DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities:
The amendment filed 15 October 2024 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure [“This application is a national stage application under 35 U.S.C. 371 and claims the benefit of PCT Application No. PCT/EP2023/060074 having an international filing date of 19 April 2023, which designated the United States, and which PCT application claimed the benefit of European Patent Application No. 22382360.0 filed 19 April 2022, the contents of each of which are incorporated by reference in their entireties” (emphasis applied)]. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: incorporation(s) by reference to foreign priority document(s) when added by amendment at the time of entry to the national stage is/are considered new matter [An incorporation by reference statement added after an application’s filing date is not effective because no new matter can be added to an application after its filing date (see 35 U.S.C. 132(a)) (MPEP § 608.01(p)(I)(B)); An international application designating the U.S. has two stages (international and national) with the filing date being the same in both stages. Often the date of entry into the national stage is confused with the filing date. It should be borne in mind that the filing date of the international stage application is also the filing date for the national stage application (MPEP § 1893.03(b))]]. The Applicant is required to cancel the new matter in the reply to this Office Action.
Appropriate correction is required.
Claim Objections
Claim(s) 1 and 7 is/are objected to because of the following informalities:
Claim 1 should read “the respective area” [line 4].
Claim 1 should read “normality model.[[.]]” [line 39, wherein the Examiner notes that the amendments filed 15 October 2024 fail to strikethrough the period in the originally filed claims, such that the amended period is considered a duplicate].
Claim 7 should read “computer-implemented” [line 1].
Claim 7 should read “the respective area” [line 11].
Claim 7 should read “physiological variables;[[:]]” [lines 13-14, wherein the Examiner notes that the amendments filed 15 October 2024 fail to strikethrough the colon in the originally filed claims].
Claim 7 should read “classifying the obtained variables” [line 32].
Appropriate correction is required.
Claim Interpretation
Examiner Notes: currently, NO limitation invokes interpretation under § 112(f).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Representative claim(s) 1 [representing all independent claims] recite(s):
A system for assessment of body movement's conditions or disorders, comprising:
several monitoring sensors, each one being configured to be attached to a different area of a body of a person to obtain one or more variables of the area according to a specified sensor configuration, the one or more variables including at least one of biomechanical and physiological variables, and the monitoring sensors comprising at least three sensors selected from the group consisting of: a heart rate sensor, an inertial sensor or a goniometer, and a plantar pressure sensor;
a memory or database, having stored therein at least one of:
control variables, obtained during different sessions of a same or different duration from healthy users while they performed a function test using the monitoring sensors, and
pathology variables, obtained during different sessions of a same or different duration from unhealthy users while they performed the function test using the monitoring sensors, the unhealthy users suffering a given body movement condition or disorder;
either or both of the control variables and the pathology variables for each session being stored classified in at least three different categories including cardiac, kinematics and plantar-pressure; and
a processing unit operatively connected to the memory or database, the processing unit being configured to:
generate a normality model for the given body movement condition or disorder by implementing a statistical-based feature selection process on either or both of the stored control variables and the pathology variables, the generated normality model configured to define a unified category score for each category of the at least three different categories, each unified category score outlining the variables that better characterize the given body movement condition or disorder;
obtain variables from a given user while the given user performed the function test during a given session using the monitoring sensors, the given user suffering the given body movement condition or disorder;
classify the obtained variables of the given user into the at least three different categories, and select, for each category, variables of the given user by considering the variables that better characterize the given body movement condition or disorder from the generated normality model;
compute, for the selected variables of the given user, a unified category score for each category; and
compute a condition or disorder score for the given user as the deviation between the computed unified category scores of the given user with the generated normality model.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 1 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper:
“having stored therein at least one of: control variables, obtained during different sessions of a same or different duration from healthy users while they performed a function test using the monitoring sensors, and pathology variables, obtained during different sessions of a same or different duration from unhealthy users while they performed the function test using the monitoring sensors, the unhealthy users suffering a given body movement condition or disorder; either or both of the control variables and the pathology variables for each session being stored classified in at least three different categories including cardiac, kinematics and plantar-pressure ” – which may be performed in the mind or by hand by merely memorizing or writing down at least a limited amount of data; wherein the Examiner notes that the language regarding certain variables having been obtained during different sessions is not considered to positively recite any step of using the monitoring sensors to measure data and are merely considered to define the type of data and where it came from
“generate a normality model for the given body movement condition or disorder by implementing a statistical-based feature selection process on either or both of the stored control variables and the pathology variables, the generated normality model configured to define a unified category score for each category of the at least three different categories, each unified category score outlining the variables that better characterize the given body movement condition or disorder” – may be performed by merely observing at least a limited amount of obtained or previously collected data or information and applying known or derived mathematical formulas or equations to the limited data
“obtain variables from a given user while the given user performed the function test during a given session using the monitoring sensors, the given user suffering the given body movement condition or disorder” – may be performed by merely observing at least a limited amount of obtained or previously collected data or information
“classify the obtained variables of the given user into the at least three different categories, and select, for each category, variables of the given user by considering the variables that better characterize the given body movement condition or disorder from the generated normality model” – may be performed by merely observing at least a limited amount of obtained or previously collected data or information and drawing mental conclusions therefrom
“compute, for the selected variables of the given user, a unified category score for each category” – may be performed by merely observing at least a limited amount of obtained or previously collected data or information and drawing mental conclusions therefrom based on known or derived relationships
“compute a condition or disorder score for the given user as the deviation between the computed unified category scores of the given user with the generated normality model” – may be performed by merely observing at least a limited amount of obtained or previously collected data or information and drawing mental conclusions therefrom based on known or derived relationships
If a claim, under BRI, covers performance of the limitations in the mind but for the mere recitation of extra-solutionary activity (and otherwise generic computer elements) then the claim falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1 of the Mayo framework as set forth in the 2019 PEG.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) [i.e., “generate a normality model for the given body movement condition or disorder by implementing a statistical-based feature selection process on either or both of the stored control variables and the pathology variables, the generated normality model configured to define a unified category score for each category of the at least three different categories, each unified category score outlining the variables that better characterize the given body movement condition or disorder” (statistical analysis), “compute, for the selected variables of the given user, a unified category score for each category”, “compute a condition or disorder score for the given user as the deviation between the computed unified category scores of the given user with the generated normality model”] to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts [Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)]. The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea [e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process] and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data [Int. Ventures v. Cap One Financial], collecting information, analyzing it, and displaying certain results of the collection and analysis [Electric Power Group], collection, storage, and recognition of data [Smart Systems Innovations].
Step 2A Prong 2
The judicial exception is not integrated into a practical application.
Representative claim 1 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity [generic computer function; wherein while the Examiner notes that as analyzed above, the recitation of the monitoring sensors is not a positive recitation of the “use” of the monitoring sensors for measuring any control or pathology variables, for the sake of compact prosecution, the Examiner notes that if the monitoring sensors were positively recited for measuring control and/or pathology variables, the use of the monitoring sensors would be considered pre-solution data gathering] – without further sufficient detail that would tie the abstract portions of the claim into a specific practical application (2019 PEG p. 55 – the instant claim, for example does not tie into a particular machine, a sufficiently particular form of data or signal collection – via the claimed extra-solution activity identified above, or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 3-4, 6, 9-11, and 13 merely add detail to the abstract portions of the claim but do not otherwise encompass any additional elements which tie the claim(s) into a particular application/integration [the dependent claim(s) recite generic ‘units’ or ‘steps’ which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
Dependent claim(s) 11 and 14 encounter substantially the same issues as the independent claim(s) from which they depend in that they encompass further generic extra-solutionary activity [generic data gathering] and/or generic computer elements [storage, memory per se].
Accordingly, the claim(s) are not integrated into a practical application under Step 2A Prong 2.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 7 as individual wholes fail to amount to significantly more than the judicial exception at Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of extra-solutionary activity [i.e., generic computer function, pre-solution data gathering] and generic computer elements cannot amount to significantly more than an abstract idea [MPEP § 2106.05(f)] and is further considered to merely implement an abstract idea on a generic computer [MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality].
For the independent claim portions and dependent claims which provide additional elements of extra-solutionary data gathering, MPEP § 2106.05(g) establishes that mere data gathering for determining a result does not amount to significantly more. The extra-solutionary activity of processor steps [acquiring, storing signals, etc.] as presently recited, cannot provide an inventive concept which amounts to significantly more than the recited abstract idea.
For the independent claims as well as the dependent claims merely reciting generic computer elements and functions [a memory or database, a processing unit, non-transitory computer-readable medium storing instructions, at least one processor, computing system, each recited at a high level of generality and corresponding functions therein], MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality.
Accordingly, the generic computer elements and corresponding functions therein, as presently limited, cannot provide an inventive concept since they fall under a generic structure and/or function that does not add a meaningful additional feature to the judicial exception(s) of the claim(s).
Claim 1 recites “several monitoring sensors, each one being configured to be attached to a different area of a body of a person to obtain one or more variables of the area according to a specified sensor configuration, the one or more variables including at least one of biomechanical and physiological variables, and the monitoring sensors comprising at least three sensors selected from the group consisting of: a heart rate sensor, an inertial sensor or a goniometer, and a plantar pressure sensor”, wherein claim 1 further recites that the memory stores at least one of “control variables” and/or “pathology variables” that are recited as having been obtained during sessions where a user or users using the monitoring sensors, which the Examiner notes is not considered to positively recite the claimed system using the monitoring sensors to measure variables, but for the sake of compact prosecution is further analyzed at Step 2B. Claim 7 recites “each one of the different monitoring sensors, for each session and for each healthy and unhealthy user, being configured to be attached to a different area of a body to obtain one or more variables of the area according to a specified sensor configuration, the one or more variables including at least one of biomechanical and physiological variables”, which the Examiner notes fails to positively recite the sensors being used in the claimed method, but for the sake of compact prosecution, the sensors as recited in claim 7 are further analyzed at Step 2B. Claims 2 and 8 further limit claims 1 and 7, respectively, and further limit the monitoring sensors as “selected from the group consisting of: a heart rate sensor, an electromyography sensor, an inertial sensor or a goniometer, and a plantar pressure sensor”. Such a combination of monitoring sensors is considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed monitoring sensors, and recites the monitoring sensors at a high level of generality [The invention integrates various (portable) monitoring sensors (e.g. heart rate, electromyography, inertial or goniometer, plantar pressure) to acquire different biomechanical and/or physiological parameters/variables… The monitoring sensors are attached to different areas of the body of a subject to obtain biomechanical and/or physiological variables of said area (Applicant’s Specification p. 9:1-7); Table 1, p. 12]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the field of biomechanical and physiological monitoring. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Kim (US-20160324445-A1) [The first sensor 520 may be an inertia sensor module, which may detect a change in an angle of the user's foot, and may include a 3-axis acceleration sensor and a 3-axis gyro sensor and have 6 degrees of freedom (Kim ¶0122); The second sensors 530a, 530b, 530c, and 530d may correspond to pressure sensors that may detect the change in the pressure of the user's sole applied to the ground, and may measure a change in resistance and capacitance and calculate the pressure so as to determine a landing of the feet during the gait and detect a pressure distribute of each part of the sole at a moment of the landing (Kim ¶0123); According to various embodiments of the present disclosure, in addition to the first sensor 520 and the second sensors 530a, 530b, 530c, and 530d, various sensors that may detect user's body information (for example, a user's blood pressure, blood flow, heart rate, body temperature, respiration rate, heart and lung sound, electromyogram, ECG, and the like) may be further included. The various sensors may include at least one of a heart rate variability (HRV) sensor, a heart rate monitor (HRM) sensor, an EMG sensor, an EEG sensor, an ECG sensor, an IR sensor, and an E-nose sensor. Further, it is preferable that the first sensor 520, the second sensors 530a, 530b, 530c, and 530d, and the various sensors are located around the user's feet, but the present disclosure is not limited thereto, and embodiments of the present disclosure may further include an additional sensor module located at another part (for example, user's wrist, shoulder, chest, head, and the like), which is not the part around the user's feet (Kim ¶0124)]
Rayner (US-20140031703-A1) [In order to monitor a participant's exertion or level of performance in an activity or event, various types of sensors or data collection devices can be used. Sensors that can be used include a clock, a timer, a stop watch, a motion sensor, a speed sensor, a pedometer, a cadence sensor, an accelerometer, a power meter, a mass sensor, an inertia sensor, a wind resistance sensor, a rolling resistance sensor, a pressure sensor, a strain gauge, a hear rate monitor, a thermal sensor, a compass, a magnetic sensor, a gravity sensor, a gyroscope, a global navigation satellite system (GNSS) receiver, a global positioning system (GPS) receiver, an altitude sensor, a humidity sensor, an acoustic sensor, a photo detector sensor, and the like. Other types of sensors or data collectors are possible and the techniques disclosed herein are not to be limited to any particular type of sensor or data collector (Rayner ¶0023); The one or more electronic devices 510 can be worn by the runner via a band, or in a specially-formed vest, belt, or other apparel such as shorts or a shirt. The one or more electronic devices 510 and/or sensors 520 can be worn on the runner's wrist, lower arm, upper arm, waist, chest, back, leg, foot, or ankle. In other situations, the one or more electronic devices 510 and/or sensors 520 may be worn on the runner's hands, shoes, or head. In some situations, the specific positioning of the electronic device or sensor may be chosen to sense movement of a specific part of the body or appendage (Rayner ¶0132)]
Huijbregts (US-20190183412-A1) [exercise state providing unit 10 comprises an optical heart rate (OHR) sensor which is to be integrated in earbuds for playing music, or the like. The OHR can determine a heart rate of the subject based on optical measurements (Huijbregts ¶0078); exercise state providing unit 10 further comprises at least one accelerometer which is attachable to at least one of the wrist, the earbud, a chest strap or a shoe of subject 7. Since the accelerometer is provided and in this example attached next to the OHR sensor, motion-induced noise can be filtered from the signal in order to obtain the correct heart rate of subject 7. This applies further to the alternative example in which an optical heart rate sensor is attached to the wrist instead of the earbud described in this example (Huijbregts ¶0079); In other examples, exercise state providing unit 10 can provide additional or alternative sensors which include a gyroscope, a magnetometer and a barometer. This list is of course not limited to the examples given above, and alternative or additional sensors can be provided in other examples. For instance, pressure sensors can be provided in a shoe of subject 7 to determine where on the foot a landing takes place for each step. Further, in other examples, EMG sensors can be provided which measure the electrical activity produced by skeletal muscles (Huijbregts ¶0080)]
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 1 and 7 recite subject matter regarding “computing a condition or disorder score for the given user as the deviation between the computed unified category scores of the given user with the generated normality model, which the Examiner notes is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language that is considered to be a particular treatment or prophylaxis as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [MPEP § 2106.04(d)(2)] [merely providing a “diagnosis” is not considered to be a particular treatment or prophylaxis and the computation of a “diagnosis” is considered to be recited within a limitation that is considered to be an abstract idea].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 5-8, and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murphy (US-11194888-B1) in view of Chen et al. (“Plantar Pressure-Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review”, NPL attached), hereinafter Chen.
Regarding claim 1, Murphy teaches
A system for assessment of body movement's conditions or disorders, comprising:
several monitoring sensors, each one being configured to be attached to a different area of a body of a person to obtain one or more variables of the area according to a specified sensor configuration, the one or more variables including at least one of biomechanical and physiological variables, and the monitoring sensors comprising at least three sensors selected from the group consisting of: a heart rate sensor, an inertial sensor or a goniometer, and a pressure sensor [Each of the body-mountable devices 110a-d of the system 100 includes at least one sensor that monitors motion of a person. Such a sensor could be configured to generate a signal related to motor activity of the wearer 105, e.g., to a motion, rotation, acceleration, pulse rate or other cardiovascular property, exerted force or pressure, or other properties of the body part to which the body-mountable device is mounted (Murphy Col 5:61-6:1); A variety of signals related to motor activity of a person may be detected, using the methods and systems described herein, and used to determine whether to prompt the person to perform a diagnostic motor task. Such signals may be related to motion of parts of the person's body (e.g., acceleration, rotation, location of parts of the person's body), physiological properties of the person's body (e.g., heart rate, breathing rate, oxygen saturation), sounds generated by the person (e.g., during speech), forces exerted by the person (e.g., to push an object, to type on a keyboard, to interact with a touch screen), or some other properties related to motor activities of the person (Murphy Col 14:41-52); An acceleration, a rotation, a force (e.g., a force between a person's foot and the floor), a location, or some other signal related to one or more body parts of the person could be detected and used to assess the performance of such a motor task, e.g., to determine a severity or degree of progression of a movement disorder or other disease state or process (Murphy Col 19:3-9)];
a memory or database [The health model could be implemented as electronics (e.g., as a microcontroller executing instructions stored in a memory) disposed in one or more of the devices (e.g., 110a, 110b, 110c, 120) of the system 100. For example, a microprocessor of the cell phone 120 could operate to implement the health model and/or to perform processes thereof by executing instructions or other information downloaded from the internet. Additionally or alternatively, the health model could be implemented on a server, on a personal computer, as instructions executed in a cloud computing service, or on some other system in communication with the system 100 (Murphy Col 12:65-13:9)], having stored therein at least one of:
control variables, obtained during different sessions of a same or different duration from healthy users while they performed a function test using the monitoring sensors, and
pathology variables, obtained during different sessions of a same or different duration from unhealthy users while they performed the function test using the monitoring sensors, the unhealthy users suffering a given body movement condition or disorder;
either or both of the control variables and the pathology variables for each session being stored classified in at least three different categories including cardiac, kinematics and pressure [Measured properties or processes from a particular person may be compared to population norms and/or to previously measured information from the particular person in order to diagnose a disease, determine a disease state or progression, or determine some other health information about the particular person (Murphy Col 1:23-29); The health model could additionally or alternatively be based on epidemiological or other medical information about a population of persons, e.g., a population of persons that are related to a wearer according to demographics. The health model could be updated based on changes in such information, e.g., based on additional data received from a population of wearers of the devices and systems described herein (Murphy Col 13:64-14:4); Murphy Col 14:41-52, wherein the signals being characterized by the sensor type is considered to read on the signals being classified by category, and wherein comparative population data for determining progression or state of a disease or health state is considered to read on control population variables and pathology population variables, in order to allow for the determination of progression between healthy and a progressed state of the disease or health state]; and
a processing unit operatively connected to the memory or database [Murphy Col 12:65-13:9], the processing unit being configured to:
generate a normality model for the given body movement condition or disorder by implementing a statistical-based feature selection process on either or both of the stored control variables and the pathology variables, the generated normality model configured to define a unified category score for each category of the at least three different categories, each unified category score outlining the variables that better characterize the given body movement condition or disorder [Measured properties or processes from a particular person may be compared to population norms and/or to previously measured information from the particular person in order to diagnose a disease, determine a disease state or progression, or determine some other health information about the particular person (Murphy Col 1:53-28); A device or system of devices (e.g., a cell phone, a watch, a wearable health device) could operate to detect signals relevant to the disease state or process (e.g., to detect accelerations or rotations related to tremor, locomotion, or other motor activity that may have properties related to the disease state or process) over a protracted period of time (e.g., during most of the day). These detected signals could be used to detect a change in a symptom of the disease state or process or to detect some other property of a person's physiological and/or behavioral state (Murphy Col 3:46-55); Additionally or alternatively, it could be determined, based on sensor signals recorded from a population of wearers, that the events are related to the health state of interest. For example, to assess the presence, progression, or other properties of a movement disorder (e.g., Parkinson's disease, dystonia, essential tremor, chorea, dyskinesia, or some other movement disorder), the events could include discrete motions or actions, e.g., footsteps, turns of the wearer's body, reaches or other arm motions, or other motions or actions engaged in by the wearer 105 (Murphy Col 9:45-55); The structure, parameters, or other properties of the health model could be determined based on past information from one or more wearers, e.g., the health model could provide an output that is predictive of whether a wearer's health state is significantly different from the wearer's health state in the past and this output could be used to determine that the wearer should seek medical attention, take a drug, or pursue some other action. The health model could be used to generate a baseline activity profile that represents one or more properties of a wearer's usual motor activity, and the process 150 could include comparing the wearer's ongoing motor activity to the determined baseline activity profile in order to determine when to prompt the wearer to perform one or more diagnostic motor tasks. The health model could additionally or alternatively be based on epidemiological or other medical information about a population of persons, e.g., a population of persons that are related to a wearer according to demographics. The health model could be updated based on changes in such information, e.g., based on additional data received from a population of wearers of the devices and systems described herein (Murphy Col 13:51-14:4), wherein identifying particular events which are related to the health state of interest is considered to read on outlining the variables that better characterize the given body movement condition or disorder];
obtain variables from a given user while the given user performed the function test during a given session using the monitoring sensors, the given user suffering the given body movement condition or disorder [Murphy Col 3:46-55, 5:61-6:1];
classify the obtained variables of the given user into the at least three different categories, and select, for each category, variables of the given user by considering the variables that better characterize the given body movement condition or disorder from the generated normality model [Murphy Col 14:41-52, wherein the signals being characterized by the sensor type is considered to read on the signals already being classified by category; Murphy Col 3:46-55];
compute, for the selected variables of the given user, a unified category score for each category [Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s) (Murphy Col 13:10-13)]; and
compute a condition or disorder score for the given user as the deviation between the computed unified category scores of the given user with the generated normality model [The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information (Murphy Col 13:1414-19); Additionally or alternatively, the process 150 could include determining a clinical standard score or some other rating of a wearer's disease state and/or performance of one or more motor tasks. For example, the process 150 could include determining a UPDRS and/or MSFC score for the wearer. Such a score or rating could be determined based on known relationship between measured and/or determined properties of the wearer's motor activities and the corresponding score or rating (e.g., based on relationship determined by measuring both the score or rating for a population of wearers and determined properties of the wearers' motor activities) (Murphy Col 13:28-39)].
However, while Murphy discloses the use of a pressure sensor configured to measure exerted force or pressure related to motor activity of the person and force between a person’s foot and the floor, Murphy fails to explicitly disclose wherein the pressure sensor is a plantar pressure sensor, and wherein the pressure category is a plantar pressure category.
Chen discloses systems and methods for assessment of body movement conditions or disorders using plantar pressure sensors, wherein Chen discloses that plantar pressure sensors may be used to diagnose body movement conditions or disorders as plantar pressure data provides variables that characterize the body movement conditions or disorders [Insole-based pressure sensors are able to obtain a variety of valuable information on human gaits, such as human balance, lower limb articular movement, and walking phase. Utilizing these temporal and spatial characteristics acquired by insole sensors, diagnosis and rehabilitation evaluation for some motor and neurological diseases can be performed (Chen p. 2); For example, the researches in refs. [180,185] used the plantar pressure dataset of patients (including neurodegenerative diseases like ALS, PD, and HD) published on PhysioNet, successfully extracted the typical abnormal parameters of patients, and trained the classification models for diagnosis. These attempts show the potential in building the relationship between abnormal gait patterns and specific diseases (Chen p. 26)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Murphy to employ wherein the pressure sensor is a plantar pressure sensor, and wherein the pressure category is a plantar pressure category, as plantar pressure sensors and corresponding measured data variables may be indicative of body movement conditions or disorders, and as this modification would amount to mere simple substitution of one known element for another with similar expected results [pressure sensor for detecting pressure/force between a subject’s foot and ground as disclosed by Murphy for a plantar pressure sensor as disclosed by Chen for the similar purpose of measuring pressure applied by a user’s foot] [MPEP § 2143(I)(B)].
Regarding claim 2, Murphy in view of Chen teaches
The system of claim 1, wherein the at least three different categories comprise two additional categories, including spatial-temporal and electromyography, and wherein the monitoring sensors are selected from the group consisting of: a heart rate sensor, an electromyography sensor, an inertial sensor or a goniometer, and a plantar pressure sensor [Each such body-mountable device could include a single sensor (e.g., an accelerometer, a gyroscope, a temperature sensor, a photoplethysmographic sensor, an EMG sensor) or multiple sensors (Murphy Col 4:19-22); The sensors of the system (e.g., of the body-mountable devices 110a-d and/or cell phone 120) could include accelerometers, gyroscopes, pressure sensors, strain sensors, magnetometers, global positioning system (GPS) receivers, photoplethysmographic sensors, laser speckle flowmeters, tonometers, blood pressure cuffs, electrocardiogram (ECG) electrodes, electromyogram (EMG) electrodes (Murphy Col 6:31-37); Murphy Col 5:61-6:1, 14:41-52, 19:3-9, wherein the signals being characterized by the sensor type is considered to read on the signals already being classified by category].
Regarding claim 5, Murphy in view of Chen teaches
The system of claim 1, wherein the specified sensor configuration comprises a time-synchronization with the other monitoring sensors [The controller could transmit sensor signals or other information to a remote system (e.g., a server, a cloud computing service) and/or transmit information determined from the sensor signals (e.g., an activity of the wearer 105 at a particular point in time, signals related to the performance of a prompted motor task, detected events and/or determined characteristics thereof detected from the sensor signals, health state information determined from the sensor signals, event characteristics, and/or performance of one or more prompted motor tasks) (Murphy Col 8:14-23), wherein the sensor data being tied to particular motor tasks as performed at particular times is conside3red to read on time-synchronization of data from the monitoring sensors].
Regarding claim 6, Murphy in view of Chen teaches
The system of claim 1, wherein the function test comprises at least one of: moving the arms, getting up and sitting down from a seat, a 6-Minute Walking Test, a 10-Meter Walking Test, a Timed Up and Go Test, and a Stair Climb Test [the prompted motor task could include a motor task. For example, the motor task could include a person getting up from a sitting stance and beginning to walk (e.g., a “timed-up-and-go” task); a person walking a specified distance from a starting point, turning around, and walking back to the starting point; a person sitting down or engaging in some other transfer activity (e.g., to or from a bed, wheelchair, chair, or automobile); a person locomoting while using a walker or other assistive device that may, itself, be instrumented; a person standing still or performing some other task while on a balance board that may be instrumented (e.g., with one or more load cells or other force sensors); or a person engaging in some other locomotive activity (Murphy Col 18:57-19:3)].
Regarding claim 7, Murphy teaches
A computed-implemented method for assessment of body movement's conditions or disorders, wherein a memory or database [The health model could be implemented as electronics (e.g., as a microcontroller executing instructions stored in a memory) disposed in one or more of the devices (e.g., 110a, 110b, 110c, 120) of the system 100. For example, a microprocessor of the cell phone 120 could operate to implement the health model and/or to perform processes thereof by executing instructions or other information downloaded from the internet. Additionally or alternatively, the health model could be implemented on a server, on a personal computer, as instructions executed in a cloud computing service, or on some other system in communication with the system 100 (Murphy Col 12:65-13:9)] comprises stored therein at least one of:
control variables, obtained from healthy users while they performed a function test using several monitoring sensors during different sessions of a same or different duration, and
pathology variables, obtained from unhealthy users while they performed the function test using the several monitoring sensors during different sessions of a same or different duration, the unhealthy users suffering a given body movement condition or disorder [Measured properties or processes from a particular person may be compared to population norms and/or to previously measured information from the particular person in order to diagnose a disease, determine a disease state or progression, or determine some other health information about the particular person (Murphy Col 1:23-29); The health model could additionally or alternatively be based on epidemiological or other medical information about a population of persons, e.g., a population of persons that are related to a wearer according to demographics. The health model could be updated based on changes in such information, e.g., based on additional data received from a population of wearers of the devices and systems described herein (Murphy Col 13:64-14:4)],
each one of the different monitoring sensors, for each session and for each healthy and unhealthy user, being configured to be attached to a different area of a body to obtain one or more variables of the area according to a specified sensor configuration, the one or more variables including at least one of biomechanical and physiological variables [Each of the body-mountable devices 110a-d of the system 100 includes at least one sensor that monitors motion of a person. Such a sensor could be configured to generate a signal related to motor activity of the wearer 105, e.g., to a motion, rotation, acceleration, pulse rate or other cardiovascular property, exerted force or pressure, or other properties of the body part to which the body-mountable device is mounted (Murphy Col 5:61-6:1); A variety of signals related to motor activity of a person may be detected, using the methods and systems described herein, and used to determine whether to prompt the person to perform a diagnostic motor task. Such signals may be related to motion of parts of the person's body (e.g., acceleration, rotation, location of parts of the person's body), physiological properties of the person's body (e.g., heart rate, breathing rate, oxygen saturation), sounds generated by the person (e.g., during speech), forces exerted by the person (e.g., to push an object, to type on a keyboard, to interact with a touch screen), or some other properties related to motor activities of the person (Murphy Col 14:41-52); An acceleration, a rotation, a force (e.g., a force between a person's foot and the floor), a location, or some other signal related to one or more body parts of the person could be detected and used to assess the performance of such a motor task, e.g., to determine a severity or degree of progression of a movement disorder or other disease state or process (Murphy Col 19:3-9)];:
the monitoring sensors comprising at least three sensors that are selected from the group consisting of: a heart rate sensor, an inertial sensor or a goniometer, and a pressure sensor [Murphy Col 5:61-6:1, 14:41-52, 19:3-9];
the control variables, the pathology variables, or both, for each session, being stored classified in at least three different categories, including cardiac, kinematics and pressure [Murphy Col 14:41-52, wherein the signals being characterized by the sensor type is considered to read on the signals being classified by category, and wherein comparative population data for determining progression or state of a disease or health state is considered to read on control population variables and pathology population variables, in order to allow for the determination of progression between healthy and a progressed state of the disease or health state]; and
the method comprising performing by one or more processors of a processing unit the following steps:
generating a normality model for the given body movement condition or disorder by implementing a statistical-based feature selection process on the stored control or pathology variables, the generated normality model defining a unified category score for each category of the at least three different categories, each unified category score outlining the variables that better characterize the given body movement condition or disorder [Measured properties or processes from a particular person may be compared to population norms and/or to previously measured information from the particular person in order to diagnose a disease, determine a disease state or progression, or determine some other health information about the particular person (Murphy Col 1:53-28); A device or system of devices (e.g., a cell phone, a watch, a wearable health device) could operate to detect signals relevant to the disease state or process (e.g., to detect accelerations or rotations related to tremor, locomotion, or other motor activity that may have properties related to the disease state or process) over a protracted period of time (e.g., during most of the day). These detected signals could be used to detect a change in a symptom of the disease state or process or to detect some other property of a person's physiological and/or behavioral state (Murphy Col 3:46-55); Additionally or alternatively, it could be determined, based on sensor signals recorded from a population of wearers, that the events are related to the health state of interest. For example, to assess the presence, progression, or other properties of a movement disorder (e.g., Parkinson's disease, dystonia, essential tremor, chorea, dyskinesia, or some other movement disorder), the events could include discrete motions or actions, e.g., footsteps, turns of the wearer's body, reaches or other arm motions, or other motions or actions engaged in by the wearer 105 (Murphy Col 9:45-55); The structure, parameters, or other properties of the health model could be determined based on past information from one or more wearers, e.g., the health model could provide an output that is predictive of whether a wearer's health state is significantly different from the wearer's health state in the past and this output could be used to determine that the wearer should seek medical attention, take a drug, or pursue some other action. The health model could be used to generate a baseline activity profile that represents one or more properties of a wearer's usual motor activity, and the process 150 could include comparing the wearer's ongoing motor activity to the determined baseline activity profile in order to determine when to prompt the wearer to perform one or more diagnostic motor tasks. The health model could additionally or alternatively be based on epidemiological or other medical information about a population of persons, e.g., a population of persons that are related to a wearer according to demographics. The health model could be updated based on changes in such information, e.g., based on additional data received from a population of wearers of the devices and systems described herein (Murphy Col 13:51-14:4), wherein identifying particular events which are related to the health state of interest is considered to read on outlining the variables that better characterize the given body movement condition or disorder];
once stored in the memory or database, obtaining variables obtained from a given user while the given user performed the function test during a given session using the monitoring sensors, the given user suffering from the given body movement condition or disorder [Murphy Col 3:46-55, 5:61-6:1];
classifying the gotten variables of the given user into the at least three different categories, and selecting, for each category, variables of the given user by considering the variables that better characterize the given body movement condition or disorder from the generated normality model [Murphy Col 14:41-52, wherein the signals being characterized by the sensor type is considered to read on the signals already being classified by category; Murphy Col 3:46-55];
computing, for the selected variables of the given user, a unified category score for each category [Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s) (Murphy Col 13:10-13)]; and
computing a condition or disorder score for the given user as the deviation between the computed unified category scores of the given user with the generated normality model [The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information (Murphy Col 13:1414-19); Additionally or alternatively, the process 150 could include determining a clinical standard score or some other rating of a wearer's disease state and/or performance of one or more motor tasks. For example, the process 150 could include determining a UPDRS and/or MSFC score for the wearer. Such a score or rating could be determined based on known relationship between measured and/or determined properties of the wearer's motor activities and the corresponding score or rating (e.g., based on relationship determined by measuring both the score or rating for a population of wearers and determined properties of the wearers' motor activities) (Murphy Col 13:28-39)].
However, while Murphy discloses the use of a pressure sensor configured to measure exerted force or pressure related to motor activity of the person and force between a person’s foot and the floor, Murphy fails to explicitly disclose wherein the pressure sensor is a plantar pressure sensor, and wherein the pressure category is a plantar pressure category.
Chen discloses systems and methods for assessment of body movement conditions or disorders using plantar pressure sensors, wherein Chen discloses that plantar pressure sensors may be used to diagnose body movement conditions or disorders as plantar pressure data provides variables that characterize the body movement conditions or disorders [Insole-based pressure sensors are able to obtain a variety of valuable information on human gaits, such as human balance, lower limb articular movement, and walking phase. Utilizing these temporal and spatial characteristics acquired by insole sensors, diagnosis and rehabilitation evaluation for some motor and neurological diseases can be performed (Chen p. 2); For example, the researches in refs. [180,185] used the plantar pressure dataset of patients (including neurodegenerative diseases like ALS, PD, and HD) published on PhysioNet, successfully extracted the typical abnormal parameters of patients, and trained the classification models for diagnosis. These attempts show the potential in building the relationship between abnormal gait patterns and specific diseases (Chen p. 26)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer-implemented method of Murphy to employ wherein the pressure sensor is a plantar pressure sensor, and wherein the pressure category is a plantar pressure category, as plantar pressure sensors and corresponding measured data variables may be indicative of body movement conditions or disorders, and as this modification would amount to mere simple substitution of one known element for another with similar expected results [pressure sensor for detecting pressure/force between a subject’s foot and ground as disclosed by Murphy for a plantar pressure sensor as disclosed by Chen for the similar purpose of measuring pressure applied by a user’s foot] [MPEP § 2143(I)(B)].
Regarding claim 8, Murphy in view of Chen teaches
The method of claim 7, wherein the at least three different categories comprise two additional categories, including spatial-temporal and electromyography, and wherein the monitoring sensors are selected from the group consisting of: a heart rate sensor, an electromyography sensor, an inertial sensor or goniometer, and a plantar pressure sensor [Murphy Col 5:61-6:1, 6:31-37, 14:41-52, 19:3-9, wherein the signals being characterized by the sensor type is considered to read on the signals already being classified by category].
Regarding claim 11, Murphy in view of Chen teaches
The method of claim 10, wherein the memory or database further has stored therein one or more control mobility metrics and one or more pathology mobility metrics obtained from the healthy and unhealthy users, respectively, during the different sessions, the method further comprising validating the computed condition or disorder score by comparing it with a normalized score obtained from both the one or more control mobility metrics and the one or more pathology mobility metrics [For example, the process 150 could include determining a UPDRS and/or MSFC score for the wearer. Such a score or rating could be determined based on known relationship between measured and/or determined properties of the wearer's motor activities and the corresponding score or rating (e.g., based on relationship determined by measuring both the score or rating for a population of wearers and determined properties of the wearers' motor activities). Alternatively, such a score or rating could be determined directly from information detected during the wearer's performance of one or more motor tasks that are traditionally used, in clinical settings, to determine such a score or rating. For example, the process 150 could include prompting the wearer to perform the UPDRS and/or MSFC assessment tasks and detecting one or more signals related to the wearer's performance of the prompted tasks, such that a UPDRS and/or MSFC score may be determined, from the detected one or more signals, based on the wearer's performance of the prompted UPDRS and/or MSFC assessment tasks (Murphy Col 13:31-50)].
Regarding claim 12, Murphy in view of Chen teaches
The method of claim 7, wherein the specified sensor configuration comprises a time-synchronization with the other monitoring sensors [Murphy Col 8:14-23, wherein the sensor data being tied to particular motor tasks as performed at particular times is conside3red to read on time-synchronization of data from the monitoring sensors].
Regarding claim 13, Murphy in view of Chen teaches
The method of claim 7, wherein the cardiorespiratory function test comprises at least one of: moving the arms, getting up and sitting down from a seat, a 6-Minute Walking Test, a 10-Meter Walking Test, a Timed Up and Go Test, a Stair Climb Test [Murphy Col 18:57-19:3].
Regarding claim 14, Murphy in view of Chen teaches
A non-transitory computer-readable medium storing instructions configured to cause at least one processor of a computing system to perform the method according to claim 7 [Murphy Col 12:65-13:9].
Claim(s) 3-4 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Murphy in view of Chen, as applied to claims 1 and 7 above, in further view of Chang et al. (“A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease”, NPL attached), hereinafter Chang.
Regarding claim 3, Murphy in view of Chen teaches
The system of claim 1, wherein the memory or database is configured to store both the control variables and the pathology variables [Murphy Col 9:45-55, 13:51-14:4].
However, while Murphy discloses identifying particular events which are related to the health state of interest based on particular variables of the monitoring sensors [Murphy Col 14:41-52], Murphy fails to explicitly disclose wherein the statistical-based feature selection process comprises: comparing the control variables between two different sessions and considering the control variables that exhibited a same underlying distribution for both sessions as robust control variables; and comparing the pathology variables between two different sessions and considering the pathology variables that exhibited a same underlying distribution for both sessions as robust pathology variables.
Chang discloses methods for monitoring movement conditions and disorders, wherein Chang discloses steps for determining features capable of differentiating between healthy subjects and stroke subjects by comparing variables between different sessions and considering variables that exhibited a same underlying distribution for each session as a variable indicative of differentiating between healthy subjects and stroke subjects [The statistical results of gait parameters showed significant differences (p -value < 0.05) between the stroke and HC groups. Obviously, the stroke patients needed more stride counts, stride time, and walking time to complete the 10 m walking test. The stroke patients presented significantly shorter stride length, lower stride frequency, slower stride velocity, lower stride cadence, longer stance time, and longer swing time in comparison with the HCs. The finding in some previous studies also indicated that the stroke patients demonstrated a shorter stride length, a slower stride velocity, a lower stride cadence, a longer stride time, a longer stance time, and a longer swing time compared with the HCs when walking at self-selected speeds… Another finding in the walking test is that the stroke patients spent more time in the stance time and swing time compared with the HCs in this paper, which could be explained by the inference that stroke patients needed more time in their double limb support period to compensate for their weak leg muscle power and to maintain balance. This notable phenomenon presumably indicates that the stroke patients exhibited increased time for standing to compensate for the decreased of their balance so as to control stability between steps (Chang p. 8450-8451), wherein repeated measures of gait parameters within a population of healthy subjects and a population of stroke subjects is considered to read on comparing both control variables and pathology variables between different sessions, and as the noted gait parameters are identified as differentiating between healthy subjects and stroke subjects, the variables associated with the noted gait parameters are considered to be within a same underlying distribution (Table 4 on p. 8451 shows the standard deviation of the parameters, which is considered to define an underlying distribution of each parameter)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Murphy in view of Chen to employ wherein the statistical-based feature selection process comprises: comparing the control variables between two different sessions and considering the control variables that exhibited a same underlying distribution for both sessions as robust control variables; and comparing the pathology variables between two different sessions and considering the pathology variables that exhibited a same underlying distribution for both sessions as robust pathology variables, so as to allow for identification of features that differentiate between healthy subjects and subjects that have the movement condition or disorder.
Regarding claim 4, Murphy in view of Chen and Chang teaches
The system of claim 3, wherein the statistical-based feature selection process further comprises comparing the robust control variables with the robust pathology variables, obtaining a compared set of robust variables as a result, and selecting from the compared set, the variables showing a difference lower than a given threshold as the variables that better characterize the given body movement condition or disorder [See § 103 modification of claim 3 above; Chang p. 8450, wherein a statistically significant difference of p-value < 0.05 to identify parameters that may differentiate between healthy subjects and stroke subjects is considered to read on variables showing a difference lower than a given threshold as the variables that better characterize the given body movement condition or disorder].
Regarding claim 9, Murphy in view of Chen teaches
The method of claim 7, wherein the memory or database stores both the control variables and the pathology variables [Murphy Col 9:45-55, 13:51-14:4].
However, while Murphy discloses identifying particular events which are related to the health state of interest based on particular variables of the monitoring sensors [Murphy Col 14:41-52], Murphy fails to explicitly disclose wherein the statistical-based feature selection process comprises: comparing the control variables between two different sessions and considering the control variables that exhibited a same underlying distribution for both sessions as robust control variables; and comparing the pathology variables between two different sessions and considering the pathology variables that exhibited a same underlying distribution for both sessions as robust pathology variables.
Chang discloses methods for monitoring movement conditions and disorders, wherein Chang discloses steps for determining features capable of differentiating between healthy subjects and stroke subjects by comparing variables between different sessions and considering variables that exhibited a same underlying distribution for each session as a variable indicative of differentiating between healthy subjects and stroke subjects [Chang p. 8450-8451, wherein repeated measures of gait parameters within a population of healthy subjects and a population of stroke subjects is considered to read on comparing both control variables and pathology variables between different sessions, and as the noted gait parameters are identified as differentiating between healthy subjects and stroke subjects, the variables associated with the noted gait parameters are considered to be within a same underlying distribution (Table 4 on p. 8451 shows the standard deviation of the parameters, which is considered to define an underlying distribution of each parameter)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Murphy in view of Chen to employ wherein the statistical-based feature selection process comprises: comparing the control variables between two different sessions and considering the control variables that exhibited a same underlying distribution for both sessions as robust control variables; and comparing the pathology variables between two different sessions and considering the pathology variables that exhibited a same underlying distribution for both sessions as robust pathology variables, so as to allow for identification of features that differentiate between healthy subjects and subjects that have the movement condition or disorder.
Regarding claim 10, Murphy in view of Chen and Chang teaches
The method of claim 9, wherein the statistical-based feature selection process further comprises comparing the robust control variables with the robust pathology variables, obtaining a compared set of robust variables as a result, and selecting from the compared set, the variables showing a difference lower than a given threshold as the variables that better characterize the given body movement condition or disorder [See § 103 modification of claim 3 above; Chang p. 8450, wherein a statistically significant difference of p-value < 0.05 to identify parameters that may differentiate between healthy subjects and stroke subjects is considered to read on variables showing a difference lower than a given threshold as the variables that better characterize the given body movement condition or disorder].
Conclusion
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