Wearable ultrasound shear-wave elastometry devices and methods

Abstract

Ultrasound shear-wave elastography is a non-invasive method that characterizes tissue mechanical properties by analyzing the propagation of shear waves, but conventional ultrasound imaging probes are bulky and rigid, limiting their use beyond clinical environments. A wearable ultrasound shear-wave elastometry device and methods are provided, comprising a miniaturized actuator and a pair of ultrasound transducers that are mounted to the body. The device generates and detects pulsed shear waves within tissue to estimate shear-wave velocity and mechanical properties of tissues with a temporal resolution of the supplied shear wave pulse repetition interval. Multi-frequency pulse excitation enables shear wave velocity dispersion analysis and viscoelastic characterization of tissues. Correction methods for surface acoustic wave motion artifacts are provided to mitigate surface acoustic wave interference in the detected shear waves within the tissues. The developed device and methods enable continuous, time-resolved monitoring of tissue mechanical properties in a wearable form factor for application during active body motion outside clinical environments.

Description

WEARABLE ULTRASOUND SHEAR- WAVE ELASTOMETRY DEVICES ANDMETHODSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application claims the benefit of priority to U.S. Provisional Patent Application 63 / 691,169 filed September 5, 2024, the entire contents of which are incorporated herein by reference.FIELD OF THE INVENTION

[0002] This patent application relates to wearable devices and more particularly to wearable ultrasound devices and methods exploiting ultrasound shear- wave elastometry.BACKGROUND OF THE INVENTION

[0003] Ultrasound shear wave elastography (US-SWE) is an emerging technology that characterizes tissue mechanical properties by generating and analyzing shear waves. The propagation speed and attenuation of these shear waves are used, in conjunction with physical models, to estimate tissue mechanical properties. US-SWE is a non-invasive method for characterizing the mechanical properties of biological soft tissues, such as skeletal muscles, tendons, and ligaments, providing indicators of various physiological states including force production, fatigue level, injury, and response to treatment and rehabilitation. US-SWE and particularly continuous tissue monitoring is thus a valuable method for clinical, sports medicine, and general monitoring applications, where, for example, monitoring muscle tissues during movements can indicate their functional state.

[0004] However, conventional ultrasound imaging probes and systems are bulky and rigid which limit their application in many scenarios outside of a clinic or a hospital, where smaller portable devices would be beneficial, such as in the continuous monitoring of muscle tissue during dynamic movements. Accordingly, it would be beneficial to provide clinicians, physicians, therapists, patients and other individuals with wearable ultrasound devices that facilitate ease of use, and enable widespread utilization in settings such as, for example, ongoing monitoring during daily activities, exercises, or therapy.

[0005] Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.SUMMARY OF THE INVENTION

[0006] It is an object of the present invention to mitigate limitations within the prior art relating to wearable devices and more particularly to wearable ultrasound devices and methods exploiting ultrasound shear-wave elastometry.

[0007] It is an object of the present invention to provide a wearable ultrasound device comprising a minimal sensing configuration and methods for shear-wave elastometry that is less complex than prior art and thus amenable to miniaturization and cost reduction, suitable for long-term monitoring, and capable of providing time-resolved measurements of tissue mechanical properties.

[0008] In accordance with an embodiment of the invention there is provided a wearable device comprising: an actuator element coupled to a region of a body, the actuator element generating shear-waves which induce tissue displacements within the region of the body; a pair of ultrasound elements coupled to another region of the body, each for transmitting an ultrasound signal and for receiving a reflected portion of the ultrasound signal where the reflection portion of the ultrasound signal is modulated by tissue displacements induced by the shear-waves generated by the actuator element within a defined depth range within the other region of the body; and means to mount each of the actuator element to the region of the body and the pair of ultrasound elements to the other region of the body; wherein the pair of ultrasound elements generate electrical outputs in dependence upon the reflected portion of the ultrasound signal; and the generated electrical outputs are processed to establish a shear-wave velocity of the shearwaves and tissue mechanical properties at one or more defined depths within the defined depth range of the other region of the body.

[0009] In accordance with an embodiment of the invention there is provided a method comprising: providing an actuator element coupled to a region of a body, the actuator element generating shear-waves which induce tissue displacements within the region of the body;providing a pair of ultrasound elements coupled to another region of the body, each for transmitting an ultrasound signal and for receiving a reflected portion of the ultrasound signal where the reflection portion of the ultrasound signal is modulated by tissue displacements induced by the shear-waves generated by the actuator element within a defined depth range within the other region of the body; and providing means to mount each of the actuator element to the region of the body and the pair of ultrasound elements to the other region of the body; wherein the pair of ultrasound elements generate electrical outputs in dependence upon displacements induced by the tone-burst shear- waves; and the generated electrical outputs are processed to establish a shear-wave velocity of the shearwaves and tissue mechanical properties at the defined depth within the other region of the body.

[0010] In accordance with an embodiment of the invention there is provided a method comprising: providing an actuator element coupled to a region of a body, the actuator element excited by a multi-frequency pulse (MFP) and generating shear waves within the region of the body; providing a pair of ultrasound elements coupled to another region of the body, each for transmitting an ultrasound signal and for receiving a reflected portion of the ultrasound signal where the reflection portion of the ultrasound signal is modulated by tissue displacements induced by the shear-waves generated by the actuator element; and processing the generated electrical outputs from the pair of ultrasound elements to establish a shear-wave velocity dispersion curve of the shear-waves at a defined depth within the defined range of the other region of the body in dependence upon the phase shift of the shear wave frequency components generated by the MFP applied to the actuator element.

[0011] In accordance with an embodiment there is provided a method comprising: transmitting shear waves within a tissue region of a body where the shear waves produce tissue displacements within a tissue region of another region of the body; providing a set of ultrasound elements which transmit ultrasound pulses into the tissue region within which the shear waves are propagating; the ultrasound pulses are reflected from the tissue boundaries and / or scatterers which change their depth location with time due to the tissue displacements induced by the shearwave propagation in the region of interest;the ultrasound elements receive ultrasound pulses reflected from the tissue boundaries and / or scatterers which change their depth location with time due to the tissue displacements induced by the shear-wave propagation within the tissue region of another region of the body and generate electrical outputs in dependence upon the displacements induced by the shear- waves.

[0012] In accordance with an embodiment there is provided a method comprising: generating a series of periodic shear wave pulses within the tissue region, the interval between successive pulses defining the temporal resolution of the measurements; and processing the electrical outputs from the ultrasound elements corresponding to the periodic shear wave pulses to estimate time-resolved shear wave velocity of the shear-waves and mechanical properties of the tissue within the region of interest.

[0013] Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Embodiments of the present invention will now be described, by way of example only, with reference to the attached Figures, wherein:

[0015] Figure 1 depicts an exemplary device supporting embodiments of the invention or coupled to devices supporting embodiments of the invention in communication to a network and remote servers and systems;

[0016] Figure 2 depicts the shear wave velocity (SWV) measurement principle and concept of continuous muscle monitoring by the proposed wearable ultrasound shear-wave elastometry (wUS-SWEM) device according to an embodiment of the invention;

[0017] Figure 3 depicts a wearable ultrasonic sensor (WUS) structure containing two unfocused ultrasound sensing elements for estimating SWV within a wUS-SWEM device according to an embodiment of the invention;

[0018] Figure 4 depicts photographs of a wUS-SWEM device according to an embodiment of the invention showing the sensing side of the wUS-SWEM device which contacts the skin surface and an in vivo experiment setup with wUS-SWEM device attached on the upper arm of a user;

[0019] Figure 5A depicts a block diagram of data acquisition system for the in vivo experiments using the wUS-SWEM device according to an embodiment of the invention;

[0020] Figure 5B depicts measurements of an in-vivo experiment for establishing a motion correction in the measurements due to surface acoustic waves induced motions from the actuator within the wUS-SWEM device;

[0021] Figure 6 A depicts a motion mode (M-mode) image of ultrasound signals acquired by an ultrasonic sensing element (sensing element) of a wUS-SWEM device according to an embodiment of the invention during the in vivo dynamic muscle contraction and an enlarged M-mode image where tone-burst SW displacements can be visually observed in the dashed bounding boxes;

[0022] Figure 6B depicts example plots of spatiotemporal displacements estimated using WUS that contain one SW tone-burst obtained during dynamic muscle contraction experiment as detected by the WUS Element-1 and WUS Element-2;

[0023] Figure 7 depicts measured tone-burst SW displacements at 6.4 mm depth obtained by WUS Element 1 and Element 2 during adynamic muscle contraction experiment together with images of enlarged SW displacement pairs showing the time delay of the SWs detected by the WUS in Element-1 and Element-2 in the relaxed and contracted states;

[0024] Figure 8 depicts acquired M-mode ultrasound data and shear modulus derived from SWV estimates during dynamic muscle contraction obtained with a wUS-SWEM device according to an embodiment of the invention;

[0025] Figure 9 depicts the shear modulus estimates in a muscle region of interest and a normalized grip force acquired during muscle contraction experiments using a hand dynamometer;

[0026] Figure 10 depicts a surface electromyography (sEMG) signal and muscle activation level obtained at the forearm during the dynamic contraction experiment;

[0027] Figure 11 depicts ultrasound pulse-echo signals reflected from a metal plate within a water tank and their frequency spectra as obtained by the WUS Element- 1 and WUS Element- 2 of the wUS-SWEM device according to an embodiment of the invention;

[0028] Figure 12 depicts the amplitude and spectra of a shear-wave multi-frequency pulse wave signal applied within the SWV dispersion measurements according to an embodiment of the invention;

[0029] Figure 13 depicts SW displacement of one shear-wave multi -frequency pulse wave signal according to an embodiment of the invention detected using a wUS-SWEM device according to an embodiment of the invention by WUS Element- 1 and WUS Element-2;

[0030] Figure 14 depicts detected SW displacements and magnitude spectra of SW displacements using the single multi-frequency pulse wave signal according to an embodiment of the invention at 6.2mm depth;

[0031] Figure 15 depicts a schematic of a wUS-SWEM device according to an embodiment of the invention;

[0032] Figure 16 depicts a wearable item incorporating wUS-SWEM devices according to an embodiment of the invention;

[0033] Figure 17 depicts a generic structure of a WUS according to an embodiment of the invention;

[0034] Figure 18 depicts a wUS-SWEM device according to an embodiment of the invention;

[0035] Figure 19 depicts the time-domain waveform of the constructed shear-wave multifrequency pulse (SW-MFP), the corresponding magnitude spectrum showing selected frequency components in the SW-MFP and a schematic of the experimental setup with the tissue mimicking phantom (phantom);

[0036] Figure 20 depicts spatiotemporal shear wave (SW) displacements detected by one ultrasonic transducer (UT), the SW displacements at 18 mm depth detected by both UTs and corresponding magnitude spectra at 18 mm depth using a wUS-SWEM device according to an embodiment of the invention using the phantom;

[0037] Figure 21 depicts the shear-wave velocity (SWV) estimates derived from one SW-MFP together with the frequency dependent SWV estimates over 24 SW-MFPs; and

[0038] Figure 22 depicts viscoelastic parameter estimates obtained from experiments using the SW-MFP methodology comprising SWV dispersion curves estimated across frequency for high, medium, and low stiffness gels together with estimated shear modulus and viscosity for the different gels.DETAILED DESCRIPTION

[0039] The present invention is directed to wearable devices and more particularly to wearable ultrasound devices and methods exploiting ultrasound shear- wave elastometry.

[0040] The ensuing description provides representative embodiment(s) only, and is not intended to limit the scope, applicability or configuration of the disclosure. Rather, the ensuing description of the embodiment(s) will provide those skilled in the art with an enabling description for implementing an embodiment or embodiments of the invention. It being understood that various changes can be made in the function and arrangement of elementswithout departing from the spirit and scope as set forth in the appended claims. Accordingly, an embodiment is an example or implementation of the inventions and not the sole implementation. Various appearances of “one embodiment,” “an embodiment” or “some embodiments” do not necessarily all refer to the same embodiments. Although various features of the invention may be described in the context of a single embodiment, the features may also be provided separately or in any suitable combination. Conversely, although the invention may be described herein in the context of separate embodiments for clarity, the invention can also be implemented in a single embodiment or any combination of embodiments.

[0041] Reference in the specification to “one embodiment”, “an embodiment”, “some embodiments” or “other embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment, but not necessarily all embodiments, of the inventions. The phraseology and terminology employed herein is not to be constmed as limiting but is for descriptive purpose only. It is to be understood that where the claims or specification refer to “a” or “an” element, such reference is not to be constmed as there being only one of that element. It is to be understood that where the specification states that a component feature, stmcture, or characteristic “may”, “might”, “can” or “could” be included, that particular component, feature, stmcture, or characteristic is not required to be included.

[0042] Reference to terms such as “left”, “right”, “top”, “bottom”, “front” and “back” are intended for use in respect to the orientation of the particular feature, stmcture, or element within the figures depicting embodiments of the invention. It would be evident that such directional terminology with respect to the actual use of a device has no specific meaning as the device can be employed in a multiplicity of orientations by the user or users.

[0043] Reference to terms “including”, “comprising”, “consisting” and grammatical variants thereof do not preclude the addition of one or more components, features, steps, integers or groups thereof and that the terms are not to be constmed as specifying components, features, steps or integers. Likewise, the phrase “consisting essentially of’, and grammatical variants thereof, when used herein is not to be constmed as excluding additional components, steps, features integers or groups thereof but rather that the additional features, integers, steps, components or groups thereof do not materially alter the basic and novel characteristics of the claimed composition, device or method. If the specification or claims refer to “an additional” element, that does not preclude there being more than one of the additional element.

[0044] A “wireless standard” as used herein and throughout this disclosure, refer to, but is not limited to, a standard for transmitting signals and / or data through electromagnetic radiationwhich may be optical, radio-frequency (RF) or microwave although typically RF wireless systems and techniques dominate. A wireless standard may be defined globally, nationally, or specific to an equipment manufacturer or set of equipment manufacturers. Dominant wireless standards at present include, but are not limited to IEEE 802.11, IEEE 802.15, IEEE 802.16, IEEE 802.20, UMTS, GSM 850, GSM 900, GSM 1800, GSM 1900, GPRS, ITU-R 5.138, ITU- R 5.150, ITU-R 5.280, IMT-1000, Bluetooth, Wi-Fi, Ultra-Wideband and WiMAX. Some standards may be a conglomeration of sub-standards such as IEEE 802.11 which may refer to, but is not limited to, IEEE 802.1a, IEEE 802.11b, IEEE 802.11g, or IEEE 802.1 In as well as others under the IEEE 802.11 umbrella.

[0045] A “wired standard” as used herein and throughout this disclosure, generally refers to, but is not limited to, a standard for transmitting signals and / or data through an electrical cable discretely or in combination with another signal. Such wired standards may include, but are not limited to, digital subscriber loop (DSL), Dial-Up (exploiting the public switched telephone network (PSTN) to establish a connection to an Internet service provider (ISP)), Data Over Cable Service Interface Specification (DOCSIS), Ethernet, Gigabit home networking (G.hn), Integrated Services Digital Network (ISDN), Multimedia over Coax Alliance (MoCA), and Power Line Communication (PLC, wherein data is overlaid to AC / DC power supply). In some embodiments a “wired standard” may refer to, but is not limited to, exploiting an optical cable and optical interfaces such as within Passive Optical Networks (PONs) for example.

[0046] A “sensor” as used herein may refer to, but is not limited to, a transducer providing an electrical output generated in dependence upon a magnitude of a measure and selected from the group comprising, but is not limited to, environmental sensors, medical sensors, biological sensors, chemical sensors, ambient environment sensors, accelerometers, position sensors, motion sensors, thermal sensors, infrared sensors, visible sensors, RFID sensors, and medical testing and diagnosis devices.

[0047] A “portable electronic device” (PED) as used herein and throughout this disclosure, refers to a wireless device used for communications and other applications that requires a battery or other independent form of energy for power. This includes, but is not limited to, devices such as a cellular telephone, smartphone, personal digital assistant (PDA), portable computer, pager, portable multimedia player, portable gaming console, laptop computer, tablet computer, a wearable device and an electronic reader.

[0048] A “fixed electronic device” (FED) as used herein and throughout this disclosure, refers to a wireless and / or wired device used for communications and other applications that requires connection to a fixed interface to obtain power. This includes, but is not limited to, a laptopcomputer, a personal computer, a computer server, a kiosk, a gaming console, a digital set-top box, an analog set-top box, an Internet enabled appliance, an Internet enabled television, and a multimedia player.

[0049] A “server” as used herein, and throughout this disclosure, refers to one or more physical computers co-located and / or geographically distributed running one or more services as a host to users of other computers, PEDs, FEDs, etc., to serve the client needs of these other users. This includes, but is not limited to, a database server, file server, mail server, print server, web server, gaming server, or virtual environment server.

[0050] An “application” (commonly referred to as an “app”) as used herein may refer to, but is not limited to, a “software application”, an element of a “software suite”, a computer program designed to allow an individual to perform an activity, a computer program designed to allow an electronic device to perform an activity, and a computer program designed to communicate with local and / or remote electronic devices. An application thus differs from an operating system (which runs a computer), a utility (which performs maintenance or general-purpose chores), and programming tools (with which computer programs are created). Within the following description with respect to embodiments of the invention an application is generally presented in respect of software permanently and / or temporarily installed upon a PED and / or FED.

[0051] An “enterprise” as used herein may refer to, but is not limited to, a provider of a service and / or a product to a user, customer, or consumer. This includes, but is not limited to, a retail outlet, a store, a market, an online marketplace, a manufacturer, an online retailer, a charity, a utility, and a service provider. Such enterprises may be directly owned and controlled by a company or may be owned and operated by a franchisee under the direction and management of a franchiser.

[0052] A “service provider” as used herein may refer to, but is not limited to, a third party provider of a service and / or a product to an enterprise and / or individual and / or group of individuals and / or a device comprising a microprocessor. This includes, but is not limited to, a retail outlet, a store, a market, an online marketplace, a manufacturer, an online retailer, a utility, an own brand provider, and a service provider wherein the service and / or product is at least one of marketed, sold, offered, and distributed by the enterprise solely or in addition to the service provider.

[0053] A “third party” or “third party provider” as used herein may refer to, but is not limited to, a so-called “arm's length” provider of a service and / or a product to an enterprise and / or individual and / or group of individuals and / or a device comprising a microprocessor whereinthe consumer and I or customer engages the third party but the actual service and I or product that they are interested in and / or purchase and / or receive is provided through an enterprise and / or service provider.

[0054] A “user” as used herein may refer to, but is not limited to, an individual or group of individuals. This includes, but is not limited to, private individuals, employees of organizations and / or enterprises, members of community organizations, members of charity organizations, men and women. In its broadest sense the user may further include, but not be limited to, software systems, mechanical systems, robotic systems, android systems, etc. that may be characterised by an ability to exploit one or more embodiments of the invention. A user may also be associated through one or more accounts and / or profiles with one or more of a service provider, third party provider, enterprise, social network, social media etc. via a dashboard, web service, website, software plug-in, software application, and graphical user interface.

[0055] “Biometric” information as used herein may refer to, but is not limited to, data relating to a user characterised by data relating to a subset of conditions including, but not limited to, their environment, medical condition, biological condition, physiological condition, chemical condition, ambient environment condition, position condition, neurological condition, drug condition, and one or more specific aspects of one or more of these said conditions. Accordingly, such biometric information may include, but not be limited, blood oxygenation, blood pressure, blood flow rate, heart rate, body temperate, fluidic pH, viscosity, particulate content, solids content, altitude, vibration, motion, perspiration, EEG, ECG, energy level, etc. In addition, biometric information may include data relating to physiological characteristics related to the shape and / or condition of the body wherein examples may include, but are not limited to, fingerprint, facial geometry, baldness, DNA, hand geometry, odour, and scent. Biometric information may also include data relating to behavioral characteristics, including but not limited to, typing rhythm, gait, and voice.

[0056] “User information” as used herein may refer to, but is not limited to, user behavior information and I or user profile information. It may also include a user's biometric information, an estimation of the user's biometric information, or a projection / prediction of a user's biometric information derived from current and I or historical biometric information.

[0057] A “wearable electronic device” (WED) or “wearable sensor” relates to miniature electronic devices that are worn by the user including those under, within, with or on top of clothing and are part of a broader general class of wearable technology which includes “wearable computers” which in contrast are directed to general or special purpose information technologies and media development. Such wearable devices and / or wearable sensors mayinclude, but not be limited to, smartphones, smart watches, e-textiles, smart shirts, activity trackers, smart glasses, environmental sensors, medical sensors, biological sensors, physiological sensors, chemical sensors, ambient environment sensors, position sensors, neurological sensors, drug delivery systems, medical testing and diagnosis devices, and motion sensors.

[0058] “Electronic content” (also referred to as “content” or “digital content”) as used herein may refer to, but is not limited to, any type of content that exists in the form of digital data as stored, transmitted, received and / or converted wherein one or more of these steps may be analog although generally these steps will be digital. Forms of digital content include, but are not limited to, information that is digitally broadcast, streamed or contained in discrete files. Viewed narrowly, types of digital content include popular media types such as MP3, JPG, AVI, TIFF, AAC, TXT, RTF, HTME, XHTME, PDF, XES, SVG, WMA, MP4, FEV, and PPT, for example, as well as others. Within a broader approach digital content mat include any type of digital information, e.g., digitally updated weather forecast, a GPS map, an eBook, a photograph, a video, a Vine™, a blog posting, a Facebook™ posting, a Twitter™ tweet, online TV, etc. The digital content may be any digital data that is at least one of generated, selected, created, modified, and transmitted in response to a user request, said request may be a query, a search, a trigger, an alarm, and a message for example.

[0059] A “profile” as used herein, and throughout this disclosure, refers to a computer and / or microprocessor readable data file comprising data relating to settings and / or limits of a device. Such profiles may be established by a manufacturer / supplier / provider of a device, service, etc. or they may be established by a user through a user interface for a device, a service or a PED / FED in communication with a device, another device, a server or a service provider etc.

[0060] A “computer file” (commonly known as a file) as used herein, and throughout this disclosure, refers to a computer resource for recording data discretely in a computer storage device, this data being electronic content. A file may be defined by one of different types of computer files, designed for different purposes. A file may be designed to store electronic content such as a written message, a video, a computer program, or a wide variety of other kinds of data. Some types of files can store several types of information at once. A file can be opened, read, modified, copied, and closed with one or more software applications an arbitrary number of times. Typically, files are organized in a file system which can be used on numerous different types of storage device exploiting different kinds of media which keeps track of where the files are located on the storage device(s) and enables user access. The format of a file is defined by its content since a file is solely a container for data, although, on some platformsthe format is usually indicated by its filename extension, specifying the rules for how the bytes must be organized and interpreted meaningfully. For example, the bytes of a plain text file are associated with either ASCII or UTF-8 characters, while the bytes of image, video, and audio files are interpreted otherwise. Some file types also allocate a few bytes for metadata, which allows a file to carry some basic information about itself.

[0061] “Metadata” as used herein, and throughout this disclosure, refers to information stored as data that provides information about other data. Many distinct types of metadata exist, including but not limited to, descriptive metadata, structural metadata, administrative metadata, reference metadata and statistical metadata. Descriptive metadata may describe a resource for purposes such as discovery and identification and may include, but not be limited to, elements such as title, abstract, author, and keywords. Structural metadata relates to containers of data and indicates how compound objects are assembled and may include, but not be limited to, how pages are ordered to form chapters, and typically describes the types, versions, relationships and other characteristics of digital materials. Administrative metadata may provide information employed in managing a resource and may include, but not be limited to, when and how it was created, file type, technical information, and who can access it. Reference metadata may describe the contents and quality of statistical data whereas statistical metadata may also describe processes that collect, process, or produce statistical data. Statistical metadata may also be referred to as process data.

[0062] An “artificial intelligence system” (referred to hereafter as artificial intelligence, Al) as used herein, and throughout disclosure, refers to machine intelligence or machine learning in contrast to natural intelligence. An Al may refer to analytical, human inspired, or humanized artificial intelligence. An Al may refer to the use of one or more machine learning algorithms and / or processes. An Al may employ one or more of an artificial network, decision trees, support vector machines, Bayesian networks, and genetic algorithms. An Al may employ a training model or federated learning.

[0063] “Machine Learning” (ML) or more specifically machine learning processes as used herein refers to, but is not limited, to programs, algorithms or software tools, which allow a given device or program to leam to adapt its functionality based on information processed by it or by other independent processes. These learning processes are in practice, gathered from the result of said process which produce data and or algorithms that lend themselves to prediction. This prediction process allows ML-capable devices to behave according to guidelines initially established within its own programming but evolved as a result of the ML. A machine learning algorithm or machining learning process as employed by an Al mayinclude, but not be limited to, supervised learning, unsupervised learning, Bayesian learning, semi-supervised learning, self-supervised learning, deep learning, cluster analysis, reinforcement learning, feature learning, sparse dictionary learning, anomaly detection, association rule learning, inductive logic programming.

[0064] Items of “apparel” or “clothing” as used herein and throughout this disclosure, refers to, but is not limited to, hats, helmets, tops, shirts, hooded jackets (hoodies), sweatshirts, t- shirts, ties, cravats, scarves, skirts, dresses, pants, trousers, socks, shorts, sweaters, jumpers, jackets, coats, overcoats, bras, underwear, lingerie, corsets, gloves, mittens, wristbands, headbands, sandals, shoes, boots, protective equipment, smart clothing, and specialist clothing such as required in undertaking certain sports activities, hobbies and / or employment activities.

[0065] Referring to Figure 1 there is depicted a Device 104 and Network Access Point 106 supporting Shear-Wave Ultrasound (SWU) Systems, Applications and Platforms (SWU-SAPs) according to embodiments of the invention. Device 104 as depicted may, for example, be a PED, FED or WED, include additional elements above and beyond those described and depicted or it may contain a subset of the elements described and depicted or a subset of the elements described and depicted with one or more additional elements above and beyond those described and depicted. The Device 104 is depicted as also being connected to an Electronic Device 100 which may a PED, FED or WED. Depicted within the Device 104 is the protocol architecture as part of a simplified functional diagram of a system that includes the Device 104, an Access Point (AP) 106 and one or more Network Devices 107, such as communication servers, streaming media servers, and routers for example. Network Devices 107 may be coupled to AP 106 via any combination of networks, wired, wireless and / or optical communication links as well as directly as indicated. Network Devices 107 are coupled to Network 100 and therein Social Networks (SOCNETS) 165, first and second Service Providers 170A and 170B respectively, first and second Third Party Service Providers 170C and 170D respectively, a User 170E, first and second Enterprises 175A and 175B respectively, first and second Organizations 175C and 175D respectively, and a Government Entity 175E. Accordingly, the Device 104 can access each of these as well as first and second Servers 190A and 190B respectively in order to provide and / or receive one or more of user information, electronic content, profile data, a computer file, metadata, sensor data and biometric information.

[0066] The Device 104 includes one or more Processors 110 and a Memory 112 coupled to Processor(s) 110. AP 106 also includes one or more processors 111 and a Memory 113 coupled to processor(s) 110. A non-exhaustive list of examples for any of Processors 110 and 111includes a central processing unit (CPU), a digital signal processor (DSP), a reduced instruction set computer (RISC), a complex instruction set computer (CISC) and the like. Furthermore, any of Processors 110 and 111 may be part of application specific integrated circuits (ASICs) or may be a part of application specific standard products (ASSPs). A non-exhaustive list of examples for Memories 112 and 113 includes any combination of the following semiconductor devices such as registers, latches, ROM, EEPROM, flash memory devices, non-volatile random access memory devices (NVRAM), SDRAM, DRAM, double data rate (DDR) memory devices, SRAM, universal serial bus (USB) removable memory, and the like.

[0067] Device 104 may include an Audio Input Element- 114, for example a microphone, and an Audio Output Element- 116, for example, a speaker, coupled to any of Processors 110. Device 104 may include a Video Input Element-118, for example, a video camera or camera, and a Video Output Element- 120, for example an LCD display, coupled to any of Processors 110. Device 104 also includes a Keyboard 115 and Touchpad 117 which may for example be a physical keyboard and touchpad allowing the user to enter content or select functions within one of more applications 122. Alternatively, the Keyboard 115 and Touchpad 117 may be predetermined regions of a touch sensitive element forming part of the display within the Device 104. The one or more Applications 122 that are typically stored in Memory 112 and are executable by any combination of Processors 110. Device 104 also includes Accelerometer 160 providing three-dimensional motion input to the Processor 110 and GPS 162 which provides geographical location information to Processor 110.

[0068] Device 104 includes a Protocol stack 124 and AP 106 includes an AP communication Stack 125. Within Figure 1 Protocol Stack 124 is shown as an IEEE 802.11 protocol stack but alternatively it may exploit other protocol stacks such as an Internet Engineering Task Force (IETF) multimedia protocol stack for example. Likewise, AP Stack 125 exploits a protocol stack but is not expanded for clarity. Elements of Protocol Stack 124 and AP Stack 125 may be implemented in any combination of software, firmware and / or hardware. Protocol Stack 124 includes an IEEE 802.11 -compatible PHY module 126 that is coupled to one or more Front- End Tx / Rx & Antenna 128, an IEEE 802.11 -compatible MAC module 130 coupled to an IEEE 802.2-compatible LLC module 132. Protocol Stack 124 includes a network layer IP module 134, a transport layer User Datagram Protocol (UDP) module 136 and a transport layer Transmission Control Protocol (TCP) module 138.

[0069] Protocol stack 124 also includes a session layer Real Time Transport Protocol (RTP) module 140, a Session Announcement Protocol (SAP) module 142, a Session Initiation Protocol (SIP) module 144 and a Real Time Streaming Protocol (RTSP) module 146. ProtocolStack 124 includes a presentation layer Media Negotiation module 148, a Call Control module 150, one or more Audio Codecs 152 and one or more Video Codecs 154. Applications 122 may be able to create maintain and / or terminate communication sessions with any Network Device 107 by way of AP 106. Typically, Applications 122 may activate any of the SAP, SIP, RTSP, media negotiation and call control modules for that purpose. Typically, information may propagate from the SAP, SIP, RTSP, media negotiation and call control modules to PHY module 126 through TCP module 138, IP module 134, LLC module 132 and MAC module 130.

[0070] It would be apparent to one skilled in the art that elements of the Device 104 may also be implemented within the AP 106 including but not limited to one or more elements of the protocol stack 124, including for example an IEEE 802.11 -compatible PHY module, an IEEE 802.11 -compatible MAC module, and an IEEE 802.2-compatible LLC module 132. The AP 106 may additionally include a network layer IP module, a transport layer User Datagram Protocol (UDP) module and a transport layer Transmission Control Protocol (TCP) module as well as a session layer Real Time Transport Protocol (RTP) module, a Session Announcement Protocol (SAP) module, a Session Initiation Protocol (SIP) module and a Real Time Streaming Protocol (RTSP) module, media negotiation module, and a call control module. Device 104 may include one or more additional wireless or wired interfaces in addition to the depicted IEEE 802.11 interface which may be selected from the group comprising IEEE 802.15, IEEE 802.16, IEEE 802.20, UMTS, GSM 850, GSM 900, GSM 1800, GSM 1900, GPRS, ITU-R 5.138, ITU-R 5.150, ITU-R 5.280, IMT-1000, DSL, Dial-Up, DOCSIS, Ethernet, G.hn, ISDN, MoCA, PON, and Power line communication (PLC).

[0071] Also depicted in Figure 1 are Electronic Devices (EDs) 100 which may support embodiments of the invention such as described and depicted below in respect of Figures 2 to 22. As depicted in Figure 1 an ED 100 may communicate directly to the Network 100A. Other EDs 100 may communicate to the Network Device 107, Access Point 106, and Device 104. Some EDs 100 may communicate to other EDs 100 directly. Within Figure 1 the EDs 100 coupled to the Network 100 and Network Device 107 communicate via wired interfaces. The EDs 100 coupled to the Access Point 106 and Device 104 communicate via wireless interfaces. Each ED 100 may communicate to another electronic device, e.g., Access Point 106, Device 104 and Network Device 107, or a network, e.g., Network 100. Each ED 100 may support one or more wireless or wired interfaces including those, for example, selected from the group comprising IEEE 802.11, IEEE 802.15, IEEE 802.16, IEEE 802.20, UMTS, GSM 850, GSM 900, GSM 1800, GSM 1900, GPRS, ITU-R 5.138, ITU-R 5.150, ITU-R 5.280, IMT-1000,DSL, Dial-Up, DOCSIS, Ethernet, G.hn, ISDN, MoCA, PON, and Power line communication (PLC).

[0072] Optionally, rather than wired and / or wireless communication interfaces devices may exploit other communication interfaces such as optical communication interfaces and / or satellite communications interfaces. Optical communications interfaces may support Ethernet, Gigabit Ethernet, SONET, Synchronous Digital Hierarchy (SDH) etc.

[0073] The first and second Servers 190A and 190B may host according to embodiments of the inventions multiple services associated with a provider of shear-wave ultrasound (UWU) Systems, Applications and Platforms (SWU-SAPs); a provider of a SOCNET or Social Media (SOME) exploiting SWU-SAPs; a provider of a SOCNET and I or SOME not exploiting SWU- SAPs; a provider of services to PEDS and / or FEDS; a provider of one or more aspects of wired and / or wireless communications; an enterprise exploiting SWU-SAPs; license databases; content databases; image databases; content libraries; customer databases; websites; and software applications for download to or access by FEDs and / or PEDs exploiting and / or hosting SWU-SAPs. First and second Servers 190A and 190B may also host for example other Internet services such as a search engine, financial services, third party applications and other Internet based services.

[0074] WEARABLE ULTRASOUND SHEAR- WAVE ELASTOMETRY FORCONTINUOUS MUSCLE MONITORING

[0075] Ultrasound Shear-Wave Elastography

[0076] Ultrasound shear-wave elastography (US-SWE) is a non-invasive method that has gained wide attention for characterizing the elastic properties of biological soft tissues, including skeletal muscles. The characterization is typically performed using the propagation velocity of shear waves (SWs) within a region of interest (ROI) estimated by means of ultrasound. The shear modulus, used as the measure of elasticity of the soft tissue, can then be calculated as the product of the squared SW velocity (SWV) and the tissue density. For biological soft tissues, the density can be approximated as that of water. Recent experimental studies have shown that factors such as force production, fatigue level, injury, and response to treatment and rehabilitation can affect the elasticity of muscle tissues. This makes the elasticity of muscle tissues a valuable parameter for clinical and sports medicine applications.

[0077] Within earlier in vivo studies that quantified the elasticity of muscle using US-SWE, a linear correlation between the shear modulus of the bicep muscle and the generated torque was demonstrated over a restricted range of contraction strength during controlled isometric contractions. In several studies, a linear correlation has been demonstrated between the shearmodulus estimated using US-SWE and the muscle activation level (MAL) obtained using surface electromyography (sEMG). Recently, US-SWE was shown to detect elasticity changes of in vivo muscle tissues in both active and passive states. The advantages of US-SWE over sEMG for muscle force estimation are known in the art, see for example Zimmer et al. in “Shear wave elastography characterizes passive and active mechanical properties of biceps brachii muscle in vivo,” (J. Mechanical Behaviour of Biomedical Materials, vol. 1377, p. 105543, 2023), particularly the higher correlation with force production and the ability to detect changes in the elasticity due to passive stretching. Using US-SWE, the shear modulus of the trunk extensor muscles was found to reduce in both active and passive states by fatigue induced from isometric exercises. Further, a recent review also concluded that most of the research in the area recognizes the potential of US-SWE for assessing exercise-induced muscle damage. However, it was commented that the method is sensitive to the detected SW propagation direction which depends on the orientation of the ultrasound imaging probe with respect to the muscle fibers. It was also remarked that estimation errors of shear modulus were caused by unwanted variations in pressure applied by the operator during data acquisition.

[0078] Additional investigations of muscle injury assessment using US-SWE has been reported where the size and severity of tears in the rotator cuff muscle tissue was shown to be closely correlated with the shear modulus. Assessment of post-stroke muscle elasticity using US-SWE was found to have utility for evaluating rehabilitative treatment effectiveness. Moreover, recent development in rehabilitative neuromodulation and adaptive exoskeleton devices have indicated the effectiveness of monitoring elasticity and other tissue mechanical properties to design systems that reduce the physical effort required by the subject and guide tissue adaptation.

[0079] Within prior art experiments on US-SWE it was identified that the estimation errors in the measurement of the shear modulus were caused by variations in the pressure applied by the operator with the probe during data acquisition where it has been shown that an increase of estimated elasticity was measured with greater applied force. Further, Sarabon et al. in “Using shear-wave elastography in skeletal muscle: A repeatability and reproducibility study on biceps femoris muscle” (PLoS One, vol. 14, no. 8, p. e0222008, 2019) found that high reproducibility of shear modulus estimates were obtained from the relaxed bicep using a conventional imaging probe but noted the need to have a consistent application force and positioning of the probe on the ROI to ensure a reliable estimate.

[0080] While US-SWE has the potential to characterize muscle tissue elasticity, its use has primarily been limited to controlled laboratory settings or clinical environments. This is in partbecause conventional ultrasound imaging probes are bulky and rigid, which creates difficulty in positioning and maintaining the probe on moving subjects. Quantifying muscle elasticity using US-SWE has demonstrated adequate reproducibility across intraday and interday experiments. However, a recent review article on ultrasound imaging for musculoskeletal assessment commented on the sensitivity of US-SWE to motion artifacts caused by probe instability and movement due to muscle contraction. Another review on the usability and issues with US-SWE for evaluation of muscle tissues similarly commented on the issue of motion artifacts due to movement of the subject or probe and the effect of probe pressure on the elasticity assessment. The effect of applied probe force on SWV estimates was further investigated by Sartori et al. “Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function” (Progress in Biomedical Engineering, vol. 3, no. 2. p. 023001. 2021). The experiment revealed an increase of estimated elasticity with greater applied force. Further other research found high reproducibility of shear modulus estimates obtained from the relaxed bleep using a conventional imaging probe, but commented on the need to have a consistent application force and positioning of the probe on the ROI to ensure a reliable estimate.

[0081] The mechanical properties of muscles during dynamic contractions could be different from those during isometric contractions, see Dick et al. “Advances in imaging for assessing the design and mechanics of skeletal muscle in vivo” (J. Biomechanics, p. 111640, 2023). As noted above, there are technical challenges when applying a conventional ultrasound imaging probe to the study of dynamic muscle contractions. Continuous monitoring of muscle contraction could provide useful information to improve the understanding of muscle dynamics. Accordingly, this provides motivation for the development of a wearable US-SWE device for monitoring muscles during dynamic contractions.

[0082] Recently, wearable ultrasound sensors (WUSs) have gained interest for continuously monitoring the morphological and dynamical properties of deep tissues. Several WUS devices with an ultrasonic transducer array structure have been proposed, some of which are based on flexible electronics that allow conformal contact of the device with the tissue area. In contrast to using an array of ultrasonic transducers to achieve a high lateral resolution by focusing the ultrasound beams, researchers have suggested the use of a small number of unfocused ultrasonic transducers for the WUS devices. This approach could reduce the complexity of the required electronics and the computational overhead required to process the resulting signals by avoiding the need for beamforming and image reconstruction. These WUS devices are operated in amplitude-mode (A-mode) or motion-mode (M-mode) depending on the intendedapplication. Hand gestures and / or wrist position were predicted using an unfocused WUS device operated in A-mode and M-mode for prosthetic control applications. An A-mode WUS device outperformed sEMG on the prediction accuracy of hand gesture and grasping force obtained at the forearm whilst within other work the authors estimated muscle contractile parameters using a WUS device by tracking the tissue displacements caused by the electrical muscle stimulation.

[0083] Despite the potential of US-SWE and the rapid increase in wearable device research over recent years, wearable US-SWE has not been discussed significantly within the literature to the inventor’s knowledge. Dagdeviren et al. in “Conformal piezoelectric systems for clinical and experimental characterization of soft tissue biomechanics” (Nature Materials vol .14, no. 7, pp. 728-736, 2015) presented a wearable device composed of piezoelectric actuator and sensing elements which was developed for estimating skin viscoelasticity. The actuators were attached onto the skin surface to produce vibrations inside the tissues where sensing elements were used to detect these vibrations allowing the researchers to quantify the skin viscoelasticity. In Martin et al. “Gauging force by tapping tendons” (Nature Communications, vol. 9, no. 1, p. 1592, 2018) and Harper et al. “Wearable tendon kinetics” (Sensors, vol. 20, no. 17, p. 4805, 2020), SWs were excited in the tissues including the Achilles tendon using a piezoelectric actuator attached on the skin surface and the SWV along the Achilles tendon was estimated using two accelerometers. These demonstrated that the SWV was correlated with the ankle torque during walking and running. Similarly, another study excited and detected SWs in the tissues around the Achilles tendon using a piezoelectric actuator and single accelerometer. A machine learning approach was used to predict the load on the Achilles tendon without requiring multiple accelerometers. All of these methods detected vibrations on the skin surface to infer the tissue property. Thus, they may be difficult to apply for characterizing deeper tissues like muscle.

[0084] Recently, some research results have demonstrated the feasibility of wearable ultrasound elastography. A quasi-static ultrasound elastography method was demonstrated using a wearable ultrasound array device with an external force applied to tissues in the ROI, see Hu et al. “Stretchable ultrasonic arrays for the three-dimensional mapping of the modulus of deep tissue” (Nature Biomedical Engineering, pp. 1-14, 2023). The proposed method was used to evaluate the recovery process of muscle after exercise over a period of multiple days. Within other prior art experiments a wearable US-SWE device was used to monitor elasticity changes of in vivo rat livers during chemically-induced liver failure, see Liu et al. “Wearable bioadhesive ultrasound shear wave elastography” (Sci. Adv., vol. 10, no. 10, 2024). Withinother work a custom probe for US-SWE was developed and integrated into a shoulder brace for quantifying the anisotropic SWV of the shoulder muscle during movement. Further, preliminary results of SWV estimation using a three-element piezoelectric system were presented with hydrogel phantoms towards a wearable ultrasound shear wave elastometry (wUS-SWEM) method, see Yan et al. “Achieving shear wave elastography by a three-element probe for wearable human-machine interface” (Int. J. of Electrical and Information Engineering, vol. 14, no. 7, pp. 208-211, 2020). The proposed system excited SWs using acoustic radiation force and estimated the SWV using two unfocused ultrasonic transducers. In vivo wearable experiments were not demonstrated but the result encouraged the development of a wUS-SWEM method and device employing M-mode signal acquisition from the unfocused ultrasonic transducers.

[0085] Accordingly, the inventors have established a wUS-SWEM device and method for continuous monitoring of biological soft tissues, allowing them to demonstrate its feasibility for continuously monitoring the SWV of in vivo muscle tissue during dynamic contraction and relaxation. The wUS-SWEM device is composed of a WUS and a miniaturized electromechanical actuator, e.g., piezoelectric bimorph vibrator (piezo-vibrator), that are embedded into a band for attachment to the individual’s body, e.g., arm or leg. The device uses a minimal number of components for generating shear waves and detecting their propagation within the tissues to reduce complexity and support manufacturing cost reduction as well as miniaturization for long-term continuous monitoring applications. The developed wUS- SWEM device has been used for an in vivo experiment of continuously monitoring SWV changes in muscle tissue during periodic gripping of a hand dynamometer.

[0086] Methodology

[0087] Measurement Principle of Shear-Wave Velocity Using Ultrasound

[0088] Referring to Figure 2, the SWV measurement principle and concept of continuously monitoring muscle during dynamic contraction employed by wUS-SWEM devices according to embodiments of the invention and measurements performed using said wUS-SWEM devices according to embodiments of the invention. In first Image 200A tone-burst SWs are periodically generated in the tissue of interest using an Actuator Element 210, which may for example be a vibratory element. Periodic SW pulses are used to enable measurements of the SWV at a specified pulse repetition interval (e.g., 100 ms) to achieve high temporal resolution so that changes in dynamic tissue mechanical properties can be resolved. Within the following description reference to a vibratory element is by way of example only and is not intended tolimit the scope of the invention as it would be evident to one of skill in the art that other devices may be employed to provide the actuator generating SWs or tone-burst SWs.

[0089] Tone-bust SWs being employed as continuous SWs could create SW standing waves and interference of reflected SWs in the tissue, which may influence the SW displacement detection and SWV estimation. The ultrasound sensing elements (sensing elements) WUS Element-1 220 and WUS Element-2230 in the WUS are operated synchronously in pulse-echo mode. They transmit unfocused ultrasound into the tissue and receive ultrasound signals reflected or back-scattered from the tissue. The SW tissue displacements parallel to the ultrasound propagation direction are obtained by analyzing the received ultrasound M-mode signals at the selected depth.

[0090] The SWVis then calculated using the known separation distance (d) and the time-difference (At) of the obtained SW displacements between the two sensing elements, i.e.,vsw=d / t. Second Image 200B presents the tone-burst SW displacement pair obtained by the sensing WUS Element-1 220 and WUS Element-2 230. The illustration in second Image 200B shows the greater At in the relaxed state (first Trace 240) compared to the contracted state (second Trace 250) where first and second Traces 240 and 250 schematically depict SW induced displacement with time. This depicts the lower SWV typically in the relaxed state, where the muscle tissue is usually softer than the contracted state. In third Image 200C, the concept of continuously monitoring the SWV during dynamic contraction of the muscle is presented as an arm is moved from relaxed (point 1) to contracted (point 2) and back to relaxed (point 3). The SWV is estimated with each pair of the tone-burst SWs generated during the dynamic contraction.

[0091] Design and Construction of Wearable Shear-Wave Elastometry Device

[0092] A schematic of a prototype WUS employed within a prototype wUS-SWEM according to an embodiment of the invention for estimating the SWV is depicted in Figure 3. As depicted the WUS structure comprises a sequence of vertically stacked layers, these being:• Upper electromagnetic shielding layer 310A;• Vibration damping layer 320;• Upper protection layer 330A;• Acoustic insulation layer 340;• Electrode layer comprising the upper Electrode 350 of WUS Element- 1 and upper Electrode 355 of WUS Element-2;• Piezoelectric polymer film 360;• Ground electrode 370;• Lower protection layer 330B; and• Lower electromagnetic shielding layer 31 OB.

[0093] Within a prototype embodiment of the invention a polyvinylidene diflouride (PVDF) polymer film of 110 pm (4.3 microinch) thickness was used as the piezoelectric material (piezoelectric polymer file 360). The PVDF film had top and bottom silver-ink electrodes (Bottom ground electrode 370 and upper Electrodes 350 and 355 of WUS Element-1 and WUS Element- 2) with 6 pm (0.24 microinch) thickness. PVDF was employed to construct WUSs for biomedical applications due to its flexibility, lightweight, broadband ultrasonic performance, and good acoustic impedance matching to the biological soft tissues although it would be evident that other materials may be employed. The two top electrodes of the ultrasonic sensing WUS Element- 1 and WUS Element-2 were formed on the single PVDF film by shaping the silver-ink electrodes as shown in Figure 3. The ultrasound active sensing area of each element is determined by the dimensions of the top electrode, which were 5 mm in width and 20 mm in length (0.2 x 0.8 inch).

[0094] The separation distance, d, between the centers of the two elements was 10 mm (0.4 inch). The ground electrode area was common to both sensing elements and had an area of 15 mm in width and 20 mm in length (0.6 x 0.8 inch).

[0095] An adhesive ethylene co-polymer film with a thickness of 0.18 mm (0.007 inch) was placed around the sensing elements as a protective layer (Protection layer 330A, 330B) for waterproof and electrical isolation. A piece of paper was inserted between the top electrode and the protective film for acoustic insulation (acoustic insulation layer 340). A 2 mm (0.08 inch) thick vibration damping layer was placed above the top protection layer to reduce the mechanical coupling between the WUS and piezo-vibrator. The electrodes of each sensing element were connected to a pulsed receiver using a coaxial cable such that a first WUS Element- 1 380A was established across the upper Electrode 350 and the Ground Electrode 370 with a second WUS Element-2380B across the upper Electrode 355 the Ground Electrode 370. Except for the ultrasound transmission / reception area, the entire WUS structure was covered by Faraday fabric which was grounded electrically to reduce environmental electromagnetic interference. The ultrasound center frequency and 6 dB bandwidth of a first constructed WUS element (WUS Element-1) were estimated to be 5.7 MHz and 4.7 MHz respectively and those of a second constructed WUS element (WUS Element-2) were estimated at 5.3 MHz and 6.1 MHz as evaluated with water tank experiments described below.

[0096] A piezo-vibrator was used to excite SWs in the tissue having mechanical dimensions of 20 mm in length, 8 mm in width, and 0.8 mm in thickness (0.8 x 0.31 x 0.031 inch) Although it would be evident that other SW excitation devices may be employed. It has desirable properties for wearable applications, such as small size and lightweight. Additionally, it can provide greater SW amplitude excitation compared to acoustic radiation force approaches, leading to more reliable detection of the SW displacements. Piezo-vibrators have also been successfully used for SW excitation in previous US SWE studies. A housing for the piezovibrator was produced using a 3D printer. Two pieces of stiff rubber were placed between the inner-housing and both ends of the piezo-vibrator to operate the piezo-vibrator in doubleclamped mode. In addition, a 2 mm (0.08 inch) thick vibration damping layer was placed inside the housing to avoid direct mechanical coupling between the piezo-vibrator and the WUS. The piezo-vibrator was wrapped by a thin non-conductive film to avoid the electrical coupling of the WUS with the SW tone-burst signals applied to the piezo-vibrator. This design also effectively reduced electrical noise in sEMG signals acquired in the in vivo experiments described subsequently. The vibrating surface area was contacted to the skin surface to generate the SWs in the tissues beneath the WUS.

[0097] The constructed WUS 410 and Actuator 420 (piezo- vibrator within the prototype) were embedded into a flexible arm-band for the in vivo experiment, as shown in first Image 400A in Figure 4. A lightweight flexible cloth was selected to reduce mechanical coupling between the piezo-vibrator and WUS through the structure. The WUS and piezo-vibrator were physically separated and mounted on individual pieces of a 2-mm thick vibration damping layer, for example 40 Durometer Sorbothane, to avoid direct mechanical coupling between the piezovibrator and the WUS. An adjustable strap was made using a hook and loop fastener. The interconnect between the sensing area and the adjustable strap was formed using a stretchable rubber to accommodate the changes in arm thickness during muscle contraction and relaxation. Second Image 400B in Figure 4 depicts the developed wUS-SWEM Device 430 attached on the upper arm for the in vivo experiment described below with the user holding a Hand Dynamometer 440 with sEMG, or Surface Electromyography (sEMG) Electrodes 450 attached.

[0098] Data Acquisition Setup

[0099] Referring to Figure 5A there is depicted a block diagram of the data acquisition system for the in vivo experiments using the proposed wUS-SWEM device. A sinusoidal tone-burst signal was generated using an arbitrary Waveform Generator 550. For the experiments in this study, a 100 Hz sinusoidal signal was amplitude-modulated with a square pulse with 50% dutycycle at a pulse repetition frequency of 10 Hz (i.e., the tone-burst duration was 50 ms). The tone-burst signals were sent to an open-loop piezo-amplifier to drive the piezo-vibrator for S W excitation.

[0100] Each ultrasonic sensing element of the WUS was driven by an ultrasonic pulsed receiver operated in the pulse-echo mode to generate and receive ultrasound, depicted as first and second Pulsed Receivers 520A and 520B. The first and second Pulsed Receivers 520A and 520B were synchronously triggered by the Function Generator 510 to send an electric pulse to the sensing elements, WUS Element- 1 and WUS Element-2, at a pulse repetition frequency (PRF) of 2 kHz for M-mode signal acquisition. The ultrasonic signals reflected from the tissues were received by the sensing elements and converted back to electrical signals. These electrical signals were digitized and acquired using a Multichannel Digitizer 530 at a sampling frequency of 60 MHz. The digitizer was also synchronized with the pulsed receivers using the same trigger signals from the Function Generator 510 for M-mode signal acquisition. The Multichannel Digitizer 530 being coupled to a PC 540 which also provides triggers to Tone- Burst Signal Generator 550 which is coupled to Piezo- Amplifier 560 which drives the Actuator. The input 570 represents additional experiment signals that were synchronously acquired using the Multichannel Digitizer 530, which could include sEMG signals from 440 and / or dynamometer signals from 450.

[0101] Signal Processing Procedure

[0102] The ultrasound data was post-processed offline to estimate the SWV after the data acquisition. A linear-phase 100-tap finite impulse response (FIR) band-pass filter with 6 dB pass-band width of 7 MHz (1.5-8.5 MHz) was applied to the acquired M-mode ultrasound signals along the depth-axis. In addition, a linear-phase 100-tap low-pass FIR filter with a 6 dB cut-off frequency of 220 Hz was applied along the time-axis of the M-mode signals. The phases of the acquired ultrasound signals were obtained using the Hilbert transform. Then, the instantaneous displacement at each depth was calculated using the phase difference between the consecutive ultrasound signals. Subsequently, a two dimensional median filter with a size of 43.12 pm x 1.4 ms (7 samples x 7 samples) was applied to the data of the spatiotemporal displacements obtained by each sensing element. Afterward, a weighted moving average using a Hanning window with a 1-mm depth range (162 samples) was applied for further denoising of the displacements detected by each sensing element.

[0103] At each sample depth, the tone-burst SW displacement pair obtained from the WUS Element- 1 and WUS Element-2 was segmented using a rectangular window with width of 50 ms and no overlap (see Figure 7). The phase of each tone-burst SW signal at the selected SWfrequency of 100 Hz was obtained by the Discrete Fourier Transform (DFT). The time delay of each segmented tone-burst SW pair was computed using the phase difference of SW displacements. SWV was then calculated using estimated time delay (At) and the known separation distance (d) of 10 mm (0.4 inch) between the two sensing elements as described above. The SWV was obtained every 100 ms since the tone-burst pulse repetition frequency (PRF) was 10 Hz in this experiment. The SWV estimation interval of 100 ms was considered as continuous monitoring of the muscle dynamics, since it is an appropriate response time required for typical prosthetic devices, where the delay should be less than 100-125 ms, see for example Farrell et al. “The optimal controller delay for myoelectric prostheses” (IEEE Trans. Neural Systems and Rehabilitation Engineering, vol. 15, no. 1. pp. 111-118, 2007). If a particular muscle monitoring application requires a shorter estimation interval of the SWV, then a higher PRF of the SW tone-burst could be chosen. This process was repeated for several selected depths in the ROI. Subsequently outlier SWV values were removed by a 7 x 7 median filter that was applied to the data structure containing the SWV estimates along analyzed depths in the ROI and time.

[0104] However, an actuator, such as the vibration source within the prototype, attached to the skin’s surface to generate surface waves (SWs) within internal tissues also induces surface acoustic waves (SAWs) that cause motion of the assembly, e.g. the ultrasound transducer (UT) assembly. This SAW induced motion generates motion artifacts (MAs) within the detected SW displacements, leading to errors in SW velocity estimation. Accordingly, the inventors have established a MA correction method using echoes from a stationary reference object (e.g. bone) in SWV measurement by wUS-SWEMs according to embodiments of the invention.

[0105] Referring to Figure 5B first Image 500B there is depicted an M-mode image of the echoes from soft tissues (including skin, fat, and muscle) and the bone echoes observed at a depth of 48 mm. Accordingly, second Image 500C depicts the displacements at 48-mm depth obtained by the pair of sensing elements, WUS Element-1 (UT-1) and WUS Element-2 (UT- 2) revealing the motion despite the bone being assumed stationary, indicating the WUS Element motion due to the SAW waves induced. The maximum displacements being 2.02 pm (WUS Element-1 (UT-1)) and 0.46 pm (WUS Element-2 (UT-2)). Third Image 500D depicts the displacements detected by WUS Elements which are the summation of the motion artefacts arising from the SAW induced displacements and the true SW displacements. After subtracting the MA from the displacements detected by each WUS Element the true SW displacements obtained are depicted in fourth Image 500E. Accordingly, the SWV estimates in the muscle region at depths between 20 mm and 40 mm are given in fifth Image 500F where the MA-corrected mean SWV is 5.1 m / s, whilst the uncorrected mean SWV is 8.6 m / s, indicating a bias of 3.5 m / s due to the SAW induced motion artifacts.

[0106] However, the motion artifact correction was established after the experiments described below were performed such that the results presented in Figures 6 A, 6B, 7, 900A, 13 and 14 do not include motion artifact correction.

[0107] In-Vivo Validation

[0108] The in vivo muscle contract experiments were approved by the Carleton University Research Ethics Board (CUREB-B, 10496 12-0382) and employed a single healthy subject (male, age 28) with no prior history of neuromuscular disease. The wUS-SWEM device was placed on the inner forearm of the user to monitor the SWV changes of the subject’s forearm muscle during the exertion of grip forces as depicted in second Image 400B in Figure 4. A thin layer of ultrasound couplant gel was applied between WUS and skin surface to ensure consistent ultrasound transmission and reception from the internal tissues. The strap tightness was adjusted so that steady contact of piezo-vibrator and WUS with the skin were maintained during the dynamic muscle contraction experiment.

[0109] In this experiment the subject squeezed a hand dynamometer periodically with the hand of their right arm on which the wUS-SWEM was attached. The subject was instructed to periodically squeeze the dynamometer for a duration of 2 seconds then relax for 1 second during the total data acquisition of 15 seconds. The hand dynamometer forces were digitized using the same digital acquisition system used to acquire the ultrasound signals with the same trigger signal and sampling frequency.

[0110] During the dynamic contraction, tone-burst SWs were generated in the forearm muscle using the piezo-vibrators , and ultrasound M mode signals were acquired with both the sensing elements in the WUS. The M-mode ultrasound signals were acquired over a total duration of 15 seconds during which five cycles of gripping and relaxing were completed. The depth range of the muscle of interest was separately verified using a B-mode ultrasound image obtained by a clinical ultrasound imaging system. The ultrasound M-mode signal acquisition during the cyclic gripping of the hand dynamometer.

[0111] Furthermore, sEMG signals were acquired simultaneously to evaluate the correlation between the SWV and the MAL of the forearm muscle during the contraction. The sEMG signals were acquired by Ag / AgCl electrodes placed on the skin above the forearm muscle as seen in second Image 400B in Figure 4. Positive and negative reference electrodes were placed at the proximal and distal ends of the inner forearm muscle and a ground reference was placed on the wrist. The electrodes were connected to an instrumentation amplifier that had a gain of100 V / V and an 80 dB common-mode rejection ratio. The sEMG signals were acquired using the same multichannel digitizer and trigger signals with the same acquisition conditions used for the M-mode ultrasound signal acquisition so that ultrasound and sEMG signals were synchronized, as shown in the input 570 in Figure 5. The acquired sEMG signal was zero-phase filtered using a 100-tap FIR band-pass filter with pass-band of 20-500 Hz. The MAL was then extracted by first computing the envelope of the filtered sEMG signal using the Hilbert Transform, and then applying a moving average filter with a window size of 1000 samples (0.2 s). The MAL was normalized by its maximum value.

[0112] Results

[0113] Detection of Shear-Waves

[0114] First Image 600A in Figure 6A depicts an example of the M-mode ultrasound signals acquired by the WUS Element- 1 in the WUS during the dynamic contraction experiment. The tissue displacements (motions) due to the muscle contraction are observed along the time axis in 3 second intervals (2 seconds squeezing, 1 second relaxing). Second Image 600B in Figure 6A depicts an enlarged area of the M-mode signals, in which the periodic tone-burst SW displacements of the tissue are visible in the dashed bounding box which contains one toneburst SW displacement detected along the depth. The observed displacements are consistent with the tone-burst SW excitation signals having a burst width of 25 ms and repetition interval of 50 ms (50% duty cycle) described above.

[0115] Referring to Figure 6B in first and second Images 600C and 600D respectively there are depicted the detected tissue displacements of the SW tone-burst for WUS Element-1 and WUS Element-2, respectively. In general, a lower displacement amplitude was observed in WUS Element-2, which is consistent the SW attenuation with increasing propagation distance.

[0116] To demonstrate further that the tissue displacements due to the tone-burst SW propagation were successfully detected, the SW displacements were retrieved from the total displacements shown in Figures 6A and 6B by high-pass filtering. First Image 700A in Figure 7 depicts an example of the retrieved tone-burst SW displacements at a depth of 6.4 mm with the WUS Element-1 and WUS Element-2 respectively over the full 15 second data acquisition. A periodic pattern is observed, which indicates the large tissue motions due to the muscle contraction to produce the grip force in the experiment. Second and third Image 700B and 700C depict the tone-burst pairs estimated at 6.4 mm at different times during the experiment. In second Image 700B the muscle was in a relaxed state at the time between 0.45 and 0.50 seconds. Third Image 700C depicts the tone-burst pair estimated during the contracted state between 1.8 and 1.85 seconds. A shorter time delay is observed between WUS Element-1 andWUS Element-2 in the contracted state compared to the relaxed, which is consistent with the expected change in SWV due to muscle contraction.

[0117] Shear-Wave Velocity

[0118] Figure 8 shows M-mode image data (800 A) and spatiotemporal shear modulus estimates (800B) derived from the SWV estimates using wUS-SWEM during a gripping experiment. In 800A, periodic tissue displacement can be observed that follow the cycles of contraction and relaxation. In 800B, the shear modulus estimates a low depth and time are presented. The shear modulus estimates varied within the muscle region of interest during the phases of contraction and relaxation.

[0119] Grip Force

[0120] Referring to Figure 9 there is depicted a plot of SWV in a muscle region of interest (900A) and normalized grip force (900B) obtained with the hand dynamometer during the contraction experiment. Five cycles of the grip force protocol are clearly evident, following a protocol of 2 seconds of contraction and 1 second of relaxation. The SWVs were estimated every 50 ms in the ROI of 4 mm thickness from 4.5 to 8.5 mm in depth during the experiment. 900A shows the estimated SWVs with respect to the acquisition time. Each SWV value represents the mean of 300 estimated SWVs in the ROI with a step of 12.83 pm which is the sampling distance along the depth corresponding to the sampling frequency of 60 MHz by assuming the average speed of sound in soft tissues to be 1540 m / s. The error bars represent the ± one stand deviation (SD) of the SWV estimates along the depth. The mean SWV in the ROI increased and decreased during the experiment within the five cycles being observed. The minimum and maximum of the mean SWV in the ROI were 3.09 m / s and 6.58 m / s, respectively. The mean SD of the SWV in the ROI was 0.22 m / s. The changes in the SWV were within the ranges reported for human muscle and can be attributed to the elasticity changes due to muscle contraction.

[0121] Surface Electromyography and Muscle Activation

[0122] The sEMG signal and MAE are shown in Figure 10 where these values were normalized by its maximum value, Furthermore, the baseline noise level observed in the relaxed state at 0 second was subtracted for the MAL values. The increases in MAL corresponded to the increase in grip force measured by the hand dynamometer during the contracted state.

[0123] Discussion

[0124] The mean SWV in the ROI was within the range of published literature values for human muscle and increased along with the increase of grip force and MAL. Five peaks were observed in the mean SWV of the ROI during the course of the experiment, indicating the number of contraction states. The time profile of the SWV was variable across each of the contract and relax cycles which could have been an artifact of the reduced sampling frequency of the SWV measurements (20 Hz) compared to the higher sampling frequency of the grip force and sEMG (2 kHz). The higher sampling frequency can improve the bandwidth of the data acquisition allowing sharper transitions to be resolved and timing improved.

[0125] Observations

[0126] Penetration Depth

[0127] Challenges and limitations of US-SWE have been discussed in the literature, see for example Airashed et al. “Reproducibility of shear wave elastography among operators, machines, and probes in an elasticity phantom” (Ultrasonography, vol. 40. no. 1, p. 158, 2021) and Franchi-Abella et al. “Performances and limitations of several ultrasound-based elastography techniques: a phantom study” (Ultrasound in Medicine & Biology, vol. 43. no. 10, pp. 2402-2415, 2017). The penetration depth of both ultrasound and SW limits the depth range of SWV estimates. The piezoelectric PVDF film used to construct the WUS in the developed wUS-SWEM device is less effective for generating ultrasound compared to piezoelectric ceramics, such as PZT, that are typically used for ultrasonic transducers. This is attributed to its relatively weak piezoelectric effect, low dielectric constant, high dielectric loss, and low electromechanical coupling coefficient, which results in the lower signal-to-noise ratio of the acquired ultrasound signals. Consequently, this creates a challenge in estimating the SW displacements with increasing depth.

[0128] To improve the ultrasound penetration depth, a thicker PVDF film could be used to lower the ultrasound frequency. This can improve the penetration depth of the ultrasound since the ultrasound attenuation is smaller at the lower ultrasonic frequency. Alternatively, piezoelectric materials that can generate greater intensity of ultrasound could be used, such as PZT, flexible PZT / polymer composites, and piezoelectric micromachined ultrasonic transducer (PMUT). Ultrasound element can also be a capacitive micromachined ultrasonic transducer (CMUT). The SW also attenuates during the propagation which limits its propagation distance and penetration depth. The mechanical coupling of the SW vibrator with the skin surface affects the efficiency to generate the SW in the tissue regions of interest. Future study will investigate these factors to optimize the wUS-SWEM device design and experimental configurations to increase the penetration depth of the ultrasound and SW.

[0129] Summary

[0130] As described above the inventors have proposed, described and developed a wUS- SWEM for continuous monitoring of dynamic muscle contraction. The developed device was demonstrated for estimating SWVs in human forearm muscle during the dynamic gripping of a hand dynamometer. The change in SWV estimated using the proposed wUS-SWEM device corresponded to the changes in the electrical activation of the muscle obtained using an sEMG. Future research could investigate the accuracy and reliability of the proposed wUS-SWEM using numerical simulation, in vivo and phantom experiments. Also, the wUS-SWEM device design and measurement configuration will be further studied and improved to enhance the measurement accuracy and reliability. Potential areas of applications include sports medicine where the analysis of the muscle mechanical property changes obtained using the proposed device could inform a practitioner of the muscle conditions, effectiveness of physical training and / or rehabilitation protocols. It could also be applicable to diagnose skeletal muscle diseases and neuromuscular diseases.

[0131] Frequency Characteristics of Wearable Ultrasonic Sensor

[0132] The frequency characteristics of the WUS Element- 1 and WUS Element-2 in the wUS-SWEM device were investigated for their pulse-echo signal obtained in a water tank experiment. The wUS was submerged in the water tank and placed parallel to a metal plate located at the bottom of the tank. The distance between the WUS and the surface of the metal plate was 20 mm (0.8 inch). The WUS Element-1 and WUS Element-2 were independently operated by an ultrasonic pulser / receiver. The ultrasound signal reflected from the top surface of the metal plate was acquired in pulse-echo mode. Figure 1100A depicts the ultrasound pulseecho signals acquired by the WUS Element-1 (solid line) and WUS Element-2 (dashed line). Figure 1100B depicts the corresponding frequency spectrum of the pulse-echo signal of the WUS Element- 1 (solid line) and WUS Element-2 (dashed line). A deformation from an ideal Gaussian shape was observed on the frequency spectrum, which is probably due to the ultrasound reflections within the wUS structure including the protection layer at the sensing surface. The peak frequency of the WUS Element-1 and WUS Element-2 were 6.4 MHz and 6.2 MHz, respectively. From Gaussian curve fitting to the frequency spectra in Figure 1 IB, the center frequency and 6-dB bandwidth of the WUS Element-1 were estimated to be 5.7 MHz and 4.7 MHz. respectively, and those of the WUS Element-2 were 5.3 MHz and 6.1 MHz.

[0133] MULTI-FREQUENCY VISCOELASTIC CHARACTERIZATION OF SOFT TISSUEAs outlined above, quantifying the viscoelastic properties of biological soft tissue holds significance for the fields of biomechanics and medicine. The composite structure of biological soft tissues gives rise to both elastic and viscous mechanical properties. These properties can indicate the structural and functional state of tissues. For example, muscles, tendons, and ligaments exhibit viscoelastic responses under applied loads and exerted forces. Viscoelasticity of muscle affects the contractile dynamics of muscle, providing a feedback path for joint torques under neuromuscular control.

[0134] Characterizing the viscoelasticity of muscle tissues can be used for diagnosis of neuromuscular disease and aid in the development of treatment and rehabilitation protocols. Research has also indicated that such mechanical properties of muscle are useful for the control of robot prosthetic devices and to guide tissue adaptation under closed-loop control along with functional electrical stimulation. These applications benefit from technologies that can unobtrusively characterize the mechanical properties of tissue.

[0135] As noted earlier, shear-wave ultrasound elastography (US-SWE) provides a technique for characterizing the mechanical properties of biological soft tissues. The mechanical properties are estimated by analyzing propagating SWs that are non-invasively detected in the tissue using pulse-echo ultrasound. Characterizing viscoelasticity using US- SWE is an area of research, with many existing studies neglecting the viscous characteristics of the tissue for model simplicity. Developing methods for viscoelastic characterization can further improve the utility of US-SWE in biomechanics research and clinical application. In muscle, the viscous component may be related to the structural characteristics of the muscle fibers. US-SWE has been used for assessing liver health, with recent studies being conducted to use viscoelastic parameters to improve surgical outcome.

[0136] Viscoelasticity creates a frequency-dependent attenuation and propagation velocity (i.e., velocity dispersion) of SWs. Broadband shear-waves (50 Hz - 1 kHz) can be excited in soft tissue using acoustic radiation force or mechanical vibration. Displacement tracking is performed on the ultrasound signals reflected from the region of interest. The velocity dispersion curve can then be estimated using the detected shear-wave displacements.

[0137] Methods

[0138] Sensors and Hardware

[0139] The construction and operation of the wUS-SWEM device are described above wherein the wUS-SWEM device contains two ultrasound sensing elements which are used to estimate the time delay of SWs propagating in the tissue. The SWs are generated in the tissue using a piezo-vibrator with a nominal unloaded resonant frequency of 370 Hz. An arbitrarywaveform generator was used to generate the control voltage for the piezo-vibrator. The digital sampling frequency of the waveform generator was 8 kHz. The control voltage was sent to an open-loop piezo-amplifier to drive the piezo-vibrator. Within the following study a multifrequency pulse (MFP) was employed to generate the SWs in the tissue with the wUS-SWEM device using a multi-frequency pulse (not broadband) to concentrate the energy in select frequency components and provide a characteristic frequency spectrum to differentiate against noise. Exploiting the wUS-SWEM device described above, a multi-frequency pulse is employed to control a piezo-vibrator to generate SWs in the tissue. The velocity dispersion curve is estimated based on the time-delay of the frequency components of the propagating SWs. Viscoelastic parameters are then estimated using a least-squares method with a Kelvin- Voigt (KV) model. The results arc compared against a conventional ultrasound imaging probe.

[0140] The nominal unloaded resonant frequency of the piezo-vibrator employed within the wUS-SWEM device according to an embodiment of the invention was 370 Hz. An arbitrary waveform generator was used to create the control voltage for the piezo-vibrator. The control voltage was sent to an open-loop piezo-amplifier to drive the piezo-vibrator. The MFP used to generate SWs in the tissue with wUS-SWEM device is given by Equation (1) where s(t) is the resulting MFP which is constructed as a sum of multiple sinusoids modulated by a window function w(t) to reduce SW reverberation and minimize spectral leakage where t represents time, f0is the lowest frequency component, N is the number of frequency components, and Afswis the frequency step between the frequency components. A 500-sample Hanning window was selected as w(t) to limit the signal in time. For the experiments, f0was selected to be 100 Hz, N = 5, Afs.w= 50Hz with the Hanning window at 5 kHz sampling spanning 100 ms such that the MFP s(t)is a periodic pulse with a period of 100 ms. First and second Images 1200A and 1200B in Figure 12 depict the MFP and its frequency spectrum, respectively. Before sending the signal to the piezo-vibrator, the control voltage of the MFP was scaled to the maximum range allowed by the waveform generator (-2 V to 2 V) to increase the amplitude of SWs. s(t) = Sn o w(t)cos[27r( / o + nAfsw)t] (1)

[0141] Data and In vivo

[0142] Data was acquired from a right bicep of a healthy human male subject of age 21 with no history of neuromuscular disease. The bicep was kept relaxed during the acquisition. The sampling frequency of ultrasound was 125 MHz, with a repetition frequency of 5 kHz. The MFP pulse was repeated every 100 ms. Data was acquired for 4 seconds (comprising 40pulses). Data were also acquired using single-frequency tone-burst to evaluate the repeatability of the SWV estimates using the MFP. For the single frequency sweep, sinusoidal tone-bursts with 50% duty cycle and repetition interval of 100 ms were used. Each of the five frequencies used in the MFP was separately applied to the vibrator, although as noted above the MFP may have N frequency components where N is a positive integer. Data was acquired for 4 seconds at each frequency.

[0143] Ultrasound Signal Processing

[0144] The acquired M-mode signals were processed offline after the experiment was performed. However, it would be evident that the signal processing may be employed upon data online as it is acquired whilst other samples are being acquired etc. The acquired M-mode data was first bandpass filtered using a linear-phase FIR bandpass filter with 200 taps and a pass-band from 1.5 MHz to 8.5 MHz. A linear-phase FIR low pass filter with 200 taps and a cutoff-frequency of 1.1 kHz was also applied along the time axis. For both sensing elements the instantaneous tissue displacements containing the propagating SWs were estimated by first computing the Hilbert transform for each ultrasound signal, and then calculating the phase change across successively acquired ultrasound signals.

[0145] If we define each S W displacement signal q( t) along time t and the depth sampled by the ultrasound pulse-echo signals then a set of narrow-band components corresponding to the selected frequencies in the MFP can be extracted using zero-phase bandpass filtering. The resulting narrowband complex signals for each UT at a selected center frequency fn(where n is an integer from 0 to N -1), denoted as q^(t; fn) for the first ultrasonic transducer (UT-1) and q^(t; fn) for the second ultrasonic transducer (UT-2) are obtained using the Hilbert transform. Accordingly, their cross-correlation can be computed using Equation (2) where C(t; fn) is the complex cross-correlation at a selected SW frequency fnthat captures the phase relationship between the SW displacement signals from the two UTs and * denotes the complex conjugation.

[0146] The normalized magnitude of the cross-correlation at a selected SW frequency (t; fn) given by Equation (3) can then be employed to weigh the phase estimates over time. The cross-correlation magnitude quantifies waveform similarity during SW propagation at a selected frequency. A magnitude-weighted complex summation may then be used to estimate the frequency-dependent phase delay ( fn). Similar approaches have been used in magnetic resonance imaging and seismic signal processing to improve the noise-robustness of phase estimates. The phase at each frequency was computed as the angle of the weighted sum of thecross-correlation values given by Equation (4). The SWV at each selected SW frequency can then be computed along the depth using the known center-to-center distance d between the pair of sensing elements as given by Equation (5).

[0147] Parameter Estimation and Kelvin-Voigt Model

[0148] The viscoelastic parameters of the tissue can be estimated using a Kelvin-Voigt (KV) model. The S WV dispersion for the KV model is given by Equation (6) where O)r= 2itfnis the angular frequency, p is the shear modulus, p is the viscosity of the medium,pis the density(assumed, for example, to be that of water for biological soft tissue but could be established specifically according to soft tissue assessed, for water this is 1000 kgm’3). The attenuation of the KV model is given by Equation (7). The KV model was fitted to the detected means of the estimated velocity dispersion curves using the least-squares method.

[0149] 3.2 Results and Discussion

[0150] The SW displacements of multi-frequency pulse that were detected by WUS Element- 1 and WUS Element-2 are shown in Figure 13 by first and second Images 1300A and 1300B, respectively. The maximum and minimum displacements detected in the ROI were 2.3 pm and -2.6 pm. Below 10 mm depth an increase in seemingly random detected displacements was observed, which could be caused by reverberation of SWs, physiological noise, and signal processing error due to the reduced signal-to-noise ratio of ultrasound with increasing depth. Above 14 mm depth the MFP becomes increasingly visually indistinguishable against the background noise.

[0151] Figure 14 shows an example of the detected SW displacements for one pulse wherein first Image 1400A depicts the displacements for both sensing elements of the WUS where WUS Element- 1 (closest to the piezo-vibrator) shows a higher amplitude compared to WUS Element-2. A time delay is also visible across the elements with the displacements detected at WUS Element- 1 occurring earlier than WUS Element-2 which is consistent with the experiment set-up and propagation of the SWs under the WUS. Second Image 1400B depicts the spectra of the detected SW displacements at WUS Element-1 and WUS Element-2. The frequency content of the multi -frequency pulse used to control the piezo-vibrator (for example a bimorph element) is also evident within the detected SW displacements. WUS Element-1 has higher amplitude than WUS Element-2, which is consistent with the attenuation of the SW with propagation distance. Notably, the 100 Hz component of the detected SW displacements was attenuated significantly which is caused by the limited bandwidth of vibrator.

[0152] Analysis

[0153] The viscoelasticity of a relaxed in vivo human bicep muscle was characterized using a developed wUS-SWEM technique. A MFP was designed and used to estimate the velocity dispersion of shear-waves in the tissue of interest. The estimates obtained using the MFP and single frequency sweep agreed with each other and previously reported values for human bicep muscle. These results point to a wearable device capable of continuously monitoring the viscoelastic properties of muscle. The developed wUS-SWEM device and MFP may be applied to monitoring viscoelastic changes of muscle during dynamic contractions.

[0154] Accordingly, the wUS-SWEM device prototype according to an embodiment of the invention in either single swept pulse or quicker multi-pulse measurements provided accurate measurements of the underlying biological soft tissue in a configuration compatible with shortterm or extended term user use to collect data from one or more activities during the measurement period. Further, the low complexity of the exemplary wUS-SWEM device prototype according to an embodiment of the invention shows that low cost wUS-SWEM devices can be established to allow widespread use of the concept by trainers, physicians, clinicians, therapists etc.

[0155] SINGLE SHOT VISCOELASTIC SENSING VIA SHEAR-WAVE MULTIFREQUENCY PULSE

[0156] As described and depicted above in respect of Figures 12 to 14 a MFP methodology for viscoelastic sensing was presented with respect to in vivo measurements on a human user’s arm. The inventors now refer to this as a single-shot method for SWV dispersion measurements using a spectrally engineered shear-wave multi-frequency pulse (SW-MFP), implemented withtheir wUS-SWEM devices according to embodiments of the invention. By concentrating energy into discrete frequency bands within a single excitation, SW-MFP eliminates repeated excitations and enables high-temporal resolution viscoelastic sensing without ultrasound transducer (UT) arrays. Within this section SW-MFP results are presented for a wUS-SWEM device according to an embodiment of the invention with tissue-mimicking phantoms with varied viscoelastic properties.

[0157] In contrast to the wUS-SWEM device described and depicted within Figures 3 and 4 then as depicted in Figure 18 a mechanical Actuator 1810 with 10-mm diameter was embedded in a three-dimensional (3D) Printed Housing 1840, and clamped to secure it on the surface of the medium (soft tissue-mimicking phantom) to generate the SWs. Two unfocused 5-MHz ultrasound transducers (UTs) 1820 and 1830 with 6-mm sensing diameter were fixed 15.4 mm apart along the phantom surface within a second 3D Printed Housing 1850. Within an embodiment of the invention the 3D printed housings for the actuator and the UTs may be connected via a flexible element such that the wUS-SWEM device is a single component or it may be deployed as two or more components.

[0158] The SWV at each frequency was estimated from the SW propagation time delay between the UTs and their known center-to-center separation distance (d). Acoustic coupling gel was placed between the UTs and phantom surface to improve acoustic coupling. The phantom was placed on a substrate (plexiglass for example) to provide a rigid boundary so that ultrasound reflections from this boundary could be used to correct for UT motion artifacts. Both UTs were operated in a pulse echo configuration, transmitting ultrasound pulses and receiving echoes from internal scatterers and reflections within the phantom to resolve SW propagation.

[0159] The SW actuator was driven by a function generator to generate SW-MFPs at a repetition rate of 5 Hz whilst a pair of pulser / receivers drove the UTs for ultrasound transmission and reception which were triggered at a 5-kHz ultrasound pulse repetition rate and synchronized via the function generator thereby enabling M-mode acquisitions of SW displacements beneath each UT.

[0160] Ultrasound pulse-echo signals were acquired at 30 MHz using a multi-channel digitizer. A concurrent timing signal marked SW-MFP onset, which was used to segment the ultrasound data into 200-ms windows aligned with the 5-Hz repetition rate. Each data acquisition in the experiment lasted 5 seconds, capturing 24 MFP events after segmenting using the timing signal.

[0161] SW displacements beneath each UT were resolved by computing instantaneous phase changes between successive ultrasound pulse-echo signals using the analytic signal from the Hilbert transform. To mitigate motion artifacts caused by mechanical coupling between the SW actuator and UTs, the UT displacements obtained by the ultrasound reflected from the phantom / substrate boundary were subtracted from those measured along the depth within the phantom.

[0162] The time-domain MFP signal and its magnitude spectrum are shown in first and second Images 1900A and 1900B respectively in Figure 19. In this experiment,0= 100 Hz, N = 5, and fsw= 50 Hz. A 500-sample Hanning window spanning (5 kHz sampling, 100 ms) was selected for w(t). Third Image 1900C in Figure 19 depicts the experimental configuration with the SW Actuator 1810, first UT 1820, second UT 1830, Phantom 1910 and Substrate 1920. The Phantom 1910 comprising a graphite loaded gel.

[0163] Referring to Figure 20 there are depicted the spatiotemporal SW displacements detected from the SW-MFP with a high-stiffness phantom. The spatiotemporal displacement map in first Image 2000A depicts SW propagation over the depth range from 5 mm to 35 mm, with displacement amplitudes ranging from -0.65 to 0.88 pm. Reverberation-like artifacts appear below around 32 mm depth, potentially due to boundary reflections at the phantom bottom against the substrate. Displacement waveforms at 18-mm depth for both UTs are shown in second Image 2000B in Figure 20 where a clear time delay between UT-1 and UT-2 is observed, with UT-2 lagging UT-1, consistent with the experiment set-up, depicted in third Image 1900C in Figure 19. The corresponding magnitude spectra, shown in third Image 2000C in Figure 20 confirm that all designed frequency components of the MFP were detected at both UTs with preserved spectral separation.

[0164] Now referring to Figure 21 there are depicted the estimated SWV as a function of frequency in the ROI for the high stiffness phantom. Accordingly, first Image 2100A depicts the estimated SWV from a single SW-MFP in a 10-mm ROI from 18-28 mm around the center of the phantom. As depicted at the five selected frequencies (100, 150, 200, 250, and 300 Hz), the mean and standard deviations (SD) of the SWV within the ROI were estimated as 4.92 ± 1.02 m / s, 6.57 ± 0.90 m / s, 7.92 ± 0.84 m / s, 11.62 ± 0.69 m / s and 13.23 ± 1.84 m / s, respectively. A monotonically increasing trend of SWV can be seen across the frequency range in the SW- MFP as expected for viscoelastic dispersion.

[0165] The mean Kelvin-Voigt (KV) viscoelastic model fit within the ROI (dashed line in first Image 2100A) follows the trend of the mean SWV values, with a root mean-squared deviation (RMSD) of 1.36 m / s between them.

[0166] In contrast second Image 2100B in Figure 21 compares SWV estimates in a high- stiffness phantom ROI across 24 pulses using the proposed MFP method and single-frequency sweep within the same ROI. The single frequency results being on the left at each frequency and the SW-MFP results on the right. For the single-frequency sweep, data were acquired separately at each frequency present in the SW-MFP by setting n = 0, 1, ...N -1 in Equation (1). Each excitation consisted of a windowed sinusoid using a 100-ms Hanning window repeating at 5 Hz. The same denoising and signal processing pipeline was applied to the singlefrequency data as to the SW-MFP data.

[0167] Both methods yielded similar SWV dispersion profiles, with frequency-dependent increases in SWV within the range of expected values for tissue-mimicking phantom materials. Moderate-low overlap was observed in the respective distributions (overlap coefficient ranging from a minimum of 0.14 at 200 Hz to a maximum of 0.6 at 250 Hz). Across the evaluated frequency range, the single-frequency sweep estimates tended to produce higher SWV means than the MFP method, particularly at the lower frequencies (e.g., 8.1 m / s versus 4.4 m / s at 100 Hz). However, these elevated estimates also exhibited substantially greater variability (standard deviation (SD) of 2.66 m / s for single -frequency sweep at 100 Hz), suggesting increased measurement uncertainty in this data.

[0168] Now referring to first Image 2200A in Figure 22 the SWV versus frequency is plotted for tissue-mimicking phantoms for three different gels having different stiffnesses. Based upon the fitted dispersion curves estimates of the shear modulus and viscosity were established as depicted in second and third Images 2200B and 2200C respectively. For the high-stiffness phantom, the estimated shear modulus and viscosity were 13.3 ± 3.8 kPa and 44.9 ± 5.9 Pa-s, respectively.

[0169] The shear modulus and viscosity of the medium stiffness phantom were 8.3 ± 6.9 kPa and 25.7 ± 6.0 Pa-s, respectively, while those of the low stiffness phantom were 5.4 ± 6.1 kPa and 14.6 ± 1.2 Pa-s. The shear modulus estimated for the high-modulus phantom was clearly separated from the other two, while the medium and low stiffness phantoms overlapped. The three phantoms were clearly differentiated by their viscosity estimates, with the viscosity decreasing from the high stiffness phantom to low stiffness phantom with minimal overlap of the distributions across the three materials.

[0170] These results demonstrate that the proposed SW-MFP method resolves both elastic and viscous parameters from a single-shot measurement, with a sensitivity sufficient to distinguish material properties across a range relevant to soft biological tissues. The viscoelastic parameters for each phantom were similar to those reported in soft biologicaltissues like muscle, although with higher viscosity estimates. Synthetic materials can exhibit substantially higher viscosities than biological soft tissues. For example, polyvinyl alcohol phantoms characterized with wUS-SWE and a KV model showed viscosity values of 6-15 Pa- s with shear modulus around 1 kPa. Further, by extracting viscoelastic parameters from a singleshot SW-MFP, the methodology according to embodiments of the invention supports rapid material characterization for resolving time dependent mechanical changes in dynamic media (e.g., contracting muscle) etc. Further, the compact lightweight design of the wUS-SWEM supports it being worn by a user performing real-world activities or monitoring longer term rather than within laboratory or clinical environments.

[0171] WEARABLE SHEAR-WAVE ULTRASOUND ELASTOMETRY DEVICE EMBODIMENTS

[0172] Referring to Figure 15 there is depicted wearable US-SWEM device (wUS-SWEM) 1500 wherein the associated electronics, actuator and a pair of ultrasonic elements are combined into a wearable assembly allowing the wUS-SWEM device 1500 to be worn outside of clinical environments and worn during activities, sports, therapy etc. As depicted the wUS- SWEM device 1500 comprises the associated electronics 1500A, a pair of ultrasonic elements 1500B and the actuator 1540. The associated electronics 1500A comprises a Signal Processing Unit 1510, Battery 1520, the Signal Generator 1530 being coupled to Actuator 1540, Signal Acquisition Unit 1550, Ultrasonic Pulser-Reciever-1 1560 being coupled to Ultrasonic Transducer-1 1570, and Ultrasonic Pulser-Reciever-2 1580 being coupled to Ultrasonic Transducer-2 1590. The Signal Processing Unit 1510 is coupled to a Data Transmission Unit 1595 which may be a wireless interface, wired interface, optical interface or combination thereof where the Signal Processing Unit 1510 and Data Transmission Unit 1595 are parts of a common Module 1500A.

[0173] Module 1500A may be part of a common circuit with Module 1500B or they may be connected by one or more interfaces, as may the Battery 1520, such that they can flex or bend to conform to a body part that the wUS-SWEM device 1500 is attached to either during mounting - demounting and / or use as the user exercises, moves, etc.

[0174] As depicted in Figure 16 the wUS-SWEM device 1500 may form part of a Wearable Item 1600 together with first to fourth Sensors 1610A to 1610D. The Wearable Item 1600 may comprise one or more wUS-SWEM devices 1500 with or without one or more sensors, such as first to fourth Sensors 1610A to 1610D for example. Each sensor may be selected, for example, from the group comprising environmental sensors, medical sensors, biological sensors, chemical sensors, ambient environment sensors, accelerometers, position sensors, motionsensors, thermal sensors, infrared sensors, visible sensors, RFID sensors, and medical testing and diagnosis devices. A biological sensor or medical testing and diagnosis device may include a sEMG electrode.

[0175] Whilst Wearable Item 1600 is shown employing multiple wUS-SWEM devices 1500 with multiple processors, batteries etc. it would be evident that within other embodiments of the invention variants of wUS-SWEM device 1500 may be employed such that multiple wUS- SWEM devices 1500 connect to a common Processor - Battery combination.

[0176] Wearable Item 1600 may comprise, for example, an item of clothing, an item of apparel, a bandage, an adhesive, elastic strap, a dressing, a splint, an orthotic device (generally orthoses), a therapeutic device, an assistive device, and a prosthetic device (prostheses).

[0177] In Figure 17 a design for a WUS employed within a prototype wUS-SWEM is presented. Within another embodiment a WUS may be implemented as depicted in Figure 17 as a Stack 1700 coupled to Skin 1760 via Couplant 1750. The Stack 1700 comprising Backing Layer 1710, Piezoelectric Material 1720 and Matching Layer 1730. As an option, Acoustic Lens 1740 can be added to focus ultrasound beam. The Piezoelectric Material 1720 may be a piezoelectric composite formed from two or more constituent materials, generally piezoelectric ceramic and polymer. Alternatively, the Piezoelectric Material 1720 may be a piezoelectric ceramic discretely or a piezoelectric polymer discretely rather than a combination thereof. Alternatively, the piezoelectric material may be a crystalline material such as lithium niobate, quartz, lead titanate etc.; a ceramic such as lead zirconate titanate, zinc oxide etc.; a lead free piezoceramic such as bismuth ferrite, sodium potassium niobate etc.; piezoelectric micromachined ultrasonic transducer (PMUT); polymers such as polyvinylidene fluoride (PVDF), polyamides, parylene-C, polyimide and polyvinylidene chloride (PVDC). Ultrasound elements can also be composed of capacitive micromachined ultrasonic transducers (CMUTs).

[0178] Whilst the embodiments of the invention have been described with respect to toneburst shear waves propagating within a region of a body it would be evident that the embodiments of the invention may be employed with other non-tone-burst shear waves.

[0179] Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, circuits may be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0180] Implementation of the techniques, blocks, steps and means described above may be done in various ways. For example, these techniques, blocks, steps and means may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above and / or a combination thereof.

[0181] Also, it is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process is terminated when its operations are completed but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.

[0182] Furthermore, embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages and / or any combination thereof. When implemented in software, firmware, middleware, scripting language and / or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine-readable medium, such as a storage medium. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures and / or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters and / or memory content. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0183] For a firmware and / or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein. For example, software codes may be stored in a memory. Memory may be implemented within the processor or external to theprocessor and may vary in implementation where the memory is employed in storing software codes for subsequent execution to that when the memory is employed in executing the software codes. As used herein the term “memory” refers to any type of long term, short term, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored.

[0184] Moreover, as disclosed herein, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and / or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and / or various other mediums capable of storing, containing or carrying instruction(s) and / or data.

[0185] The methodologies described herein are, in one or more embodiments, performable by a machine which includes one or more processors that accept code segments containing instructions. For any of the methods described herein, when the instructions are executed by the machine, the machine performs the method. Any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine are included. Thus, a typical machine may be exemplified by a typical processing system that includes one or more processors. Each processor may include one or more of a CPU, a graphicsprocessing unit, and a programmable DSP unit. The processing system further may include a memory subsystem including main RAM and / or a static RAM, and / or ROM. A bus subsystem may be included for communicating between the components. If the processing system requires a display, such a display may be included, e.g., a liquid crystal display (LCD). If manual data entry is required, the processing system also includes an input device such as one or more of an alphanumeric input unit such as a keyboard, a pointing control device such as a mouse, and so forth.

[0186] The memory includes machine-readable code segments (e.g., software or software code) including instructions for performing, when executed by the processing system, one of more of the methods described herein. The software may reside entirely in the memory, or may also reside, completely or at least partially, within the RAM and / or within the processor during execution thereof by the computer system. Thus, the memory and the processor also constitute a system comprising machine-readable code.

[0187] In alternative embodiments, the machine operates as a standalone device or may be connected, e.g., networked to other machines, in a networked deployment, the machine mayoperate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment. The machine may be, for example, a computer, a server, a cluster of servers, a cluster of computers, a web appliance, a distributed computing environment, a cloud computing environment, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. The term “machine” may also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0188] The foregoing disclosure of the exemplary embodiments of the present invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many variations and modifications of the embodiments described herein will be apparent to one of ordinary skill in the art in light of the above disclosure. The scope of the invention is to be defined only by the claims appended hereto, and by their equivalents.

[0189] Further, in describing representative embodiments of the present invention, the specification may have presented the method and / or process of the present invention as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. As one of ordinary skill in the art would appreciate, other sequences of steps may be possible. Therefore, the particular order of the steps set forth in the specification should not be constmed as limitations on the claims. In addition, the claims directed to the method and / or process of the present invention should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the present invention.

Claims

1. CLAIMSWhat is claimed is:

1. A wearable device comprising: an actuator element coupled to a region of a body, the actuator element generating shear-waves which induce tissue displacements within the region of the body; a pair of ultrasound elements coupled to another region of the body, each for transmitting an ultrasound signal and for receiving a reflected portion of the ultrasound signal where the reflection portion of the ultrasound signals is modulated by tissue displacements induced by the shear-waves generated by the actuator element; and means to mount each of the actuator element to the region of the body and the pair of ultrasound elements to the other region of the body; wherein the pair of ultrasound elements generate electrical outputs in dependence upon the reflected portion of the ultrasound signal; and the generated electrical outputs are processed to establish a shear-wave velocity of the shearwaves at one or more defined depths within the defined depth range of the other region of the body.

2. The wearable device according to claim 1, further comprising a printed circuit board comprising: a signal generator to drive the actuator; an ultrasonic pulser receiver for transmission and reception of ultrasound signals using the ultrasound elements; a digital acquisition unit to convert the analog electrical signals detected by the ultrasound elements to a digitized data; a digital signal processing unit to estimate the shear wave velocity and tissue mechanical properties; and a data transmission unit.

3. The wearable device according to claim 1, wherein the means to mount each of the actuator element to the region of the body and the pair of ultrasound elements to the other region of the body is one of an item of clothing, an item of apparel, a bandage, an adhesive, an elastic strap, a dressing, a splint, an orthotic device, a therapeutic device, an assistive device and a prosthetic device.

4. The wearable device according to claim 1, wherein each ultrasound element of the pair of ultrasound elements comprises one or more of the following: a matching layer, a backing layer, an acoustic lens, a piezoelectric material, a capacitive or piezoelectric micromachined transducer.

5. The wearable device according to claim 1, wherein the pair of ultrasound elements comprise one or more of an electromagnetic shielding layer, a vibration damping layer, a protection layer, an acoustic insulating layer, a top electrode layer including top electrodes for the pair of ultrasound elements, a piezoelectric polymer film, and a ground bottom electrode.

6. The wearable device according to claim 1, wherein the actuator produces one of: shear-waves having an arbitrary waveform; shear- waves that are continuous shear waves; the shear- waves are tone-burst shear waves; the shear-waves are broadband pulse shear waves; and the shear-waves are muti-frequency pulse shear waves.

7. The wearable device according to claim 1, wherein the shear-waves are tone-burst shear waves where each tone-burst pulse comprises N frequency components multiplied by a window function; the window function limits each tone-burst pulse in time; each frequency component is an integer multiple of a fundamental frequency; andN is a positive integer.

8. The wearable device according to claim 1, wherein the actuator element generates a series of periodic shear wave pulses; and a shear wave velocity for each pulse is used to resolve the tissue mechanical properties with a temporal resolution defined by the interval between successive pulses.

9. A method comprising: providing an actuator element coupled to a region of a body, the actuator element generating shear-waves which induce tissue displacements within the region of the body; providing a pair of ultrasound elements coupled to another region of the body, each for transmitting an ultrasound signal and for receiving a reflected portion of the ultrasound signal where the reflection portion of the ultrasound signal is modulated by tissue displacements induced by the shear-waves generated by the actuator element; and providing means to mount each of the actuator element to the region of the body and the pair of ultrasound elements to the other region of the body; wherein the pair of ultrasound elements generate electrical outputs in dependence upon displacements induced by the tone-burst shear- waves; and the generated electrical outputs are processed to establish a shear-wave velocity of the shearwaves at one or more defined depths within the defined depth range of the other region of the body.

10. The method according to claim 9, wherein the means to mount each of the actuator element to the region of the body and the pair of ultrasound elements to the other region of the body is one of an item of clothing, an item of apparel, a bandage, an adhesive, an elastic strap, a dressing, a splint, an orthotic device, a therapeutic device, an assistive device and a prosthetic device.

11. The method according to claim 9, wherein each ultrasound element of the pair of ultrasound elements comprises one or more of the following: a matching layer, a backing layer, an acoustic lens, a piezoelectric material, a capacitive or piezoelectric micromachined transducer. OBBOBBOBB12. The method according to claim 9, wherein the pair of ultrasound elements comprise one or more of an electromagnetic shielding layer, a vibration damping layer, a protection layer, an acoustic insulating layer, a top electrode layer including top electrodes for the pair of ultrasound elements, a piezoelectric polymer film, and a ground bottom electrode.

13. The method according to claim 9, whereinthe actuator produces one of: shear-waves having an arbitrary waveform; shear- waves that are continuous shear waves; the shear- waves are tone-burst shear waves; the shear-waves are broadband pulse shear waves; and the shear-waves are muti-frequency pulse shear waves.

14. The method according to claim 9, wherein the shear-waves are pulse shear waves where each pulse comprises N frequency components multiplied by a window function; the window function limits each pulse in time; each frequency component is an integer multiple of a fundamental frequency; andN is a positive integer.

15. The method according to claim 9, wherein processing the generated electrical outputs comprises extracting motion artefacts relating to surface acoustic wave induced displacements from shear-wave induced displacements; and the surface acoustic waves are generated in the another region of the body by the actuator.

16. A method comprising: providing an actuator element coupled to a region of a body, the actuator element excited by a multi-frequency pulse (MFP) and generating shear waves within the region of the body; providing a pair of ultrasound elements coupled to another region of the body, each for transmitting an ultrasound signal and for receiving a reflected portion of the ultrasound signal where the reflection portion of the ultrasound signals is modulated by tissue displacements induced by the shear-waves generated by the actuator element; and processing the generated electrical outputs from the pair of ultrasound elements to establish a shear-wave velocity dispersion curve of the shear-waves at a defined depth within the defined range of the other region of the body in dependence upon the phase shift or time delay of the shear wave frequency components generated by the MFP applied to the actuator element.

17. The method according to claim 16, whereinthe MFP concentrates energy at selected frequencies within a range of frequencies of shear waves within the region of the body and the other region of the body.

18. The method according to claim 16, further comprising establishing one or more viscoelastic parameters of the other region of the body at a defined depth by fitting the detected shear-wave velocity dispersion curve to a Kelvin-Voigt (KV) mechanical model .

19. The method according to claim 16, wherein the predetermined relationship relative to the actuator element of the pair of ultrasound elements is such that the shear-waves generated by the actuator element are received initially by one ultrasound element of the pair of ultrasound elements and subsequently by the other ultrasound element of the pair of ultrasound elements.

20. The method according to claim 16, further comprising providing a wearable element where the wearable element comprises the actuator element and the pair of ultrasound elements; wherein the wearable element when worn by user positions the actuator element at the region of the body and the pair of ultrasound elements at the other region of the body.

21. The method according to claim 16, wherein the shear- waves are pulse shear waves.

22. The method according to claim 16, wherein the shear-waves are pulse shear waves where each pulse comprises N frequency components multiplied by a window function; the window function limits each pulse in time; each frequency component is an integer multiple of a fundamental frequency; andN is a positive integer.

23. The method according to claim 16, further comprising providing periodic shear wave pulses and estimating tissue mechanical properties for each pulse with the temporal resolution of the supplied pulse repetition interval.

24. The method according to claim 16, wherein processing the generated electrical outputs comprises extracting motion artefacts relating to surface acoustic wave induced displacements from shear-wave induced displacements; and the surface acoustic waves are generated in the another region of the body by the actuator.

25. A method comprising: transmitting shear waves within a tissue region of a body where the shear waves produce tissue displacements within a tissue region of another region of the body; providing a set of ultrasound elements which transmit ultrasound pulses into the tissue region within which the shear waves are propagating; the ultrasound pulses are reflected from the tissue boundaries and / or scatterers which change their depth location with time due to the tissue displacements induced by the shearwave propagation in the region of interest; the ultrasound elements receive ultrasound pulses reflected from the tissue boundaries and / or scatterers which change their depth location with time due to the tissue displacements induced by the shear-wave propagation within the tissue region of another region of the body and generate electrical outputs in dependence upon the displacements induced by the shear- waves.

26. A method comprising: generating a series of periodic shear wave pulses within the tissue region, the interval between successive pulses defining the temporal resolution of the measurements; and processing the electrical outputs from the ultrasound elements corresponding to the periodic shear wave pulses to estimate time-resolved shear wave velocity and mechanical properties of the tissue within the region of interest.

Images