Patent Application
20230014065
The present invention, in some embodiments thereof, relates to a non-invasive monitoring and, more particularly, but not exclusively, to a system and method for mapping muscular activation.
Human facial muscle activation underlies highly sophisticated signaling mechanisms that are important for healthy physiological function. Current technologies for analyzing facial muscle activity are based on electromyography electrodes, which are usually in the form of stiff metal pads. Also known is the use of gel for improving the electrical communication between the skin and the electrodes.
The Inventors of the present invention realized the need to analyze muscle activation (for example, facial muscles, diaphragm muscle, limb muscles) at high-resolution and in a non-invasive manner for the diagnosis and treatment of many medical, psychological and cognitive conditions as well of for cosmetic purposes. The Inventors found that current clinical examination methods are neither precise nor quantitative. For example, stiff metal pads lack flexibility and thus suffer from poor adhesion to the skin, resulting in low signal-to-noise ratio, especially during muscle activation, and gelled electrodes are usually bulky and cumbersome and suffer from reduced signal over time due to gel dehydration. The inventors realize that visual inspection is highly subjective and builds on highly trained personnel, and that video processing lacks physiological validity. The inventors found that both visual inspection and video processing are insensitive to isometric muscle activations since in some cases the muscles can be activated, without a noticeably change in their length.
According to an aspect of some embodiments of the present invention there is provided a system for determining muscle activation. The system comprises a set of electrode adherable to a skin of a subject, and a processor in communication with the electrodes. The processor has a circuit configured for receiving locations of the electrodes and electrical signals detected by the electrodes, analyzing the signals to identify a section of an active muscle, identifying locations of at least a segment of active muscles and activation patterns of the active muscles based on the identified section, and constructing a displayable map of the locations and the activation patterns, wherein patterns corresponding to different active muscles are distinguishable on the map.
According to some embodiments of the invention the map overlays an image of a body portion and/or a graphical representation of the electrodes.
According to some embodiments of the invention the analysis is carried out by a blind source separation algorithm.
According to some embodiments of the invention the circuit is configured for detecting muscle unit action potential (MUAP) activity based on an output of the blind source separation algorithm.
According to some embodiments of the invention the set of electrodes comprises two subsets of electrode for receiving signals from respective two opposite sides of a portion of the skin.
According to some embodiments of the invention the set of electrodes comprises two subsets of electrode for receiving signals from respective two limbs.
According to some embodiments of the invention the circuit is configured to access a database storing a library of activation patterns and associated control commands, to search the database for a database activation pattern matching the identified activation pattern, and to extract from the library control commands associated with the matched database activation pattern.
According to some embodiments of the invention the circuit is configured to transmit the extracted control commands to an appliance.
According to some embodiments of the invention the circuit is configured for at least one member of a group consisting of: determining muscle fatigue, performance training, for rehabilitation, for determining muscle pain and any combination thereof.
According to some embodiments of the invention the circuit is configured to generate a warning if a parameter is outside at least one predetermined limit.
According to some embodiments of the invention the parameter comprises at least one of level of pain, force exerted by a muscle, level of muscle fatigue, and asymmetry of muscle activity.
According to some embodiments of the invention the warning is provided by a member of a group consisting of visually, audibly or tactilely and any combination thereof.
According to some embodiments of the invention the system is in use in relation to plastic surgery, for a member of a group consisting of improvement of facial symmetry, during rehabilitation physiotherapy and any combination thereof.
According to some embodiments of the invention the system is in use for neurorehabilitation.
According to some embodiments of the invention the circuit is configured for at least one of: providing characterization of walking, providing assessment of post-stroke recovery, providing assessment of post-spinal cord injury motor recovery, providing spasticity assessment, providing biofeedback, employing serious games, providing indication of muscle synergies, controlling a prosthesis, controlling an exoskeleton, controlling a robot.
According to some embodiments of the invention the system is in use for at least one of: extracting neural control strategies, myoelectric manifestations of muscle fatigue, and myoelectric manifestations of cramps.
According to some embodiments of the invention the circuit is configured for identifying the locations and the activation patterns, while the subject is moving.
According to some embodiments of the invention the circuit is configured for identifying the locations and the activation patterns, while the active muscles do not change their length or shape.
According to an aspect of some embodiments of the present invention there is provided a method of determining muscle activation. The method comprises adhering a set of electrodes to a skin of a subject, receiving locations of the electrodes and electrical signals detected by the electrodes, analyzing the signals to identify a section of an active muscle, identifying locations of at least segments of active muscles and activation patterns of the active muscles based on the identified section, and constructing a displayable map of the locations and the activation patterns, wherein patterns corresponding to different active muscles are distinguishable on the map. Various operations of the method are optionally and preferably carried out by a processor.
According to some embodiments of the invention the map overlays an image of a body portion and/or a graphical representation of the electrodes.
According to some embodiments of the invention the body portion is selected from a group consisting of a portion of a face, a portion of a neck, a portion of an arm, a portion of a leg, a portion of a hand, a portion of a foot, a portion of a torso, a portion of a head, and any combination thereof.
According to some embodiments of the invention the analysis is carried out by a blind source separation algorithm.
According to some embodiments of the invention the blind source separation algorithm comprises an algorithm selected from a group consisting of independent component analysis (ICA), fast independent component analysis (fastICA), principal component analysis, singular value decomposition, dependent component analysis, non-negative matrix factorization, low-complexity coding and decoding, stationary subspace analysis, common spatial pattern analysis and any combination thereof.
According to some embodiments of the invention the method comprises detecting muscle unit action potential (MUAP) activity based on an output of the blind source separation algorithm.
According to some embodiments of the invention the adhering comprises adhering two subsets of electrode to respective two opposite sides of a portion of the skin.
According to some embodiments of the invention the adhering comprises adhering two subsets of electrode to respective two limbs.
According to some embodiments of the invention the method comprises accessing a database storing a library of activation patterns and associated control commands, search the database for a database activation pattern matching the identified activation pattern, and extracting from the library control commands associated with the matched database activation pattern.
According to some embodiments of the invention the method comprises transmitting the extracted control commands to an appliance.
According to some embodiments of the invention the appliance comprises at least one of a robot and a personal mobile device.
According to some embodiments of the invention the method is in use for at least one of: determining muscle fatigue, performance training, for rehabilitation, for determining muscle pain and any combination thereof.
According to some embodiments of the invention the method comprises generating a warning if a parameter is outside at least one predetermined limit. According to some embodiments of the invention the parameter comprises at least one of: level of pain, force exerted by a muscle, level of muscle fatigue, and asymmetry of muscle activity.
According to some embodiments of the invention the method is in use in relation to plastic surgery, for a member of a group consisting of improvement of facial symmetry, during rehabilitation physiotherapy and any combination thereof.
According to some embodiments of the invention the method is in use for neurorehabilitation.
According to some embodiments of the invention the method comprises at least one of: providing characterization of walking, providing assessment of post-stroke recovery, providing assessment of post-spinal cord injury motor recovery, providing spasticity assessment, providing biofeedback, employing serious games, providing indication of muscle synergies, controlling a prosthesis, controlling an exoskeleton, controlling a robot.
According to some embodiments of the invention the method is in use for at least one of: extracting neural control strategies, myoelectric manifestations of muscle fatigue, and myoelectric manifestations of cramps.
According to some embodiments of the invention the identifying the locations and the activation patterns is executed while the subject is moving.
According to some embodiments of the invention the identifying the locations and the activation patterns is executed while the active muscles do not change their length or shape.
Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.
Implementation of the method and/or system of embodiments of the invention can involve performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system.
For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of method and/or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data. Optionally, a network connection is provided as well. A display and/or a user input device such as a keyboard or mouse are optionally provided as well.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings and images. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.
In the drawings:
The present invention, in some embodiments thereof, relates to a non-invasive monitoring and, more particularly, but not exclusively, to a system and method for mapping muscular activation.
Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.
The present embodiments comprise a set of electrodes configured to provide a high-definition map of muscle activation in a region below the skin. The present embodiments can also comprise a circuit configured to execute program instructions that analyze muscle activation so as to determine which muscles are activated and how strong the activation is. The set of electrodes are non-invasively attachable to the region of skin, imposing no mechanical disturbance to the user and are configured to be customized for the user.
Computer programs implementing the method of the present embodiments can commonly be distributed to users by a communication network or on a distribution medium such as, but not limited to, a floppy disk, a CD-ROM, a flash memory device and a portable hard drive. From the communication network or distribution medium, the computer programs can be copied to a hard disk or a similar intermediate storage medium. The computer programs can be run by loading the code instructions either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of this invention. During operation, the computer can store in a memory data structures or values obtained by intermediate calculations and pulls these data structures or values for use in subsequent operation. All these operations are well-known to those skilled in the art of computer systems.
Processing operations described herein may be performed by means of processor circuit, such as a DSP, microcontroller, FPGA, ASIC, etc., or any other conventional and/or dedicated computing system.
The method of the present embodiments can be embodied in many forms. For example, it can be embodied in on a tangible medium such as a computer for performing the method operations. It can be embodied on a computer readable medium, comprising computer readable instructions for carrying out the method operations. In can also be embodied in electronic device having digital computer capabilities arranged to run the computer program on the tangible medium or execute the instruction on a computer readable medium.
Fields in which such the map of the present embodiments can be used include, but are not limited to, medicine, esthetic treatments, and sport. Objective quantification of the muscular activity signatures holds exciting opportunities in many fields such as diagnostic pathology, prosthetics control, rehabilitation after stroke or injury, sports and entertainment, neurological and psychological evaluation, respiratory monitoring.
The electrodes measure electrical activity in different parts of the region of skin under examination. From patterns of electrical activity, the system of the present embodiments can be determined muscle or muscle-segment locations, muscle coordination. The system can optionally and preferably also determine whether a muscle or one or more muscle groups are non-functional, ill-functioning or improperly functioning. The system can also be used to induce functionality in non-functioning muscles or improve functionality in ill-functioning or improperly-functioning muscles or muscle groups.
The set of electrodes of the present embodiments optionally and preferably comprise a wearable customizable high-resolution surface electromyography electrode array.
The wearable high-resolution surface electromyography electrode array of the present embodiments are optionally and preferably printed electrodes, such as, but not limited to, printed carbon electrodes. Other conductive printed electrodes are also contemplated. The electrodes are optionally and preferably deposited, more preferably printed, on a substrate characterized by a Young's modulus of less than 30 MPa, e.g., from about 1 MPa to about 30 MPa. A representative example of a material suitable for use as a substrate is, without limitation a polyurethane. The diameter of the electrodes comprises is typically from about 3 mm to about 10 mm. The inventors found that such dimensions allow high density, while maintaining low noise levels and good conformity with the skin. The thickness of the substrate is typically from about 60 to about 150 μm, e.g., 80 μm.
As used herein the term “about” refers to ±10%
In some embodiments of the present invention the signals from the electrodes are analyzed to provide a map of muscle activation patterns and locations of active muscles or segments of active muscles. The maps can be derived from repeated voluntary muscle activations. An independent component (IC) analysis procedure and a machine learning procedure can then identify activation patterns that are subject-specific, and optionally and preferably also activation patterns that are universal or specific to a group of subjects.
The application of IC analysis and machine learning procedure of the present embodiments to data acquired by the electrodes is advantageous because it allows identifying the locations of the active muscles (or of their segments) and the activation patterns, even when the subject is moving. This is unlike conventional techniques in which the subject is restricted to be static. An additional advantage is that it allows the identifying of activation patterns of active muscles while the muscles do not change their length or shape (neither during the contraction of the muscle nor during the return of the muscle to its relaxed state).
The activation patterns can optionally and preferably be used to identify normal and abnormal activation patterns and, therefore, normal and abnormal patterns of use of the muscles. In some embodiments, the patterns can be used as input to a training program to improve muscle use, as an identifier of tiredness in muscles, muscle sections or muscle groups, as an identifier of overuse of muscles or muscle groups, and any combination thereof.
In some embodiments, the set of electrodes are attached to the face of the subject.
Human facial muscle is illustrated in
In preferred embodiments, the electrode array comprises printed dry electrodes having multiple recording sites that allow a customized match to human anatomy with synchronous recordings from numerous muscles using a single electrode array. For example, in experiments performed according to some embodiments of the present invention, a hemi-facial 16 electrode array, which covers many lateral parts of the face, has been employed, allowing mapping several facial expressions.
Electrical activity of a muscle can be found by employing a technique that can sense a change in bioelectrical potential which can be picked-up from the surface of the skin. Examples include, but are not limited to, electroencephalography (EEG), electrocardiography (ECG), Electrooculography (EOG) (recording eye movement), electro-olfactography (EOLG), and electromyography (EMG). In some preferred embodiments, at least one of EMG, EoG and ECG is used.
Muscle activation maps can be derived from repeated voluntary muscle activations. The IC analysis and machine learning procedure of the present embodiments can identify consistent building block activation patterns within and between participants. A further analysis of spontaneous muscle activations (e.g., smiles or other expressions, in case of facial muscles) can be used to classify muscle activation sources. This can optionally and preferably be used to extract consistent subject-specific activation, and also estimate inter-subject variability. The analysis can also be used to classify muscle activation sources for, for non-limiting example, expressions such as commanded smiling, spontaneous frowning and commanded frowning.
The present embodiments allow automated and objective mapping of, for example, facial expressions in general and in the assessment of normal and abnormal smiling in particular and, for another non-limiting example, muscle use in the forearm. Other parts of the body for which muscle activation can be mapped can include the upper arm, a leg, a torso, and the neck. The system can also be used on animals, for non-limiting example, on domestic pets, farm animals, guard animals and racing animals.
Other applications can include detecting use of illegal drugs, and detecting bombs and explosives. For example, the system of the present embodiments can be used during training of animals to detect bombs and explosives, and/or to identify muscular activity of the animal upon sensing existence of a bomb or explosive.
The electrode array optionally and preferably, together with the aforementioned IC analysis and machine learning procedure, establish a non-invasive and high-resolution approach to specific muscle detection and identification at the individual level. For example, once robust normal activation facial building blocks (FBBs) have been characterized, spontaneous, natural, and even clinical events can benefit from such findings. This can in turn be used in diagnostic, assessment and therapeutic purposes, as well as in human-machine-interface.
Unlike conventional techniques, the system and method of the present embodiments does not need to use visual methodologies, such as imaging, that require proper lighting and resolution. Thus, in various exemplary embodiments of the invention the system and method identify activation patterns of one or more active muscles without analyzing optical signals received from the body. This is advantageous because it does not demand a camera view of the subject under investigation. For example, when facial muscle activation patterns are desired, there is no need for a frontal full-face view of the face of the subject.
The system and method of the present embodiments can achieve deep and detailed muscle resolution that cannot be achieved by image analysis. This high-resolution capacity can provide, for non-limiting example, valuable information regarding synergetic muscle activity and precise identification of muscle sections.
Hereinbelow, a representative example is shown of the synergy of two spatially separable regions, such as smiling activating the Zygomaticus major (lower face) and Glabellar (Corrugator supercilii and Procerus) muscles (upper face) simultaneously. This observation is in line with the dual nerve supply from the frontal as well as the zygomatic and buccal branches of the facial nerve. After receiving innervation from the zygomatic branch, the buccal branch forms the angular nerve to supply the Glabellar muscles [Caminer D M, Newman M I and Boyd J B, Angular nerve: New insights on innervation of the corrugator supercilii and procerus muscles. J. Plast. Reconstr. Aesthetic Surg. 59 366-72, 2006; Yu M and Wang S-M, Anatomy, Head and Neck, Eye Corrugator Muscle. (StatPearls Publishing), 2019]. The far reaching application of such outcome can be used for accurate aesthetic and reconstructive surgery, e.g. denervation of Glabellar muscles from fibers of the frontal branch has shown low success and unpredictable results since the innervation is also supplied by the buccal and zygomatic branches. High-resolution neurophysiological preoperative evaluation in laboratory or natural setting may lead to better surgical decisions.
The system optionally and preferably comprises an electrode array to capture sEMG data. The sEMG data are transferred, preferably wirelessly, but possibly wiredly, to a processor having a circuit configured to run dedicated software. The processor can be local, can be remote and can be in the cloud. The software comprises an algorithmic solution to cluster the independent sEMG sources and to derive therefrom individual mappings for each participant. The individual mappings can be combined to identify robust building blocks (RBBs) which are associated with specific muscles. The patterns of RBB use can then determine patterns of use for muscle groups, individual muscles and portions of muscles for different types of activity utilizing the muscle groups, individual muscles and portions of muscles. For non-limiting example, RBB patterns can be used to distinguish between different facial expressions and even between different types of smiles. In other non-limiting examples, RBB patterns can distinguish between types of muscle use to move different fingers, and can be used to identify changes in muscle activation patterns in response to a mechanical load experienced by the respective muscle or muscles; and the RBBs at the diaphragm region can be used to distinguish between different types of breathing, for use as a diagnostic in determining onset of breathing difficulties and onset of increased severity of respiratory-related illnesses.
The IC procedure used by the technique of the present embodiments typically executes a blind source separation algorithm, such as, but not limited to, independent component analysis (ICA), fast independent component analysis (fastICA), principal component analysis, singular value decomposition, dependent component analysis, non-negative matrix factorization, low-complexity coding and decoding, stationary subspace analysis, common spatial pattern analysis and any combination thereof.
For use on the face, the IC procedure disclosed herein can capture activation of specific muscles in voluntary and spontaneous expressions.
When used on the face, the system of the present embodiments can comprise a hemi-facial electrode array over at least a portion of the upper and lower parts of the face. For use on the face, the IC procedure can extract derived data for each participant of a research group separately. Then, all individual mappings can be combined to identify robust FBBs which are associated with specific facial muscles. From these, a classification approach can determine FBB activation during spontaneous smiles.
Proper facial musculature activation is advantageous both for physiological needs (e.g. swallowing, chewing, speaking, eating or closing the eyes) and for social interactions (e.g. smiling, frowning). Many clinical disorders are manifested by abnormal facial activation patterns leading to physiological and psycho-social burden. In Parkinson's disease, for example, hypomimia; the reduction of spontaneous facial expression is a major challenge with severe esthetic and psychological ramifications [Argaud S, Delplanque S, Houvenaghel J-F, Auffret M, Duprez J, Vérin M, Grandjean D and Sauleau P, Does Facial Amimia Impact the Recognition of Facial Emotions? An EMG Study in Parkinson's Disease. ed S Kotz, PLoS One 11 e0160329, 2016; Bologna M, Berardelli I, Paparella G, Marsili L, Ricciardi L, Fabbrini G and Berardelli, A, Altered Kinematics of Facial Emotion Expression and Emotion Recognition Deficits Are Unrelated in Parkinson's Disease. Front. Neurol. 7 1-7, 2016]. Tourette syndrome, an opposite example, is typified by fast and repetitive involuntary facial movements in the form of tics [Brandt V C, Patalay P, Bäumer T, Brass M and Münchau A, Tics as a model of over-learned behavior-imitation and inhibition of facial tics. Mov. Disord. 31 1155-62, 2016; Muth C C, Tics and Tourette Syndrome. JAMA 317 1592, 2017]. Uncontrolled facial episodes of laughter or crying occur in amyotrophic lateral sclerosis [Thakore N J and Pioro E P, Laughter, crying and sadness in ALS. J. Neurol. Neurosurg. Psychiatry 88 825-31, 2017]. Abnormal facial muscle activation patterns appear in many other conditions such as hemifacial spasm (HFS), facial paresis, aberrant regeneration and synkinesis [Valls-Solé J and Montero J, Movement disorders in patients with peripheral facial palsy, Mov. Disord. 18 1424-35, 2003; Wang A and Jankovic J, Hemifacial spasm: Clinical findings and treatment. Muscle Nerve 21 1740-7, 1998; Yaltho T C and Jankovic J, The many faces of hemifacial spasm: Differential diagnosis of unilateral facial spasms. Mov. Disord. 26 1582-92, 2011]. In HFS, for example, involuntary and irregular movements occur in muscles innervated by the seventh cranial nerve [Yaltho]. Typical symptoms include “twitching” of the lower eyelid, followed by spasms of other facial muscles [Wang]. Damaged facial muscle activation may also be the result of cancer or trauma. Following tumor removal surgery, muscle activation may be damaged resulting in deterioration of speech, deglutition and dryness of the eyes [Shah J P and Gil Z, Current concepts in management of oral cancer—Surgery. Oral Oncol. 45 394-401, 2009; Eskes M, van Alphen M J A, Smeele L E, Brandsma D, Balm A J M, van der Heijden F, Alphen M J A Van and Smeele L E, Predicting 3D lip movement using facial sEMG: a first step towards estimating functional and aesthetic outcome of oral cancer surgery. Med. Biol. Eng. Comput. 55 573-83, 2017]. Facial plastic surgery and nerve grafting are challenged by the complex anatomy of facial musculature and nerve anatomy, especially in reanimation procedures such as smile reconstruction [Fattah A, Borschel G H, Manktelow R T, Bezuhly M and Zuker R M, Facial Palsy and Reconstruction. Plast. Reconstr. Surg. 129 340e-352e, 2012; Manktelow R T, Tomat L R, Zuker R M and Chang M, Smile Reconstruction in Adults with Free Muscle Transfer Innervated by the Masseter Motor Nerve: Effectiveness and Cerebral Adaptation. Plast. Reconstr. Surg. 118 885-99; 2006; Guntinas-Lichius O, Genther D J and Byrne P J, Facial Reconstruction and Rehabilitation. Advances in Oto-Rhino-Laryngology vol 78 pp 120-31, 2016].
In some embodiments, facial muscle activation during smiling is analyzed. It is appreciated that although smiling is ubiquitous, understanding its spatial structure and function is advantageous and is useful in psychological and neurological evaluation. Extensive investigations have demonstrated the importance and complexity of smiles in countless fields, ranging from human emotion perception and action, behavioral aspects, well-being, human-robot communication, security, lie detection and aesthetics to pathological manifestations [Ugail H and Aldahoud A A A, Computational Techniques for Human Smile Analysis. (Cham: Springer International Publishing, 2019); Ekman P, Telling lies. (New York-London: W. W. Norton & Company, 1985); Abel E L and Kruger M L, Smile Intensity in Photographs Predicts Longevity. Psychol. Sci. 21 542-4, 2010; Kraus M W and Chen T-W D, A winning smile? Smile intensity, physical dominance, and fighter performance. Emotion 13 270-9, 2013].
The Inventors found that conventional methods for facial muscle activation mapping are neither precise nor quantitative. A widely used method is the facial action coding system (FACS). FACS is based on observed displacements of facial features [Ekman P and Friesen W V., Measuring facial movement. Environ. Psychol. Nonverbal Behav. 1 56-75, 1976] and is used extensively in psychological and behavioral studies. Conventionally, FACS requires a trained human coder and lengthy analysis. Computational approaches, where image or video analysis are employed, have partially automated the process, yet require a vast amount of data to account for facial rotation, translation and scale invariance relative to camera location. Proper visual path and illumination are needed to reach high accuracy [Ugail; Barrett L F, Adolphs R, Marsella S, Martinez A M and Pollak S D, Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements. Psychol. Sci. Public Interes. 20 1-68, 2019].
Several computerized systems have been developed to measure facial movements by analyzing facial reflecting dots in optical motion systems [Hontanilla B and Aubá C, Automatic three-dimensional quantitative analysis for evaluation of facial movement. J. Plast. Reconstr. Aesthetic Surg. 61 18-30, 2008; Coulson S E, Croxson G R and Gilleard W L, Quantification of the Three-Dimensional Displacement of Normal Facial Movement. Ann. Otol. Rhinol. Laryngol. 109 478-83, 2000; Dusseldorp J R, van Veen M M, Mohan S and Hadlock T A, Outcome Tracking in Facial Palsy. Otolaryngol. Clin. North Am. 51 1033-50, 2018]. However, the Inventors found that these techniques lack muscle specificity.
Conventional EMG is known to elucidate muscle activation processes.
The system of the present embodiments provides a high-resolution sEMG that can be integrated easily with the body portion and be used, for non-limiting example, on the face, the arm, the leg, and the torso.
In some embodiments of the present invention the electrodes are in the formed of a patch which can be plugged into a miniature wireless Data Acquisition Unit (DAU) 258 that amplifies, digitizes, and transmits the signal using a standard wireless transmission protocol, such as, but not limited to, a Bluetooth protocol. The data can be displayed and stored on a computer 254 or a mobile device using dedicated software. The computer 254 or mobile device is preferably local to the DAU 258 but can be remote from the DAU 258. Data can also be stored in a cloud 262. The analysis can optionally and preferably include application of a machine learning procedure 264. Data analysis can be performed locally or in a cloud-based engine. The results can then be sent in real-time to a physician or health care provider for further evaluation and treatment.
Embodiments of the present invention can provide a means of recording biopotentials of the respiratory muscles (sEMGdi) and the heart (ECG) at a site remote from a clinician or medical setup, such as, but not limited to, a home or a quarantine facility using a proprietary disposable, dry, and flexible multi-electrode array patch applied to the chest region.
In some embodiments of analysis of facial activation, two hemi-facial electrode arrays are used, one for each side on the face.
As shown in
In an embodiment of analysis of sEMG signals, given a set of sEMG signals (observations) {right arrow over (x)}1(t), {right arrow over (x)}2(t), . . . , {right arrow over (x)}n(t), where t is the time and n is the number of electrodes, it can be assumed that they are generated as a linear mixture of independent components
where A is a mixing matrix and {right arrow over (s)}1(t), {right arrow over (s)}2(t), . . . , {right arrow over (s)}n(t) are the original signals, as generated by the muscles. A is a square matrix of size n×n. A blind source separation algorithm such as a fastICA algorithm can be applied to find the original signals, {right arrow over (s)}i(t), i=1, . . . , n, from the mixed observations {right arrow over (n)}i(t), i=1, . . . , n:
where W=A−1 is the un-mixing matrix of size n×n. Therefore, the muscle activity signals {right arrow over (s)}1(t), {right arrow over (s)}2(t), . . . , {right arrow over (s)}n(t) can be identified from the set of sEMG signals {right arrow over (x)}1(t), {right arrow over (x)}2(t), . . . , {right arrow over (x)}n (t) and their weight in each electrode. In an embodiment, the fastICA algorithm, using a MATLAB 2.5 package [Hyvarinen 1: Hyvarinen A, Karhunen J and Erkki O, Independent Component Analysis. (John Wiley & Sons), 2001], can be applied with a nonlinear fit for facial mapping. The nonlinear fit can be, for example, a polynomial fit. In experiments performed by the inventors a 3rd degree polynomial was employed, but other packages and/or other nonlinearity functions can be used for facial mapping, limb muscle mapping and torso muscle mapping.
For facial mapping, the electrode location and inverse un-mixing matrix, W, can be used to generate IC patterns for each facial calibration expression in each repetition separately. The machine learning (e.g., clustering) procedure can then be applied to classify the IC patterns and to construct a map which is specific to a activation of a particular muscle, or a combined maps in which activations of different muscles are distinguishable. In preferred embodiments, the fastICA needs not to be limited in the number of extracted output components, but will result in a number of components consistent with the number of electrodes. For the device as tested in the examples herein, 16 components were found, consistent with the 16 electrodes.
In some embodiments of the present invention the data processing flow is as follows. The signals from the electrodes are digitized to provide multidimensional sEMG data. The data are filtered, for example, with a notch or comb filter of about 50 Hz and a bandpass filter. Typically, the pass band is from about 5 to about 1000 Hz, more preferably from about 20 Hz to from about 500 Hz, but other bands are also contemplated. The bandpass filter is preferably applied to include physiologically relevant data and to reject low-frequency and high frequency noise. The sEMG sources are optionally and preferably calculated by applying blind source separation (e.g., fastICA) to the data. This provides a plurality of data components, one data component for each sEMG source.
The data components are optionally and preferably represented as digital vectors. The components can then be classified by applying a machine learning procedure, such as, but not limited to, a clustering procedure, to the components. In experiments performed by the inventors k-means clustering was employed. The K-means procedure employs a successive sequence of iterations so as to minimize a predetermined criterion, such as the sum of the squares of the distances from all the data points in the cluster to their nearest cluster centers. The k-means procedure is advantageous because the number of clusters can be determined a priori thereby reducing the complexity of the procedure. In some embodiments of the present invention the k-means procedure is executed for total of from about 5 to about 15 clusters. Other clustering procedures (hierarchical or partitional) such as, but not limited to, graph a clustering procedure which is based on graph theory, scale-space clustering, hard or fuzzy C-means clustering, minimal spanning tree clustering, and a clustering procedure which is based on Potts-spins, are also contemplated.
In various exemplary embodiments of the invention the clustering is according to the temporal and spectral signal properties of the components. The classified components can then be mapped spatially using the electrode positions as landmarks. In some embodiments of the present invention the centroids of the clusters are spatially resolved over the locations of the contacts of the electrodes. The cluster centroids are referred to as FBBs. Preferably, a map is constructed by marking activation patterns around the spatially resolved centroids. Typically, the patterns include contours defined at locations at which the muscle activations reach maxima within a predetermined tolerance (e.g., tolerance of from about 0.5 to about 3 standard deviations).
Typical results for facial activation are shown in the Examples section that follows, see
The un-mixing matrix, W, can change its column order every time the fastICA algorithm is applied. The inventors found that such a change can be resolved by applying clustering. In experiments performed by the inventors k-means clustering (k=8) was applied to group similar IC sources across repetitions and voluntary expressions. This utilized a cosine distance metric to compare each column in Wi to that of Wj, where i and j are different repetitions or expression segments. Explicitly, dpq=1−COS(θpq), where dpq and θpq are the distance and angle between column p in Wi and column q in Wj (treated as vectors), respectively. In addition to clustering similar sources to a single group, the clustering algorithm calculated the centroid's cluster for each cluster separately. Experimental results relating to these embodiments are provided in the Examples section that follows (see
The centroid center (over subjects) can be calculated for each group by averaging all contours in that group. This is illustrated in
In some embodiments, the classification algorithm relies on a k-nearest neighbor algorithm to derive the relevant FBBs for each spontaneous IC map. The classification algorithm can utilize a cosine distance metric (as detailed for the clustering algorithm above). Preferably the closest neighboring FBBs, whose distance values from the centroid center were less than a predetermined distance threshold are classified to construct a spontaneous IC source.
In experiments performed by the inventors, the predetermined distance threshold was set to be 0.35. Results of these experiments are shown in
Each FBB can be either activated or not in a single facial expression (e.g., smile). In some embodiments, an FBB score is calculated as the number of activation occurrences divided by the number of single facial expressions in that category for each participant separately. Thus this score varies between 0 (not activated in any single facial expressions in a category) and 1 (activated in all single facial expressions in that category).
The terms “comprises”, “comprising”, “includes”, “including”, “having” and their conjugates mean “including but not limited to”.
The term “consisting of” means “including and limited to”.
The term “consisting essentially of” means that the composition, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.
As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.
Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.
It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.
Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.
Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the invention in a non limiting fashion.
In a test of the electrode array, the left (
In the test, the array was adhered to the right side of the faces of 13 volunteer subjects (age: 31.77±7.11 years; 9 females,) after a mild skin cleaning and exfoliation. A commercial ground plate electrode (Natus Medical Incorporated; 019-409100) was positioned at the back of the neck. The array application took ˜2 min and the recorded signals stabilized after ˜2 min in all 16 channels for all participants.
In some embodiments, no separate ground electrode is needed, as the ground electrode is part of the sensing device applied to the skin. The sensing device can comprise a sensing electrode that be bipolar, a non-sensing ground electrode and any combination thereof.
In some embodiments, a ground contact is placed in other positions, for example, as an additional electrode on the face, ear or other body part. In some embodiments, the electrodes are bipolar and no ground electrode is used. The ground electrode, if used, need not be the electrode used in the test. Any commercial or proprietary electrode can be used as the ground electrode.
In this test, the software code was implemented using LabVIEW 2012 or 2017 and MATLAB R2015a. In other embodiments, any commercial or proprietary analysis software can be used.
In a test of the system, analysis of facial expressions using the electrode array of
In a test of the system, in an introductory step, the user is shown a sample of photographs and text of four expressions: a voluntary-smile (
In a test of the embodiment, thirty-three videos were presented during the spontaneous step (duration range: 5 to 39 s) separated by 7 s of a blank slide (total time was 15 min 23 s). The number of videos can range from 1 to 100, and the length of each video range from 5 s to 10 min. More than one expression can be elicited during a single video.
Users were instructed to watch the videos and react spontaneously. In addition, users were instructed to perform facial expressions as a response to written command tasks shown on the screen (e.g. “Smile as if you saw your best friend”). This step involved three types of scene: (1) funny episodes (i.e. funny) (Nf=16); (2) individuals smiling to the camera (i.e. mimicry) (Nm=12); and (3) written instructions to smile (i.e. command) (Nc=5). Nf, Nm, and Nc can each be in the range from 5 to 30, with the number of videos for each expression dependent on the total number of videos used. Preferably, the number of is approximately the same for the different types of facial expression.
In the test, sEMG and the videos were recorded simultaneously for later evaluation. In use, only sEMG is necessarily recorded; video or other visual recordings can also be made. For the visual recordings during the test, the users were laterally photographed; these photographs included images taken of neutral facial expression for later analysis (used for the blind source separation algorithm step).
In this test, data analysis was performed using MATLAB R2017ab and R2018b. sEMG data were recorded with a sampling rate of 3000 samples/s; the sampling rate can be in a range from 2000 samples/s to 5000 samples/s. Data were filtered using a 50 Hz comb filter and a bandpass 4 order Butterworth filter in the frequency range of 20-500 Hz. Other filters and filtering frequencies can be used in other embodiments. Any commercial or proprietary data analysis software with the appropriate capabilities can be used Examples include, but are not limited to, HubSpot Analytics, Qlik, Alteryx, NumPy, Stata, PARIS, Base SAS, SAS Enterprise Miner, HPE Vertica and SAS/STAT.
sEMG segments were cut as follows: Calibration step: 3 s before voluntary task instructions commencement and 3 s after termination. Spontaneous step: 1 s before video commencement and 6 s after video termination. The adapted fastICA was applied for the 16 single-channel sEMG data for each voluntary facial expression (six repetitions) separately and for each video segment separately. Different pre-step and post-step cut times different numbers of repetitions can be used in other embodiments. Pre-step and post-step cut times can be in the range from 0 s to 20 s and the number of repetitions can be in the range from none (once for at least one repetition) to 20 repetitions.
The printed hemifacial 16 electrode array disclosed herein has a high inter-electrode density to cover both upper and lower lateral parts of the face. The same electrode array layout was used for all participants (N=13). In the calibration step, each participant sat in a relaxed upright position and was instructed to perform four voluntary expressions (described by photographs and text on a computer screen). Typical sEMG results are shown in
While the data in
In addition to the primary sources, fastICA also revealed additional secondary sources for each expression (identified by a weight in each electrode, cells of columns in the un-mixing matrix, W). A clustering algorithm groups similar IC sources across repetitions and voluntary expressions.
In a test of the system, the mathematical scheme described above was repeated for each of the 13 participants in the test and the derived clusters were grouped together. This procedure resulted in 10 distinct clusters (numbered by roman numerals I-X in
In this test, 10 consistent FBBs were found (
Number of participants (out of 13) that activated a specific (well separated) FBB during the calibration step. Facial muscles were identified for each FBB. The 10 FBBs are depicted in roman numerals (I-X)
With an objective process to derive individual (
A summary of all normalized FBBs distributions per category is depicted in
For facial activation, the system of the present invention can identify and cluster sEMG IC sources from voluntary facial activation and can consistently classify them while allowing participants to freely move their heads and faces.
For facial activation, the system can robustly identify 10 separate activation FBBs and associate them to six facial muscles and their sections. The smile signature appears to be similar among smile types, comprising the activation of the Zygomaticus major with or without the Orbicularis oculi.
In another test, a high-resolution electrode array combined with statistical analysis enabled robust classification of complex forearm muscle activity at varying contraction levels.
The high-resolution electrode array 2000 was used to record sEMG from the forearm (
In order to improve the accuracy of the weighting of the electrode responses, the muscle contraction level during finger flexion was measured using a force gauge 2100, for middle finger flexion against a spring.
As shown in
Muscle unit action potential (MUAP) activity can be detected after applying ICA.
The classified ICs (muscle activity sources) were sorted using a similarity metric. They were mapped onto the electrode positions according to their fastICA mixing matrices.
Given a set of tasks which utilize flexor muscles in the forearm (flexor carpi radialis, flexor pollicis longus, flexor digitorum superficialis, flexor digitorum profundus, flexor carpi ulnaris, pronator teres and palmaris longus) it is possible to show the spatial distribution of extracted independent components over the area covered by the electrodes.
As shown in
The system of the present embodiments can be used hemi-facially or bilaterally, e.g., on only one side of the face or on both sides; similarly, electrodes can be attached to one limb or both, and to one side or both sides of a portion of a torso.
The system of the present embodiments can be used in sports for determining muscle fatigue, for performance training, for rehabilitation, for determining muscle pain and any combination thereof. If a parameter, for non-limiting example, pain, is outside at least one predetermined limit, a warning can be provided. The warning can be provided visually, audibly or tactilely, and can be provided to a user, to another person, stored in a database, and any combination thereof.
The system of the present invention can be used in relation to plastic surgery, for improvement of facial symmetry, during rehabilitation physiotherapy and any combination thereof. Again, a warning can be provided if a parameter is outside at least one predetermined limit. The warning can be provided visually, audibly or tactilely, and can be provided to a user, to another person, stored in a database, and any combination thereof.
The system of the present invention can be used to determine drug toxicity, as a biomarker, to identify bruxism and any combination thereof. A facial change can be a marker for a disease, a stroke, paralysis, brain damage, a brain tumor and any combination thereof.
The system of the present invention can be used in neurorehabilitation and for characterization of walking, assessment of post-stroke and post-spinal cord injury motor recovery, spasticity assessment, biofeedback and “serious games”, study of muscle synergies, control of prosthesis, exoskeletons and robots, body-machine interfaces, non-invasive extraction of neural control strategies, myoelectric manifestations of muscle fatigue, cramps, and any combination thereof.
The system of the present invention can be used before, during or after a surgical intervention.
The electrode set of the present embodiments can be a sticker, a temporary tattoo, or any other method of soft adhering electrodes that can be positioned at a specific portion of a body. Preferably, the electrode set is temporarily adhered to the body. In some embodiments, the electrode set can remain functional on the body for a period of up to one week.
In some embodiments, the detected muscle activation can be used to control a robot or a smart appliance.
The connection between an electrode set and the system comprising the analysis software can be wired or wireless. Preferably, the connection is wireless.
Other embodiments of the system can provide home-based monitoring of patients with ailments affecting the respiratory and cardiovascular functions, such as, but not limited to, corona virus disease 2019 (COVID-19), pneumonia, and influenza.
In such embodiments, the system comprises a telemetric device to monitor respiratory and cardiac measures of the patients at early stages of the disease, such as while quarantined at home or at a dedicated quarantine center. The device is designed to provide an alert for a transition from a mild to a severe manifestation of the disease, at the point where at least one of the patient's respiratory and cardiovascular function begins to deteriorate. The technology can provide real-time data to assist a clinician in the decision on whether or not to provide more aggressive treatment, such as, for non-limiting examples, transferring a remote patient to a hospital or transferring a patient in a hospital to an intensive-care ward.
An example of an application of such embodiments is provided by the COVID-19, a pandemic that creates a tremendous load on healthcare systems worldwide. The clinical spectrum of the disease varies from asymptomatic to conditions that involve respiratory failure, such as in severe acute respiratory distress syndrome (ARDS), which necessitates mechanical ventilation and support in an intensive care unit (ICU). ARDS is characterized by the development of acute shortness of breath (dyspnea) and deficiency of oxygen in the blood (hypoxemia) within hours to days of an inciting event. Physical findings of ARDS often are nonspecific and include abnormal rapid breathing (tachypnea) and heartrate (tachycardia). Fever, which may increase the heart rate, is associated with severe dyspnea, but it can be moderate or even absent. Moreover, cardiovascular implications are also reported in the disease especially in, but not limited to, patients with preexisting cardiovascular disease. COVID-19 has been associated with multiple direct and indirect cardiovascular complications including myocardial injury, myocarditis, arrhythmias and venous thromboembolism (Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Centers for Disease Control and Prevention www(dot)cdc(dot)gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients(dot)html (2020)). The Chinese Center for Disease Control and Prevention (CDC) report divided the clinical manifestations of COVID-19 by severity: mild, non- and mild pneumonia, accounted for 81% of cases; severe, characterized by development of ARDS with respiratory frequency increase above 30 cycles per minute, occurred in 14% of cases; critical, characterized by respiratory failure, septic shock, and/or multiple organ dysfunction, occurred in 5% of cases. Other reports suggest that over half of the patients with confirmed COVID-19 and pneumonia develop dyspnea 5-13 days after illness onset, and that ARDS develops in 17-29% of hospitalized patients (Kangelaris, K. N. et al. Timing of intubation and clinical outcomes in adults with acute respiratory distress syndrome. Crit. Care Med. 44, 120-129 (2016)). Though severe coronavirus cases have been reported among younger and middle-aged adults, older adults, the elderly and those with chronic health conditions seem to be most at risk for the sudden decline that leads to ARDS. This seems to be the crucial phase of the disease. From this point onwards there may be a rapid deterioration of respiratory functions (Cascella, M., Rajnik, M., Dulebohn, S. C. & Di Napoli, R. Features, Evaluation and Treatment Coronavirus (COVID-19). (StatPearls Publishing LLC, 2020)), and timing for intubation and mechanical ventilation critical, since late intubation is associated with increased mortality (Cabral, E. E. A. et al. Surface electromyography (sEMG) of extradiaphragm respiratory muscles in healthy subjects: A systematic review. J. Electromyogr. Kinesiol. 42, 123-135 (2018)). Once diagnosed, patients with a mild clinical manifestation are quarantined at home or at dedicated quarantine centers. Thus, it is of extreme importance to remotely monitor their respiratory and cardiovascular functions, to detect early signs of deterioration, and to assist the clinician to decide on whether and when to transfer a patient to a hospital or other more intensive care facility.
The respiratory muscles are vital to produce adequate ventilation and gas exchange. Contraction of the respiratory muscles creates a negative pressure gradient that results in inflow of air into the lungs. The diaphragm performs the largest portion of the inspiratory process, together with several other muscles which contribute to inspiration and expiration (Gibson, G. J. et al. ATS/ERS Statement on respiratory muscle testing. Am. J. Respir. Crit. Care Med. 166, 518-624 (2002)). EMG signals can be analyzed to determine normal and abnormal function of the neuromuscular system, including the respiratory muscles (Luo, Y. M. & Moxham, J. Measurement of neural respiratory drive in patients with COPD. Respir. Physiol. Neurobiol. 146, 165-174 (2005)).
EMG monitoring of the respiratory muscles has been evaluated in a variety of clinical and experimental scenarios. Traditionally, quantifying the diaphragmatic electromyogram (EMGdi) activity has been performed by using a multipair esophageal electrode catheter that is swallowed by patients (also called transesophageal EMGdi; esEMGdi) (Wu, W. et al. Correlation and compatibility between surface respiratory electromyography and transesophageal diaphragmatic electromyography measurements during treadmill exercise in stable patients with COPD. Int. J. COPD 12, 3273-3280 (2017)). This technology is invasive and leads to discomfort during EMGdi detection. Furthermore, the complex operation and the discomfort experienced by patients reduces follow-up visits and increases the loss rate. On the other hand, as discussed above, sEMG decreases the pain associated with the procedure, increases patient compliance and can provide continuous monitoring. Moreover, surface inspiratory EMG activity, recorded from the diaphragm (sEMGdi), the parasternal intercostal muscle (sEMGpara), and from the sternocleidomastoid (sEMGsc) are closely related to the esEMGdi. Therefore, surface measurements of respiratory muscle activity are a reliable method of detecting breathlessness and respiratory disorders, such as are manifested in COVID-19. In the prior art, however, sEMG still required the presence of a clinician or other formal medical setup.
Moreover, as COVID-19 has implications for the cardiovascular system as well, ongoing monitoring of the heart, as in ECG can be crucial for evaluating the severity of the disease progression.
An exemplary embodiment of measuring the sEMG signal is shown in
An exemplary embodiment of measuring the ECG is shown in
The sEMG data and the ECG data can be analyzed as disclosed above, and an alert, typically at a remote location but, in some embodiments, at a local location, can be provided at such time as at least one of the heart function data and the muscular function data show a change indicating increased severity of the illness and a need for further intervention, as discussed above.
Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.
It is the intent of the applicant(s) that all publications, patents and patent applications referred to in this specification are to be incorporated in their entirety by reference into the specification, as if each individual publication, patent or patent application was specifically and individually noted when referenced that it is to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority document(s) of this application is/are hereby incorporated herein by reference in its/their entirety.
This application is a Continuation of PCT Patent Application No. PCT/IL2021/050360 having International filing date of Mar. 30, 2021, which claims the benefit of priority under 35 USC § 119(e) of U.S. Provisional Patent Application No. 63/001,589 filed on Mar. 30, 2020. The contents of the above applications are all incorporated by reference as if fully set forth herein in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63001589 | Mar 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/IL2021/050360 | Mar 2021 | US |
| Child | 17952387 | US |
