Digital biomarker

Abstract

Currently, assessment of symptom severity and progression in subjects diagnosed with myopathies, particularly SMA, involves monitoring and testing the subject in a clinic every six to twelve months. However, while more frequent monitoring and testing of subjects would be preferable, increasing the frequency of in-clinic monitoring and testing can be costly and inconvenient for the subject. Therefore, as described herein, assessing symptom severity and progression by remote monitoring and testing of subjects outside of a clinic environment provides advantages in cost, ease of monitoring, and convenience for the subject. The systems, methods, and devices disclosed herein provide diagnostics for assessing axial motor function in subjects with myopathies, particularly SMA, through active testing of the subject.

Description

Technical Field

[0001] Field The present invention relates to a medical device for improved subject testing and subject analysis. More specifically, aspects described herein provide a diagnostic device, system, and method for evaluating the severity and progression of symptoms of muscular disorders, particularly spinal muscular atrophy (SMA), in a subject by an active test of the subject.

Background Art

[0002] Background Spinal muscular atrophy (SMA) is an autosomal recessive disorder also known as proximal spinal muscular atrophy and 5q spinal muscular atrophy. It is a life-threatening neuromuscular disorder associated with the loss of motor neurons and progressive muscle wasting, affecting a small number of patients.

[0003] SMA is a health problem and a significant economic burden on the insurance system. Since SMA is a clinically heterogeneous CNS disorder, there is a need for diagnostic tools that can enable reliable diagnosis and discrimination of the current disease state and symptom progression, and thus support accurate treatment.

[0004] To measure the severity and progression of symptoms in subjects diagnosed with SMA, several standardized methods and tests exist. These tests involve a physician measuring the ability of the subject to perform physical functions. These standardized tests can provide an assessment of various symptoms, particularly axial motor function, and help track changes in these symptoms over time. Thus, evaluating symptom severity and progression using standardized methods and tests can help guide treatment and therapeutic options.

[0005] Currently, the assessment of the severity and progression of symptoms in subjects diagnosed with a muscular disorder, particularly SMA, requires monitoring and testing of the subjects at a clinic every six to twelve months (http: / / www.motor - function - measure.org / user - s - manual.aspx, MFM - 5, 15, 32 (Non - Patent Document 1)). Although it is desirable to monitor and test the subjects more frequently, increasing the frequency of monitoring and testing at the clinic can be costly and inconvenient for the subjects.

Prior Art Documents

Non - Patent Documents

[0006]

Non - Patent Document 1

Summary of the Invention

[0007] Brief Summary The following presents a simplified summary of various aspects described herein. This summary is not an extensive overview and is not intended to identify key or critical elements or to elaborate on the claims in detail. The following summary merely presents some concepts in a simplified form as an introduction to the more detailed explanation provided below. The aspects described herein relate to a special medical device for assessing the severity and progression of symptoms in subjects diagnosed with a muscular disorder, particularly SMA. Since testing and monitoring can be performed remotely outside the clinic environment, it can provide lower costs, higher frequencies, as well as simplified ease and convenience to the subjects, resulting in improved detection of symptom progression and leading to better treatment.

[0008] According to one aspect, the present disclosure relates to a diagnostic device for evaluating axial motor function of muscle disorders, particularly SMA, in a subject. The device includes at least one processor, one or more sensors associated with the device, and a memory storing computer-readable instructions. When the computer-readable instructions are executed by the at least one processor, the device is caused to receive a plurality of first sensor data via the one or more sensors associated with the device, extract a first plurality of features related to axial motor function of muscle disorders, particularly SMA, in the subject from the received first sensor data, and determine a first evaluation of the axial motor function of muscle disorders, particularly SMA, based on the extracted first plurality of features.

[0009] E1 A diagnostic device for evaluating the axial motor function of a subject having a muscle disorder, particularly SMA, comprising: said device comprising: at least one processor; one or more sensors associated with said device; a memory storing computer-readable instructions and comprising: when the computer-readable instructions are executed by the at least one processor, causing the device to: receive a plurality of first sensor data via the one or more sensors associated with the device; extract a first plurality of features related to the axial motor function of the subject having a muscle disorder, particularly SMA, from the received first sensor data; and determine a first evaluation of the axial motor function of the subject based on the extracted first plurality of features to be executed; device.

[0010] E2 When the computer-readable instructions are executed by the at least one processor, causing the device to: prompt the subject to perform a diagnostic task of balancing on a rope for 30 seconds; In response to the subject performing the diagnostic task, receiving a plurality of second sensor data via the one or more sensors associated with the device extracting a second plurality of features related to the axial motor function of the subject from the received second sensor data, and determining a second evaluation of the axial motor function of the subject based on the extracted second plurality of features The device of E1, which further causes the above to be executed

[0011] The device of any one of E1 to E2, which is a E3 smartphone

[0012] The device of any one of E1 to E3, wherein the E4 diagnostic task is associated with at least one of the motor function tests

[0013] A computer-implemented method for evaluating the axial motor function of a subject with a muscular disorder, particularly SMA, comprising receiving a plurality of first sensor data via one or more sensors associated with a device extracting a first plurality of features related to the axial motor function of a subject with a muscular disorder, particularly SMA, from the received first sensor data determining a first evaluation of the axial motor function of a subject with a muscular disorder, particularly SMA, based on the extracted first plurality of features and the computer-implemented method including the above

[0014] prompting the subject to perform one or more diagnostic tasks in response to the subject performing the one or more diagnostic tasks, receiving a plurality of second sensor data via the one or more sensors extracting a second plurality of features related to the axial motor function of a subject with a muscular disorder, particularly SMA, from the received second sensor data At least, based on the extracted second sensor data, determining a second evaluation of the axial motor function of a subject having a muscular disorder, particularly SMA A computer-implemented method of E5, further comprising

[0015] E7 A computer-implemented method according to any one of E5 to E6, wherein the axial motor function of the subject is evaluated based on an active task, particularly based on balancing on a rope for 30 seconds.

[0016] E8 An apparatus according to any one of E1 to E4 or a computer-implemented method according to any one of E5 to E7, wherein the subject is a human.

[0017] E9 A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method for evaluating the axial motor function of a subject having a muscular disorder, particularly SMA, wherein the method comprises receiving a plurality of sensor data via one or more sensors associated with the apparatus, extracting, from the received sensor data, a plurality of features related to the axial motor function of a subject having a muscular disorder, particularly SMA, determining an evaluation of the axial motor function of a subject having a muscular disorder, particularly SMA, based on the extracted plurality of features and comprising a non-transitory machine-readable storage medium.

[0018] E10 A method for evaluating a muscular disorder, particularly SMA, of a subject, comprising determining usage behavior parameters from a data set including usage data of the apparatus within a first predetermined time window, wherein the apparatus according to any one of E1 to E5 has been used by the subject, and comparing at least one of the determined usage behavior parameters with a reference, whereby a subject having a muscular disorder, particularly SMA, is evaluated. A method comprising

[0019] A method for identifying whether a subject E11 has a muscular disorder, particularly a subject with SMA, comprising: i) scoring the subject in a diagnostic task of balancing on a rope for 30 seconds; and ii) comparing the determined score with a reference, whereby a muscular disorder, particularly SMA, is evaluated. A method comprising the above.

[0020] E12 Further comprising administering a pharmaceutically active agent to the subject to reduce the likelihood of progression of the muscular disorder, particularly SMA, In particular, the pharmaceutically active agent is suitable for the treatment of SMA in the subject, particularly an m7GpppX phosphatase (DCPS) inhibitor, a survival motor neuron protein 1 regulator, an SMN2 expression inhibitor, an SMN2 splicing regulator, an SMN2 expression enhancer, a survival motor neuron protein 2 regulator, or an SMN-AS1 (long non-coding RNA derived from SMN1) inhibitor, more specifically nusinersen, onasemnogene abeparvovec, risdiplam, or branaplam. The method of E11.

[0021] E13 A combination of the method of E12, wherein at least one determined parameter is better compared to the reference parameter of the patient before the subject receives treatment with the pharmaceutical agent.

[0022] E14 The method according to any one of E12 to E13, wherein the subject is a human.

[0023] E15 The method according to any one of E12 to E14, wherein the agent is risdiplam. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] A more complete understanding of the embodiments described herein and their advantages can be obtained by referring to the following description in consideration of the accompanying drawings. In the accompanying drawings, like reference numerals indicate like features.

[0025]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Mode for Carrying Out the Invention

[0026] Detailed Description In the following description of various aspects, reference is made to the accompanying drawings which form a part hereof and which illustrate various embodiments in which the aspects described herein can be practiced. It is to be understood that other aspects and / or embodiments can be utilized and structural and functional changes can be made without departing from the scope of the described aspects and embodiments. The aspects described herein can be practiced in other embodiments and can be implemented or executed in a variety of ways. Also, it is to be understood that the language and terminology used herein is for the purpose of description and should not be regarded as limiting. Rather, the phrases and terms used herein are to be accorded the broadest interpretation and meaning. The use of "including" and "comprising" and variations thereof is meant to encompass the items listed thereafter and their equivalents, as well as additional items and their equivalents. The use of terms such as "attach", "connect", "couple", "position", "engage", and the like is meant to include both direct and indirect attachment, connection, coupling, positioning, and engagement.

[0027] The systems, methods, and apparatuses described herein provide a diagnosis for assessing axial motor function in a subject with a muscular disorder, particularly SMA. In some embodiments, the diagnosis may be provided to the subject as a software application installed on a mobile device, particularly a smartphone.

[0028] In some embodiments, the diagnosis obtains or receives sensor data from one or more sensors associated with a mobile device when the subject performs activities of daily living. In some embodiments, the sensors may be within a mobile device such as a smartphone or a wearable sensor such as a smartwatch. In some embodiments, sensor features related to the symptoms of a muscular disorder, particularly SMA, are extracted from the received or obtained sensor data. In some embodiments, an assessment of the severity and progression of the symptoms of a muscular disorder, particularly SMA, in the subject is determined based on the extracted sensor features.

[0029] In some embodiments, the systems, methods, and apparatuses according to the present disclosure provide a diagnosis for assessing muscle disorders, particularly SMA, in a subject based on an active test of the subject. In some embodiments, the diagnosis prompts the subject to perform a diagnostic task. In some embodiments, the diagnostic task is anchored or modeled after established methods and standardized tests. In some embodiments, in response to the subject performing the diagnostic task, the diagnosis obtains or receives sensor data via one or more sensors. In some embodiments, the sensors may be within a mobile device or within a wearable sensor worn by the subject. In some embodiments, sensor features related to the symptoms of muscle disorders, particularly SMA, are extracted from the received or obtained sensor data. In some embodiments, an assessment of the severity and progression of the symptoms of muscle disorders, particularly SMA, in the subject is determined based on the extracted features of the sensor data.

[0030] The assessment of the severity and progression of the symptoms of muscle disorders, particularly SMA, using the diagnosis according to the present disclosure is highly correlated with the assessment based on clinical outcomes and can thus replace clinical subject monitoring and testing. The exemplary diagnosis according to the present disclosure is usable outside of a clinic environment and thus has advantages in terms of cost, ease of subject monitoring, and convenience for the subject. This facilitates frequent subject monitoring and testing, such as daily, and as a result, provides a better understanding of the disease stage and useful disease insights for both the clinical and research communities. The exemplary diagnosis according to the present disclosure can detect even small changes in the axial motor function of muscle disorders, particularly SMA, in a subject and can thus be used for better disease management, including personalized treatment.

[0031] According to the embodiments disclosed herein, the sensor can be, for example, a motion sensor, a gyroscope sensor, a position sensor, or a pressure sensor.

[0032] FIG. 1 is a diagram of an exemplary environment in which a diagnostic device 105 for evaluating the axial motor function of a subject 110 with a muscular disorder, particularly SMA, is provided. In some embodiments, the device 105 may be a smartphone, a smartwatch, or other mobile computing device. The device 105 includes a display screen 160. In some embodiments, the display screen 160 may be a touch screen. The device 105 includes at least one processor 115 and a memory 125 that stores computer instructions for a symptom monitoring application 130 that, when executed by the at least one processor 115, causes the device 105 to evaluate the axial motor function of a subject with a muscular disorder, particularly SMA. The device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. In some embodiments, one or more sensors associated with the device are at least one of a sensor disposed within the device or a sensor configured to be worn by the subject and communicate with the device. In FIG. 1, the sensors associated with the device 105 include a first sensor 120a disposed within the device 105 and a second sensor 120b that can be worn by the subject 110. The device 105 receives a plurality of first sensor data via the first sensor 120a and a plurality of second sensor data via the second sensor 120b when the subject 110 is performing an activity.

[0033] The device 105 extracts features related to the axial motor function of the subject 110 with a muscular disorder, particularly SMA, from the received first sensor data and second sensor data. In some embodiments, the symptoms of the muscular disorder in the subject 110, particularly SMA, can include symptoms representing the axial motor function of the subject 110.

[0034] In some embodiments, the sensor 120 associated with the device 105 can include sensors associated with Bluetooth and WiFi functions, and the sensor data can include information associated with the Bluetooth and WiFi signals received by the sensor 120. In some embodiments, the device 105 extracts data corresponding to the density of the Bluetooth and WiFi signals received or transmitted by the device 105 or the sensor from the received first sensor data and second sensor data. In some embodiments, the evaluation of the axial motor function of the subject 110 can be based on the extracted Bluetooth and WiFi signal data (for example, the evaluation of the subject's sociability can be partially based on the density of the picked-up Bluetooth and WiFi signals).

[0035] Device 105 determines an assessment of the motor impairment in subject 110, particularly the axial motor function of the SMA, based on the extracted features of the received first and second sensor data. In some embodiments, device 105 transmits the extracted features to server 150 via network 180. Server 150 includes at least one processor 155 and a memory 161 that stores computer instructions for symptom assessment application 170 that, when executed by the server's processor 155, cause the processor 155 to determine an assessment of the motor impairment in subject 110, particularly the axial motor function of the SMA, based on the extracted features received from device 105 by server 150. In some embodiments, symptom assessment application 170 can determine an assessment of the motor impairment in subject 110, particularly the axial motor function of the SMA, based on the extracted features of the sensor data received from device 105 and the subject database 175 stored in memory 160. In some embodiments, subject database 175 can include subject data and / or clinical data. In some embodiments, subject database 175 can include sensor-based measurements in a clinic of the long-term axial motor function at baseline from subjects with motor impairment, particularly SMA. In some embodiments, subject database 175 may be independent of server 150. In some embodiments, server 150 transmits the assessment determined for the axial motor function of the motor impairment in subject 110 to device 105. In some embodiments, device 105 can output an assessment of the motor impairment, particularly the axial motor function of the SMA. In some embodiments, device 105 can communicate information to subject 110 based on the assessment. In some embodiments, the assessment of the motor impairment, particularly the axial motor function of the SMA, can be communicated to a clinician who can determine an individual treatment for subject 110 based on the assessment.

[0036] In some embodiments, when the computer instructions for the symptom monitoring application 130 are executed by at least one processor 115, the device 105 causes the device 105 to evaluate the axial motor function of the subject 110 for muscle disorders, particularly SMA, based on an active test of the subject 110. The device 105 prompts the subject 110 to perform one or more tasks. In some embodiments, prompting the subject to perform one or more diagnostic tasks includes prompting the subject to transcribe a pre-specified text or prompting the subject to perform one or more actions. In some embodiments, the diagnostic tasks are anchored or modeled after well-established methods and standardized tests for diagnosing and evaluating muscle disorders, particularly SMA.

[0037] In response to the subject 110 performing one or more diagnostic tasks, the diagnostic device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. As described above, the sensors associated with the device 105 can include a first sensor 120a disposed within the device 105 and a second sensor 120b worn by the subject 110. The device 105 receives a plurality of first sensor data via the first sensor 120a and receives a plurality of second sensor data via the second sensor 120b. In some embodiments, one or more diagnostic tasks can be associated with the measurement of axial motor function, particularly the duration and accuracy of shape drawing when performing the task.

[0038] The device 105 extracts features related to the axial motor function of the muscle disorder in the subject 110, particularly SMA, from the received plurality of first sensor data and the received plurality of second sensor data. The symptoms of the muscle disorder in the subject 110, particularly SMA, can include symptoms representing the axial motor function of the subject 110.

[0039] Device 105 determines an assessment of the motor impairment, particularly the axial motor function of the SMA, in subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, device 105 transmits the extracted features to server 150 via network 180. Server 150 can include at least one processor 155 and a memory 161 that stores computer instructions for symptom assessment application 170 that, when executed by the server's processor 155, cause the processor 155 to determine an assessment of the motor impairment, particularly the axial motor function of the SMA, in subject 110 based on the extracted features received from device 105 by server 150. In some embodiments, symptom assessment application 170 can determine an assessment of the motor impairment, particularly the axial motor function of the SMA, in subject 110 based on the extracted features of the sensor data received from device 105 and the subject database 175 stored in memory 160. In some embodiments, subject database 175 can include subject data and / or clinical data. In some embodiments, subject database 175 can include measurements of the long-term axial motor function at baseline from subjects with motor impairment, particularly SMA. In some embodiments, subject database 175 can include data from subjects at other stages of motor impairment, particularly SMA. In some embodiments, subject database 175 may be independent of server 150. In some embodiments, server 150 transmits the assessment determined for the axial motor function of the motor impairment, particularly SMA, in subject 110 to device 105. In some embodiments, device 105 can output an assessment of the axial motor function of the motor impairment, particularly SMA. In some embodiments, device 105 can communicate information to subject 110 based on the assessment. In some embodiments, the assessment of the axial motor function of the motor impairment, particularly SMA, can be communicated to a clinician who can determine an individualized treatment for subject 110 based on the assessment.

[0040] FIG. 2 shows an exemplary method for evaluating muscle disorders, particularly axial motor function of SMA, in a subject based on an active test of the subject using the exemplary apparatus 105 of FIG. 1. FIG. 3, which is described with reference to FIG. 1, note that each step of the method of FIG. 3 may be performed by other systems. The method includes prompting the subject to perform one or more diagnostic tasks (205). The method includes receiving a plurality of sensor data via one or more sensors in response to the subject performing one or more tasks (step 210). The method includes extracting a plurality of features related to muscle disorders, particularly axial motor function of SMA, from the received sensor data (215). The method includes determining an evaluation of muscle disorders, particularly axial motor function of SMA, based at least on the extracted sensor data (step 220).

[0041] FIG. 2 shows an exemplary method for evaluating muscle disorders, particularly axial motor function of SMA, in a subject 110 based on an active test of the subject using the exemplary apparatus 105 of FIG. 1. In some embodiments, the active test of the subject 110 using the apparatus 105 may be selected via the user interface of the symptom monitoring application 130.

[0042] The method begins by proceeding to step 205, which includes prompting the subject to perform a diagnostic task. The apparatus 105 prompts the subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting the subject to perform one or more diagnostic tasks includes prompting the subject to perform one or more movements. In some embodiments, the diagnostic tasks are anchored or modeled after well-established methods and standardized tests for diagnosing and evaluating muscle disorders, particularly SMA.

[0043] In some embodiments, the diagnostic task can include drawing a shape as fast and accurately as possible.

[0044] As used herein, the term "test" refers to a test in which a subject is required to perform a diagnostic task described herein.

[0045] The method proceeds to step 210, which includes receiving, via one or more sensors, a plurality of second sensor data in response to a subject performing one or more diagnostic tasks. In response to subject 110 performing one or more diagnostic tasks, diagnostic device 105 receives a plurality of sensor data via one or more sensors associated with device 105. As described above, the sensors associated with device 105 include a first sensor 120a disposed within device 105 and a second sensor 120b worn by subject 110. Device 105 receives a plurality of first sensor data via first sensor 120a and a plurality of second sensor data via second sensor 120b.

[0046] The method proceeds to step 215, which includes extracting, from the received sensor data, a second plurality of features related to axial motor function of a muscle disorder, particularly SMA. Device 105 extracts features related to a muscle disorder, particularly axial motor function of SMA, in subject 110 from the received first and second sensor data. The muscle disorder in subject 110, particularly the symptoms of SMA, can include symptoms representing the axial motor function of subject 110. In some embodiments, the extracted features of the plurality of first and second sensor data can represent symptoms of a muscle disorder, particularly SMA, such as axial motor function.

[0047] This method proceeds to step 220, which includes determining an assessment of axial motor function of muscle disorders, particularly SMA, based at least on the extracted sensor data. Device 105 determines an assessment of axial motor function of muscle disorders, particularly SMA, in subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, device 105 can transmit the extracted features to server 150 via network 180. Server 150 includes at least one processor 155 and a memory 160 that stores computer instructions for a symptom assessment application 170 that, when executed by processor 155, determines an assessment of axial motor function of muscle disorders, particularly SMA, in subject 110 based on the extracted features received from device 105 by server 150. In some embodiments, symptom assessment application 170 can determine an assessment of axial motor function of muscle disorders, particularly SMA, in subject 110 based on the extracted features of the sensor data received from device 105 and a subject database 175 stored in memory 160. Subject database 175 can include various clinical data. In some embodiments, the second device may be one or more wearable sensors. In some embodiments, the second device may be any device including a motion sensor having an inertial measurement unit (IMU). In some embodiments, the second device may be several devices or sensors. In some embodiments, subject database 175 may be independent of server 150. In some embodiments, server 150 transmits the assessment determined for axial motor function of muscle disorders, particularly SMA, in subject 110 to device 105. In some embodiments as shown in FIG. 1, device 105 can output an assessment of axial motor function of muscle disorders, particularly SMA, on a display 160 of device 105.

[0048] As described above, the evaluation of the severity and progression of symptoms of muscular disorders, particularly SMA, using the diagnosis according to the present disclosure, is highly correlated with the evaluation based on clinical outcomes, and thus can replace the clinical monitoring and testing of subjects. The diagnosis according to the present disclosure has been studied in a group of subjects with muscular disorders, particularly subjects with SMA. The subjects were provided with a smartphone application including axial motor function tests, particularly a test called "walk on a rope".

[0049] FIG. 3 shows an example of a network architecture and a data processing device that can be used to implement one or more exemplary aspects described herein, such as the aspects described in FIGS. 1 and 2. Various network nodes 303, 305, 307, and 309 may be interconnected via a wide area network (WAN) 301 such as the Internet. Other networks including a private intranet, a corporate network, a LAN, a wireless network, a personal area network (PAN) may also be used, or instead. Network 301 is for illustration purposes and may be replaced by fewer or additional computer networks. The local area network (LAN) can have one or more of any known LAN topologies and can use one or more of various protocols such as Ethernet. Devices 303, 305, 307, 309 and other devices (not shown) may be connected to one or more of the networks via twisted pair wire, coaxial cable, optical fiber, radio wave, or other communication media.

[0050] As used herein and represented in the drawings, the term "network" refers not only to a system in which remote storage devices are coupled to each other via one or more communication paths, but also to stand-alone devices that can be coupled to such a system having a storage function at any time. Thus, the term "network" includes not only a "physical network", but also a "content network" composed of data that exists across all physical networks - and is attributed to a single entity.

[0051] The components can include a data server 303, a web server 305, and client computers 307, 309. The data server 303 provides overall access, control, and management of a database and control software for executing one or more specific embodiments described herein. The data server 303 can be connected to a web server 305 that the user interacts with and from which the requested data is obtained. Alternatively, the data server 303 can itself operate as a web server and be directly connected to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet), via a direct or indirect connection, or via some other network. The user can interact with the data server 303 by using remote computers 307, 309, for example, by using a web browser, to connect to the data server 303 via one or more externally published websites hosted by the web server 305. The client computers 307, 309 can be used in cooperation with the data server to access data stored in the data server 303, or can be used for other purposes. For example, from the client device 307, the user can access the web server 305 by using an Internet browser as known in the art, or by executing a software application that communicates with the web server 305 and / or the data server 303 via a computer network (such as the Internet). In some embodiments, the client computer 307 can be a smartphone, a smartwatch, or other mobile computing device, and can implement a diagnostic device such as the device 105 shown in FIG. 1. In some embodiments, the data server 303 can implement a server such as the server 150 shown in FIG. 1.

[0052] The server and the application may be combined on the same physical device, may hold different virtual or logical addresses, or may exist on separate physical devices. FIG. 1 shows merely an example of a network architecture that can be used, and those skilled in the art will understand that, as further described herein, the specific network architecture and data processing devices used may vary and are secondary to the functions they provide. For example, the services provided by web server 305 and data server 303 may be combined on a single server.

[0053] Each of the components 303, 305, 307, and 309 may be any type of known computer, server, or data processing device. The data server 303 can include, for example, a processor 311 that controls the overall operation of the rate server 303. The data server 303 can further include a RAM 313, a ROM 315, a network interface 317, an input / output interface 319 (e.g., keyboard, mouse, display, printer, etc.), and a memory 321. The I / O 319 can include various interface units and drivers for reading, writing, displaying, and / or printing data or files. The memory 321 can further store an operating system software 323 for controlling the overall operation of the data processing device 303, a control logic 325 for instructing the data server 303 to execute the aspects described herein, and other application software 327 that may or may not be used in combination with other aspects described herein to provide secondary support functions and / or other functions. The control logic may also be referred to herein as data server software 325. The functions of the data server software can refer to operations or decisions that are automatically performed based on rules encoded in the control logic, operations or decisions that are manually performed by the user by providing input to the system, and / or combinations of automatic processing based on user input (e.g., queries, data updates, etc.).

[0054] Furthermore, the memory 321 can store data used for the execution of one or more aspects described herein, including a first database 329 and a second database 331. In some embodiments, the first database may include the second database (e.g., as a separate table, report, etc.). That is, information may be stored in a single database or separated into different logical, virtual, or physical databases depending on the design of the system. Devices 305, 307, and 309 can have an architecture that is the same as or different from the architecture described for device 303. One of ordinary skill in the art will understand that the functions of the data processing device 303 (or devices 305, 307, and 309) described herein can be distributed across multiple data processing devices, for example, to distribute the processing load across multiple computers or to separate transactions based on geographical location, user access level, quality of service (QoS), etc.

[0055] One or more aspects described herein can be embodied in computer-usable or readable data and / or computer-executable instructions, such as one or more program modules executed by one or more computers or other devices described herein. Generally, a program module includes routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor of a computer or other device. Modules may be written in source code programming languages that are later compiled for execution, or in script languages such as HTML or XML (but not limited thereto). Computer-executable instructions can be stored on a computer-readable medium such as a hard disk, optical disk, removable storage medium, solid state memory, RAM, etc. As will be understood by those skilled in the art, the functions of program modules may be combined or distributed as desired in various embodiments. Further, the functions can be embodied in whole or in part in firmware or in hardware equivalents such as integrated circuits, field programmable gate arrays (FPGAs). Certain data structures can be used to more effectively implement one or more aspects, and such data structures are contemplated within the scope of the computer-executable instructions and computer-usable data described herein.

[0056] Figure 4 represents an example showing a diagnostic test according to one or more exemplary aspects described herein. The user needs to select "Start" to initiate the task.

[0057] Figure 5 is a plot showing the results of sensor characteristics from the "Walk the Rope" diagnostic test of Example 6 shown in Figure 4. The results of the sensor characteristics (standard deviation of the magnitude of acceleration with respect to the wind response) are consistent with the clinical anchor (MFM32) in both studies.

[0058] While the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed only as illustrative forms of implementing the claims.

Example

[0059] Example 1 Characteristics of the patient population to be analyzed collected in two different studies. i) OLEOS study (https: / / clinicaltrials.gov / ct2 / show / NCT02628743) Participants to be analyzed: 20 Period of data analysis: Smartphone data between the last two hospital visits (176 days) TIFF0007687968000001.tif25128

[0060] ii) JEWELFISH study (https: / / clinicaltrials.gov / ct2 / show / NCT03032172?term=BP39054) Participants to be analyzed: 19 TIFF0007687968000002.tif25128

[0061] Obtaining a dataset using an axial motor function test, which is a computer-implemented test (test: Walk on a rope) for determining by measuring the acceleration variation that occurs when rotating a telephone while reacting / compensating to sudden wind movements. TIFF0007687968000003.tif255161TIFF0007687968000004.tif25542Covariates: 1: Total 32 = Total score of MFM; 2: MFM_D2; 3: AGEIC; 4: MFM005; 5: MFM015 ICC: Intraclass correlation coefficient, SD = Standard deviation

[0062] The test was conducted using a mobile phone (iPhone). Refer to Figure 4. Assume that the patient is balancing the monster on a rope while the wind is blowing against the monster and disrupting its balance. The phone needs to be held with both hands. To balance the monster, the phone needs to be rotated left and right. The rotation of the phone can further cancel out the effect of the wind. Assume that the patient shows the position of the arm, i.e., whether it is extended, the elbow is bent and held in the air, the elbow is placed on the elbow rest, or the hand is placed on the table. The test lasts for 30 seconds.

[0063] Figure 5 shows the correlation between the clinical anchor test and the results of the rope walking test (standard deviation of the magnitude of acceleration in response to wind in m / s 2 units). In the test of balancing the monster, the monster may be challenged by the wind, which is the reaction in the first 2 seconds afterwards and indicates how much variability in hand movement occurs. This is the average over all wind challenges in one test run. The results of the sensor characteristics are clearly related to the clinical anchor (MFM32) in both studies.

Claims

1. A diagnostic device for evaluating the axial motor function of a subject with muscle disorders, wherein the device comprises at least one processor, one or more sensors associated with the device, and a memory storing computer-readable instructions and when the computer-readable instructions are executed by the at least one processor, cause the device to receive a plurality of first sensor data via the one or more sensors associated with the device, extract a first plurality of features related to the axial motor function of the subject with muscle disorders from the received first sensor data, and determine a first evaluation of the axial motor function of the subject based on the extracted first plurality of features and when the computer-readable instructions are executed by the at least one processor, cause the device to prompt the subject to perform a diagnostic task as a software application executed on the device, the diagnostic task being to balance on a rope for 30 seconds, in response to the subject performing the diagnostic task, receive a plurality of second sensor data via the one or more sensors associated with the device, extract a second plurality of features related to the axial motor function of the subject from the received second sensor data, and determine a second evaluation of the axial motor function of the subject according to how much variability in the subject's hand movement has occurred based on the extracted second plurality of features and further execute, device.

2. The device according to claim 1, which is a smartphone.

3. A computer-implemented method for evaluating the axial motor function of a subject with muscle disorders, comprising receiving a plurality of first sensor data via one or more sensors associated with a device, extracting a first plurality of features related to the axial motor function of the subject with muscle disorders from the received first sensor data, determining a first evaluation of the axial motor function of the subject with muscle disorders based on the extracted first plurality of features and the method further comprises prompting the subject to perform a diagnostic task as a software application executed on the device, the diagnostic task being to balance on a rope for 30 seconds, Receiving, via the one or more sensors, a plurality of second sensor data in response to the subject performing the diagnostic task; Extracting, from the received second sensor data, a plurality of second features related to the axial motor function of a subject with a muscle disorder; Determining a second evaluation of the axial motor function of a subject with a muscle disorder according to at least the variability of the subject's hand movement, depending on how much such variability occurs, based on the extracted plurality of second features; comprising; wherein the axial motor function of the subject is evaluated based on an active task of balancing on a rope for 30 seconds; A computer-implemented method. **Claim 4** The apparatus according to claim 1 or 2, wherein the subject is a human. **Claim 5** The computer-implemented method according to claim 3, wherein the subject is a human. **Claim 6** A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method for evaluating the axial motor function of a subject with a muscle disorder, wherein the method comprises: Receiving a plurality of sensor data via one or more sensors associated with the apparatus; Extracting a plurality of features related to the axial motor function of a subject with a muscle disorder from the received sensor data; Determining an evaluation of the axial motor function of a subject with a muscle disorder based on the extracted plurality of features; comprising; The method further comprises: Prompting the subject to perform a diagnostic task of balancing on a rope for 30 seconds; Receiving, via the one or more sensors, a plurality of second sensor data in response to the subject performing the diagnostic task; Extracting a plurality of second features related to the axial motor function of a subject with a muscle disorder from the received second sensor data; Determining a second evaluation of the axial motor function of a subject with a muscle disorder according to at least the second sensor data, depending on how much variability of the subject's hand movement occurs; comprising; wherein the axial motor function of the subject is evaluated based on an active task of balancing on a rope for 30 seconds; A non-transitory machine-readable storage medium.

Images