback implementations, the feedback system 306 may activate (i.e., turn on) a single LED light 210 among the plurality of LED lights 210. The single, activated LED light 210 may be representative of a particular range of gait speeds, wherein the particular range includes the gait speed determined in step 420. For example, in FIG. 2, the sensor system 100 displays a single activated LED light 210 that is representative of a range of gait speeds less than 0.4 meters per second (m / s).

[0046] In some visual feedback implementations, the feedback system 306 may activate (i.e., turn on) the LED light 210 representative of the particular range of gait speeds along with all the LED lights 210 representative of ranges of gait speeds slower than the particular range. For one example, illustrated in FIG. 5, the sensor system 100 displays an activated LED light 210 that is representative of a range of gait speeds around 0.7 m / s. Additionally, the sensor system 100 displays an activated LED light 210 that is representative of a range of gait speeds around 0.6 m / s and an activated LED light 210 that is representative of a range of gait speeds less than 0.4 m / s.

[0047] For another example, illustrated in FIG. 6, the sensor system 100 displays an activated LED light 210 that is representative of a range of gait speeds around 1.1 m / s. Additionally, the sensor system 100 displays six activated LED lights 210, one activated LED light 210 for each of the ranges of gait speeds less than 1.1 m / s.

[0048] For another example, illustrated in FIG. 7, the sensor system 100 displays an activated LED light 210 that is representative of a range of gait speeds greater than 1.2 m / s. Additionally, the sensor system 100 displays seven activated LED lights 210, one activated LED light 210 for each of the ranges of gait speeds less than 1.2 m / s.

[0049] In some aspects of the present disclosure, the plurality of LED lights 210 are all the same color. In this instance, interpreting the result of the test involves the clinician noting the range of gait speeds associated with the activated LED light 210 or the highest range of gait speeds associated with the activated LED light 210.

[0050] In other aspects of the present disclosure, the plurality of LED lights 210 may be illuminated in various colors. Relatively faster gait speeds may, for example, be associated with a green LED light 210. Relatively slower gait speeds may, for example, be associated with a red LED light 210. Intermediate gait speeds, between the relatively faster gait speeds and the relatively slower gait speeds, may, for example, be associated with a yellow LED light 210 or an orange LED light 210. The aspect that includes multiple colors for the LED lights 210 may be shown to speed up interpretation of the result of the test relative to the aspects wherein all LED lights 210 are the same color. Conveniently, multiple colors for the LED lights 210 may be shown to enable relatively quick clinical decision-making.

[0051] In those aspects of the present disclosure wherein the plurality of LED lights 210 may be illuminated in various colors, all of LED lights 210 may be illuminated in a single color to indicated the selected risk level. For example, in FIG. 2, the single LED light 210 may be illuminated in red to indicate that the risk level selected in step 425 is a high risk level. For example, in FIG. 5, the LED lights 210 may be illuminated in orange to indicate that the risk level selected in step 425 is a moderately high risk level. For example, in FIG. 6, the LED lights 210 may be illuminated in yellow to indicate that the risk level selected in step 425 is a moderately low risk level. For example, in FIG. 7, the LED lights 210 may be illuminated in green to indicate that the risk level selected in step 425 is a low risk level.

[0052] FIG. 8 illustrates a sensor system 100A with an external appearance that is an alternative to the external appearance of the sensor system 100 of FIG. 2. Instead of the LEDs 210 being available to view on the front surface, as illustrated the sensor system 100 of FIGS.2, 5, 6 and 7, the sensor system 100A of FIG. 8 has a display 810.

[0053] In some visual feedback implementations, the indicating may involve the feedback controller 312 activating the display 810 to provide a numerical representation of the gait speed determined in step 420. There are many possible implementations for the display, including a liquid crystal display, an alphanumeric LED display and a dial with a mechanical arm controlled, by the feedback controller 312, to point at a gait speed on the dial. In the example illustrated in FIG. 8, the display 810 indicates a gait speed of 0.4 m / s. In the example illustrated in FIG. 9, the display 810 indicates a gait speed of 0.7 m / s. In the example illustrated in FIG. 10, the display 810 indicates a gait speed of 1.1 m / s. In the example illustrated in FIG. 11, the display 810 indicates a gait speed of 1.2 m / s.

[0054] FIG. 12 illustrates a sensor system 100B with an external appearance that is an alternative to the external appearance of the sensor system 100 of FIG. 2 and to the external appearance of the sensor system 100A of FIG. 8. Instead of the LEDs 210 or the display 810, the sensor system 100B of FIG. 12 has no on-device visual feedback mechanism. Instead, the sensor system 100B of FIG. 12 is associated with an off-device display 1210. In the example illustrated in FIG. 12, the off-device display 1210 indicates a gait speed of 1.2 m / s. In visual feedback implementations associated with the arrangement illustrated in FIG. 12, the indicating may involve the feedback controller 312 communicating with the off-device display 1210 to provide a numerical representation of the gait speed determined in step 420.

[0055] The feedback controller 312 may, for example, employ the communications unit 310 to communicate wirelessly with the off-device display 1210, say, through use of Bluetooth™ technology. Of course, there are many other options for wireless communication protocols for use by the communications unit 310 and the off-device display 1210. Indeed, a wired connection may be provided between the sensor system 100B and the off-device display 1210. Notably, the off-device display 1210 may be located at some distance from the sensor system 100B. For example, the off-device display 1210 may be located in the office of the clinician, perhaps in proximity to, or in combination with, the push button 320. It has been discussed, hereinbefore, that the push button 320 may be implemented in software. It follows that the off-device display 1210 may also be implemented in software as a component of an application. That is, the component of the application may be used to indicate, to the clinician or other application user, the gait speed determined in step 420. A further component of the application may be used to indicate, to the clinician or other application user, the risk level selected in step 425.

[0056] Notably, the sensor system 100A of FIGS. 8, 9, 10 and 11 and the sensor system 100B of FIG. 12 may not entirely do away with the LEDs 210 in favor of one of the displays 810, 1201. Instead of the LEDs 210 being positioned on the anterior surface (the face) of the sensor system, a plurality of LEDs may be positioned on a posterior surface (not shown) of the sensor system.

[0057] The LEDs positioned on the posterior surface may be illuminated in various colors to signal risk stratification. The LEDs positioned on the posterior surface may be illuminated in red to indicate that the risk level selected in step 425 is a high risk level. The LEDs positioned on the posterior surface may be illuminated in orange to indicate that therisk level selected in step 425 is a moderately high risk level. The LEDs positioned on the posterior surface may be illuminated in yellow to indicate that the risk level selected in step 425 is a moderately low risk level. The LEDs positioned on the posterior surface may be illuminated in green to indicate that the risk level selected in step 425 is a low risk level. In each case, in conjunction with presenting, on the display 810, 1210, an indication of a gait speed, the illuminated LEDs positioned on the posterior surface may provide the sensor system 100A, 100B with a halo of light reflecting off the wall behind the sensor system 100 A, 100B. Notably, the halo of light may surround the entire perimeter of the sensor system 100A, 100B or only a portion of the perimeter of the sensor system 100 A, 100B. The partial halo is illustrated, in FIGS. 8, 9, 10 and 11, as arched lines over the sensor system 100A and is illustrated, FIG. 12, as arched lines over the sensor system 100B.

[0058] It is further contemplated that LEDs may be used to illuminate a background of the display 810, 1210 with a color representative of the risk level selected in step 425.

[0059] In some auditory feedback implementations, the indicating may involve the feedback controller 312 activating the speaker 314, which may be associated with an amplifier (not shown) and a volume control mechanism (not shown). In particular, the feedback controller 312 may synthesize a voice to speak an indication of the gait speed. It should be clear that the synthesized voice may speak the gait speed in any one of the languages spoken around the world, with only a complexity of voice synthesis software acting as a limitation.

[0060] Subsequent to the indicating of the feedback, the method may be considered to be complete and the signal processing unit 302 may switch (step 435) back to the passive operational mode to await receipt (step 405) of another activation command. Alternatively, the signal processing unit 302 may simply turn off the sensor system 100. The act of turning off the sensor system 100 may have advantages in that resources are conserved.

[0061] FIGS. 8, 9, 10 and 11 illustrate the sensor system 100 installed in various settings. In particular, FIG. 13 illustrates the sensor system 100 installed in a physiotherapy clinic setting. FIG. 14 illustrates the sensor system 100 installed in a physician clinic setting. FIG.15 illustrates the sensor system 100 installed in a community center setting. FIG. 16 illustrates the sensor system 100 installed in a hospital environment.

[0062] The sensor system 100 may be understood to leverage ambient radar sensors (not shown as part of the sensor module 318) to obtain relatively accurate data. The sensor system 100 may be understood to process the obtained data to obtain a gait speed measurement in a manner that involves minimal disruption to workflow in busy clinical settings. By focusing on targeted gait speed assessments, the sensor system 100 may allow healthcare professionals to identify at-risk individuals without comprehensively evaluating a plurality of patients.

[0063] In the physiotherapy clinic setting (FIG. 13) and the physician clinic setting (FIG.14), the sensor system 100 may be optimized for quick setup and efficient operation, capturing gait speed as an indicator of overall health and mobility. Quick setup and efficient operation may be considered to be especially valuable in high-demand environments, where time constraints often limit comprehensive mobility assessments. In the community center setting (FIG. 15), the sensor system 100 may be shown to facilitate early detection of gait abnormalities, thereby promoting timely interventions that may act to prevent functional decline and reduce fall risk.

[0064] The importance of targeted gait speed measurements lies in the role of gait speed as a vital sign indicative of mobility and frailty. Accurate gait speed estimation allows clinicians to stratify risk, monitor progress and tailor rehabilitation programs effectively, thereby enhancing patient outcomes while optimizing resource utilization. The sensor system 100 may be shown to bridge a gap between clinical precision and practical usability, thereby making gait speed assessment accessible and impactful in diverse healthcare and community contexts.

[0065] Features of the sensor system 100 may be shown to work together to enable the subject, and a provider of healthcare to the subject, to quickly and clearly interpret the realtime feedback. The real-time feedback allows the healthcare provider to make immediate, informed decisions regarding the mobility and the overall health of the subject. By identifying potential issues early, healthcare providers may develop and implement tailored interventions. Additionally, the sensor system 100 allows patients to self-monitor and, accordingly, track changes in their own gait speed over time. Such self-monitoring may act as a source of motivation for patient self-improvement.

[0066] The sensor system 100 may include mounting hardware (not shown) that allows the sensor system 100 to be relatively easily moved from one location to another location.The mounting hardware may, optionally, include a hinge for optimization of a field of view of the sensor system 100. Conveniently, the sensor system 100 may be shown to maintain a reliability and a validity that is independent of location.

[0067] Further conveniently, the gait-speed measurements resulting from the processing (step 420) of the data obtained at the sensor module 318 exist completely without reference to an identity of the subject. Neither image capture nor video capture is involved in the gaitspeed measurement. That is, the sensor system 100 may be considered to preserve privacy.

[0068] Beyond fall prevention, the sensor system 100 may be used for early detection of changes in mobility. The sensor system 100 may be shown to allow for early detection, through detected changes in gait speed, of other health conditions including, but not limited to, dementia, cardiovascular disease and / or other neurodegenerative diseases. This risk stratification may be shown to allow for rapid intervention, thereby helping the subject and a caregiver of the subject for early diagnosis.

[0069] The sensor system 100 may be considered to be scalable and adaptable for underserved and rural areas, thereby ensuring access to advanced healthcare technology where traditional gait-measuring tools may be inaccessible.

[0070] The sensor system 100 may be considered to offer a relatively simple, user-friendly system that may be easily set up in a variety of environments.

[0071] In contrast to a system that merely provides raw gait speed data, the sensor system 100 may be considered to actively stratify a measured gait speed as falling into a distinct risk level selected from a plurality of risk levels. The risk level aspect of the feedback may be shown to provide immediate, actionable information. This risk stratification may be shown to allow for rapid intervention, thereby helping the subject and a caregiver of the subject to address any mobility concerns and other health conditions without delay.

[0072] While other gait speed devices may include basic visual displays, the sensor system 100 may employ a dual-feedback system, using both color-coded LED lights 210 and auditory cues, spoken using the speaker 314, to communicate risk levels. This combination enhances the accessibility of the sensor system 100.

[0073] It may be shown that the sensor system 100 may be easily set up in a wide variety of settings, from a home to a healthcare facility, thereby making the sensor system 100 an accessible option for caregivers and clinicians alike. The sensor system 100 does not require complex installation or calibration, thereby establishing that the sensor system 100 may be deployed quickly and efficiently.

[0074] It should be appreciated that one or more steps of the implementation methods provided herein may be performed by corresponding units or modules. For example, data may be transmitted by a transmitting unit or a transmitting module. Data may be received by a receiving unit or a receiving module. Data may be processed by a processing unit or a processing module. The respective units / modules may be hardware, software, or a combination thereof. For instance, one or more of the units / modules may be an integrated circuit, such as field programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). It will be appreciated that where the modules are software, they may be retrieved by a processor, in whole or part as needed, individually or together for processing, in single or multiple instances as required, and that the modules themselves may include instructions for further deployment and instantiation.

[0075] Although a combination of features is shown in the illustrated implementations, not all of them need to be combined to realize the benefits of various implementations of this disclosure. In other words, a system or method designed according to an implementation of this disclosure will not necessarily include all of the features shown in any one of the Figures or all of the portions schematically shown in the Figures. Moreover, selected features of one example implementation may be combined with selected features of other example implementations.

[0076] Although this disclosure has been described with reference to illustrative implementations, this description is not intended to be construed in a limiting sense. Various modifications and combinations of the illustrative implementations, as well as other implementations of the disclosure, will be apparent to persons skilled in the art upon reference to the description. It is therefore intended that the appended claims encompass any such modifications or implementations.

[0077] In the present disclosure, the terms “a” and “an” are defined to mean “at least one.” That is, these terms do not exclude a plural number of items, unless stated otherwise.

[0078] In the present disclosure, terms such as “substantially,” “generally” and “about,” which modify a value, condition or characteristic of a feature of an example implementation, should be understood to mean that the value, condition or characteristic is defined within tolerances that are acceptable for the proper operation of the example implementation for its intended application.

[0079] In the present disclosure, unless stated otherwise, the terms “connected” and “coupled,” and derivatives and variants thereof, refer herein to any structural or functional connection or coupling, either direct or indirect, between two or more elements. For example, the connection or coupling between the elements can be acoustical, mechanical, optical, electrical, thermal, logical or any combinations thereof.

[0080] In the present disclosure, expressions such as “match,” “matching” and “matched,” including variants and derivatives thereof, are intended to refer herein to a condition in which two or more elements are either the same or within some predetermined tolerance of each other. That is, these terms are meant to encompass not only “exactly” or “identically” matching the two elements but also “substantially,” “approximately” or “subjectively” matching the two or more elements, as well as providing a higher or best match among a plurality of matching possibilities.

[0081] In the present disclosure, the expression “based on” is intended to mean “based at least partly on.” That is, this expression can mean “based solely on” or “based partially on” and, so, should not be interpreted in a limited manner. More particularly, the expression “based on” could also be understood as meaning “depending on,” “representative of,” “indicative of,” “associated with” or similar expressions.

[0082] In the present disclosure, the terms “system” and “network” may be used interchangeably in different implementations of this disclosure. “At least one” means one or more and “a plurality of’ means two or more. The term “and / or” describes an association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may indicate the following three cases: only A exists; both A and B exist; and only B exists; where A and B may be singular or plural. The character “ / ” indicates an “or” relationship between associated objects. “At least one of the following items (pieces)” or a similar expression thereof indicates any combination of these items, including a single item (piece) or any combination of a plurality of items (pieces). For example, “at least one ofA, B, or C” includes: only A; only B; only C; A and B; A and C; B and C; or A, B, and C. “at least one of A, B, and C” may also be understood as including: only A; only B; only C; A and B; A and C; B and C; or A, B, and C. In addition, unless otherwise specified, ordinal numbers such as “first” and “second” in implementations of this disclosure are used to distinguish between a plurality of objects and are not used to limit a sequence, a time sequence, priorities, or importance of the plurality of objects.

[0083] A person skilled in the art should understand that implementations of this disclosure may be provided as a method, an apparatus (or system), computer-readable storage medium, or a computer program product. Therefore, this disclosure may use a form of a hardware-only implementation, a software-only implementation, or an implementation with a combination of software and hardware. Moreover, this disclosure may use a form of a computer program product that is implemented on one or more computer-usable storage media (including but not limited to a disk memory, an optical memory, and the like) that include computer-usable program code.

[0084] This disclosure is described with reference to the flowcharts and / or block diagrams of the method, the device (system), and the computer program product according to this disclosure. It should be understood that computer program instructions may be used to implement each process and / or each block in the flowcharts and / or the block diagrams and a combination of a process and / or a block in the flowcharts and / or the block diagrams. The computer program instructions may be provided for a general-purpose computer, a dedicated computer, an embedded processor, or a processor of another programmable data processing device and enable a machine to execute the instructions. When executed by any computer or the processor of a programmable data processing device, the instructions cause the apparatus to implement specific functions as described in one or more procedures in the flowcharts and / or one or more blocks in the block diagrams. The computer program instructions may alternatively be stored in a computer-readable memory that can indicate a computer or another programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate an artifact that includes an instruction apparatus. The instruction apparatus implements a specific function in one or more procedures in the flowcharts and / or one or more blocks in the block diagrams.

[0085] The computer program instructions may alternatively be loaded onto a computer or another programmable data processing device, so that a series of operations and steps areperformed on the computer or the another programmable device, so that computer-implemented processing is generated. Therefore, the instructions executed on the computer or on another programmable device provide steps for implementing specific functions as described in one or more procedures in the flowcharts and / or in one or more blocks in the block diagrams.

[0086] It is clear that a person skilled in the art can make various modifications and variations to this disclosure without departing from the scope of this disclosure. This disclosure is intended to cover these modifications and variations of this disclosure provided that they fall within the scope of protection defined by the following claims and their equivalent technologies.

Claims

CLAIMS1. A sensor system comprising:a sensor module configured to use radar-based technology to collect data related to a walking subject;a signal processing unit configured to:process the data to obtain a gait speed;select, based on the gait speed, a risk level from among a plurality of risk levels; anda feedback system configured for communicating the selected risk level at the sensor system.

2. The system of claim 1, further comprising, as part of the feedback system, a plurality of light-emitting diodes (LEDs), wherein the communicating the selected risk level comprises activating a particular LED among the plurality of LEDs, wherein the particular LED is associated with a range of gait speeds that includes the gait speed.

3. The system of claim 2, wherein the communicating the selected risk level comprises activating the particular LED to illuminate with a particular color selected from among a plurality of colors, wherein the particular color is associated with the selected risk level.

4. The system of claim 1, further comprising, as part of the feedback system, a visual numeric display, wherein the communicating the selected risk level comprises activating the visual numeric to display the gait speed.

5. The system of claim 1, further comprising, as part of the feedback system, a speaker, wherein the communicating the selected risk level comprises activating the speaker to speak the gait speed.

6. The system of claim 5, further comprising an amplifier associated with the speaker, the amplifier associated with a volume control.

7. The system of claim 1, wherein the signal processing unit is configured to select the risk level based on comparing the gait speed to a predefined threshold.

8. The system of claim 1, wherein the feedback system provides color-coded visual indicators to denote low, medium or high-risk gait speeds, respectively.

9. The system of claim 1, wherein the feedback system provides real-time feedback with numeric value displayed on a screen.

10. The system of claim 1, wherein the auditory feedback includes spoken cues indicating the risk level.

11. The system of claim 1, wherein the plurality of risk levels includes a low risk level.

12. The system of claim 1, wherein the plurality of risk levels includes a moderate risk level.

13. The system of claim 1, wherein the plurality of risk levels includes a high-risk level.

14. The system of claim 1, wherein data collected by the sensor module relates to electromagnetic energy reflected by the walking subject.

15. The system of claim 14, further comprising a radar emitter arranged to emit the electromagnetic energy that is reflected by the walking subject.

16. A method of stratifying mobility risk, the method comprising:receiving data obtained at a sensor module configured to use radar-based technology to collect data related to a walking subject;processing the data to obtain a gait speed;selecting, based on the gait speed, a risk level from among a plurality of risk levels; andcontrolling a feedback system to communicate the selected risk level.

17. The method of claim 16, further comprising controlling the feedback system to communicate the gait speed.

18. The method of claim 16, further comprising, before the receiving, receiving an activation command and switching from a passive mode of operation to an active mode of operation,wherein the processing, selecting and controlling are configured to be carried out in the active mode of operation.

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