Digital Biomarkers

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 medical devices for improved subject testing and subject analysis. More specifically, embodiments described herein provide diagnostic devices, systems, and methods for assessing the severity and progression of symptoms of muscle disorders, particularly spinal muscular atrophy (SMA), in a subject through active subject testing. [Background technology]

[0002] background Spinal muscular atrophy (SMA), also known as proximal spinal muscular atrophy and 5q spinal muscular atrophy, is an autosomal recessive disorder that is a rare but life-threatening neuromuscular disorder associated with motor neuron loss and progressive muscle wasting.

[0003] SMA has become a health problem and a significant economic burden on the health care system. Because SMA is a clinically heterogeneous disease of the CNS, there is a need for diagnostic tools that allow reliable diagnosis and differentiation of the current disease state and symptom progression, thus supporting accurate treatment.

[0004] There are several standardized methods and tests for measuring the severity and progression of symptoms in subjects diagnosed with SMA.This test involves a doctor measuring the subject's ability to perform physical functions.These standardized tests can provide an assessment of various symptoms, especially axial motor function, and can help track the changes in these symptoms over time.Therefore, using standardized methods and tests to evaluate the severity and progression of symptoms can help guide treatment and therapeutic options.

[0005] Currently, assessment of symptom severity and progression in subjects diagnosed with muscle disorders, particularly SMA, requires monitoring and testing of subjects in a clinic every 6 to 12 months (http: / / www.motor-function-measure.org / user-s-manual.aspx, MFM-9, 10, 15, 20, 21, MFM_D2). While it would be ideal to monitor and test subjects more frequently, increasing the frequency of clinic monitoring and testing can be costly and inconvenient for subjects. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] http: / / www.motor-function-measure.org / user-s-manual.aspx,MFM-9,10,15,20,21,MFM_D2 Summary of the Invention

[0007] Quick Overview The following provides a simplified summary of various embodiments described herein. This summary is not an extensive overview and is not intended to identify key or critical elements or to delineate the claims. The following summary merely presents some concepts in a simplified form as a prelude to the more detailed description provided below. The embodiments described herein describe a particular medical device for assessing the severity and progression of symptoms in subjects diagnosed with muscle disorders, particularly SMA. The ability to perform testing and monitoring remotely and outside of a clinic setting can provide lower cost, greater frequency, and simplified ease and convenience for subjects, resulting in improved detection of symptom progression and better treatment.

[0008] According to one aspect, the present disclosure relates to a diagnostic device for assessing myopathy, particularly SMA axial motor function, in a subject, the device including at least one processor, one or more sensors associated with the device, and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the device to: receive a first plurality of first sensor data via the one or more sensors associated with the device; extract a first plurality of features related to myopathy, particularly SMA axial motor function, in the subject from the received first sensor data; and determine a first assessment of the myopathy, particularly SMA axial motor function, based on the extracted first plurality of features.

[0009] E1 A diagnostic device for assessing axial motor function in a subject with a muscle disorder, particularly SMA, comprising: The device, at least one processor; one or more sensors associated with the device; a memory for storing computer readable instructions; Including, The computer-readable instructions, when executed by the at least one processor, cause the apparatus to: receiving a plurality of first sensor data via the one or more sensors associated with the device; extracting a first plurality of features from the received first sensor data that are related to the axial motor function of a subject with a muscle disorder, particularly SMA; and determining a first assessment of the subject's axial motor function based on the extracted first plurality of features. Execute Device.

[0010] E2 The computer-readable instructions, when executed by the at least one processor, cause the device to: prompting the subject to perform a diagnostic task in which they rotate the device, in particular a phone, as much as possible; receiving a plurality of second sensor data via the one or more sensors associated with the device in response to the subject performing the diagnostic task; 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 assessment of the subject's axial motor function based on the extracted second plurality of features. Further execute the E1 device.

[0011] E3 Any one of the devices E1 to E2 that is a smartphone.

[0012] E4. The apparatus of any one of E1-3, wherein the diagnostic task is associated with at least one of motor function tests.

[0013] E5 A computer-implemented method for assessing axial motor function in a subject with a muscle disorder, particularly SMA, comprising: receiving a plurality of first sensor data via one or more sensors associated with the device; extracting a first plurality of features from the received first sensor data that are related to the axial motor function of a subject with a muscle disorder, particularly an SMA; determining a first assessment of the axial motor function of the subject with a muscle disorder, particularly SMA, based on the extracted first plurality of features; 11. A computer-implemented method comprising:

[0014] E6 prompting the subject to perform one or more diagnostic tasks; receiving a plurality of second sensor data via the one or more sensors in response to the subject performing the one or more diagnostic tasks; extracting a second plurality of features from the received second sensor data related to the axial motor function of the subject with a muscle disorder, particularly SMA; determining a second assessment of the axial motor function of the subject with a muscle disorder, particularly an SMA, based on at least the extracted second sensor data; The computer-implemented method of E5 further comprising:

[0015] E7. The computer-implemented method of any one of E5-6, wherein the subject's axial motor function is assessed based on an active task, in particular based on the duration of turning the device, in particular a phone.

[0016] E8. The apparatus of any one of E1-4 or the computer-implemented method of any one of E5-7, 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 assessing axial motor function in a subject with a muscle disorder, particularly SMA, comprising: The method comprises: receiving a plurality of sensor data via one or more sensors associated with the device; extracting from the received sensor data a plurality of features related to the axial motor function of a subject with a muscle disorder, in particular SMA; determining an assessment of the axial motor function of the subject with a muscle disorder, particularly SMA, based on the extracted features; Including, A non-transitory machine-readable storage medium.

[0018] E10 A method of assessing a muscle disorder, particularly SMA, in a subject, comprising: determining a usage behavior parameter from a dataset including usage data of any one of devices E1 to E5 within a first predetermined time window during which the device was used by the subject; comparing the determined at least one usage behavior parameter with a reference, whereby the subject with a muscle disorder, in particular SMA, is evaluated; and A method comprising:

[0019] E11 A method for identifying whether a subject has a muscle disorder, in particular SMA, comprising: i) scoring subjects on a diagnostic task of reorienting a device, particularly a phone, as quickly as possible; ii) comparing the determined score with a standard, thereby assessing muscle disorders, particularly SMA; and A method comprising:

[0020] E12 further comprising administering to said subject a pharmaceutically active agent to reduce the likelihood of progression of a muscle disorder, particularly SMA; In particular, the pharmaceutically active agent is suitable for treating SMA in a subject, and is in particular an m7GpppX diphosphatase (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 (SMN1-derived long non-coding RNA) inhibitor, more particularly nusinersen, onasemnogene abeparvovec, risdiplam, or branapram; E11 method.

[0021] E13. The combination of methods of E12, wherein at least one determined parameter is better compared to a baseline parameter of said patient before said subject was treated with said pharmaceutical agent.

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

[0023] E15. The method of any one of E12 to E14, wherein said agent is risdiplam. [Brief explanation of the drawings]

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

[0025] [Figure 1] 1 is a diagram of an exemplary environment in which a diagnostic device for assessing axial motor function of muscle disorders, particularly SMA, in a subject is provided according to an exemplary embodiment. FIG. [Figure 2] FIG. 1 is a flow diagram of a method for assessing axial motor function of muscle disorders, particularly SMA, in a subject based on active testing of the subject, according to an exemplary embodiment. [Figure 3] 1 illustrates an example of a network architecture and data processing device that can be used to implement one or more exemplary aspects described herein. [Figure 4] 1 illustrates an example illustrating a diagnostic application according to one or more exemplary aspects described herein. [Figure 5] 10 is a plot showing the results of the sensor characteristics according to Example 1. DETAILED DESCRIPTION OF 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 show, by way of illustration, various embodiments in which the aspects described herein may be practiced. It is to be understood that other aspects and / or embodiments may be utilized and structural and functional changes may be made without departing from the scope of the described aspects and embodiments. The aspects described herein are capable of other embodiments and of being practiced or carried out in various ways. It is also to be understood that the phraseology and terminology used herein are for purposes of description and should not be regarded as limiting. Rather, the words and terms used herein should 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 the terms "mounted," "connected," "coupled," "disposed," "engaged," and similar terms is meant to include both direct and indirect mounting, connecting, coupling, disposing, and engaging.

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

[0028] In some embodiments, the diagnosis involves acquiring or receiving sensor data from one or more sensors associated with a mobile device as the subject performs activities of daily living. In some embodiments, the sensors may be located within the mobile device, such as a smartphone. In some embodiments, sensor features related to symptoms of myopathy, particularly SMA, are extracted from the received or acquired sensor data. In some embodiments, an assessment of the severity and progression of symptoms of myopathy, particularly SMA, in the subject is determined based on the extracted sensor features.

[0029] In some embodiments, systems, methods, and devices according to the present disclosure provide a diagnosis for assessing myopathy, particularly SMA, in a subject based on active testing 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 acquires or receives sensor data via one or more sensors. In some embodiments, the sensors may be in a mobile device or in a wearable sensor worn by the subject. In some embodiments, sensor features related to symptoms of myopathy, particularly SMA, are extracted from the received or acquired sensor data. In some embodiments, an assessment of the severity and progression of symptoms of myopathy, particularly SMA, in the subject is determined based on the extracted features of the sensor data.

[0030] Assessment of the severity and progression of symptoms of myopathies, particularly SMA, using a diagnostic according to the present disclosure correlates well with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing. Exemplary diagnostics according to the present disclosure can be used outside of a clinic environment, thus offering advantages in cost, ease of subject monitoring, and convenience for subjects. This facilitates frequent subject monitoring and testing, particularly daily, resulting in a better understanding of disease stages and providing insights into the disease that are useful to both the clinical and research communities. Exemplary diagnostics according to the present disclosure can detect even small changes in axial motor function of myopathies, particularly SMA, in subjects early, and can therefore be used for better disease management, including personalized treatment.

[0031] According to embodiments disclosed herein, the sensor may 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 assessing myopathy, particularly SMA axial motor function, in a subject 110 may be provided. In some embodiments, the device 105 may be a smartphone, smartwatch, or other mobile computing device. The device 105 includes a display screen 160. In some embodiments, the display screen 160 may be a touchscreen. 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 assess myopathy, particularly SMA axial motor function. The device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. In some embodiments, the one or more sensors associated with the device are at least one of sensors disposed within the device or sensors worn by the subject and configured to communicate with the device. 1, sensors associated with device 105 include a first sensor 120a disposed within device 105 and a second sensor 120b that can be 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 when subject 110 performs an activity.

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

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

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

[0036] In some embodiments, the computer instructions for the symptom monitoring application 130, when executed by the at least one processor 115, cause the device 105 to assess axial motor function of a myopathy, particularly SMA, in the subject 110 based on active testing 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 pre-specified statements or 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 assessing myopathy, 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 may 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 a plurality of second sensor data via the second sensor 120b. In some embodiments, the one or more diagnostic tasks may be associated with measuring axial motor function, particularly measuring the duration of device, particularly phone, orientation changes when performing the tasks.

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

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

[0040] FIG. 2 illustrates an exemplary method for assessing myopathy, particularly SMA axial motor function, in a subject based on active testing of the subject using the exemplary device 105 of FIG. 1. While FIG. 3 is described with reference to FIG. 1, it should be noted that the steps 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 the one or more tasks (step 210). The method includes extracting a plurality of features related to myopathy, particularly SMA axial motor function, from the received sensor data (215). The method includes determining an assessment of myopathy, particularly SMA axial motor function, based at least on the extracted sensor data (step 220).

[0041] Figure 2 illustrates an exemplary method for assessing muscle disorders, particularly SMA axial motor function, based on active testing of a subject 110 using the exemplary device 105 of Figure 1. In some embodiments, active testing of a subject 110 using the device 105 may be selected via a user interface of a symptom monitoring application 130.

[0042] The method begins by proceeding to step 205, which includes prompting the subject 110 to perform a diagnostic task. The device 105 prompts the subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting the subject to perform the 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 assessing muscle disorders, particularly SMA.

[0043] In some embodiments, the diagnostic task may involve changing the orientation of the device, particularly the phone, as quickly as possible.

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

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

[0046] The method proceeds to step 215 with extracting a second plurality of features related to myopathy, particularly SMA axial motor function, from the received sensor data. The device 105 extracts features related to myopathy, particularly SMA axial motor function, in the subject 110 from the received first and second sensor data. Symptoms of myopathy, particularly SMA, in the subject 110 can include symptoms indicative of the axial motor function of the subject 110. In some embodiments, the extracted features of the first and second plurality of sensor data can be indicative of myopathy, particularly SMA, symptoms such as axial motor function.

[0047] The method proceeds to step 220, which includes determining an assessment of myopathy, particularly SMA axial motor function, based on at least the extracted sensor data. The device 105 determines an assessment of myopathy, particularly SMA axial motor function, in the subject 110 based on the extracted features of the received first and second sensor data. In some embodiments, the device 105 can transmit the extracted features to the server 150 via the network 180. The 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 the processor 155, determines an assessment of myopathy, particularly SMA axial motor function, in the subject 110 based on the extracted features from the device 105 received by the server 150. In some embodiments, the symptom assessment application 170 can determine an assessment of myopathy, particularly SMA axial motor function, in the subject 110 based on the extracted features of the sensor data received from the device 105 and a subject database 175 stored in the memory 160. The 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 with an inertial measurement unit (IMU). In some embodiments, the second device may be several devices or sensors. In some embodiments, the subject database 175 may be independent of the server 150. In some embodiments, the server 150 transmits the determined assessment of myopathy, particularly SMA axial motor function, in the subject 110 to the device 105. In some embodiments, as shown in FIG. 1 , the device 105 may output the assessment of myopathy, particularly SMA axial motor function, on the display 160 of the device 105.

[0048] As described above, the assessment of symptom severity and progression of myopathies, particularly SMA, using the diagnostics disclosed herein correlates well with clinical outcome-based assessments and can therefore replace clinical subject monitoring and testing. The diagnostics disclosed herein were studied in a group of subjects with myopathies, particularly SMA subjects. Subjects were provided with a smartphone application that included axial motor function tests, particularly a test called "Turn the Phone."

[0049] FIG. 3 illustrates an example of a network architecture and data processing devices that can be used to implement one or more exemplary aspects described herein, such as those 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 private intranets, corporate networks, LANs, wireless networks, and personal networks (PANs), may also or instead be used. Network 301 is illustrative and may be replaced by fewer or additional computer networks. The local area network (LAN) may have one or more of any known LAN topologies and may use one or more of a variety of 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 waves, or other communication media.

[0050] The term "network," as used herein and depicted in the drawings, refers not only to a system in which remote storage devices are coupled together through one or more communication paths, but also to stand-alone devices that may be coupled to such a system at any time with storage capabilities. Thus, the term "network" includes not only a "physical network" but also a "content network" consisting of data—belonging to a single entity—that resides across all physical networks.

[0051] The components may 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 the databases and control software for implementing one or more exemplary embodiments described herein. The data server 303 may be connected to a web server 305 with which users interact and obtain requested data. Alternatively, the data server 303 may itself operate as a web server and be connected directly to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet) through a direct or indirect connection, or through some other network. Users may interact with the data server 303 using remote computers 307, 309, for example, by using a web browser to connect to the data server 303 through one or more publicly accessible websites hosted by the web server 305. The client computers 307, 309 may be used in conjunction with the data server 303 to access data stored therein, or may be used for other purposes. For example, from client device 307, a user may access web server 305 using an internet browser as known in the art or by executing a software application that communicates with web server 305 and / or data server 303 over a computer network (such as the internet). In some embodiments, client computer 307 may be a smartphone or other mobile computing device and may implement a diagnostic device such as device 105 shown in FIG. 1. In some embodiments, data server 303 may implement a server such as server 150 shown in FIG. 1.

[0052] The servers and applications may be combined on the same physical device, maintain separate virtual or logical addresses, or may reside on separate physical devices. Figure 1 shows only one example of a network architecture that may be used, and those skilled in the art will appreciate that the particular network architecture and data processing devices used may vary and are secondary to the functionality they provide, as described further herein. 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 may include, for example, a processor 311 that controls the overall operation of the rate server 303. The data server 303 may further include RAM 313, ROM 315, a network interface 317, an input / output interface 319 (e.g., a keyboard, a mouse, a display, a printer, etc.), and memory 321. The I / O 319 may include various interface units and drivers for reading, writing, displaying, and / or printing data or files. The memory 321 may further store operating system software 323 for controlling the overall operation of the data processing device 303, control logic 325 for directing the data server 303 to perform aspects described herein, and other application software 327 that provides secondary support and / or other functions that may or may not be used in combination with other aspects described herein. The control logic is sometimes referred to herein as data server software 325. The functionality of the data server software can refer to actions or decisions that are made automatically based on rules coded into the control logic, actions or decisions that are made manually by a user by providing input to the system, and / or a combination of automated processing based on user input (e.g., queries, data updates, etc.).

[0054] Additionally, memory 321 can store data used to perform one or more aspects described herein, including first database 329 and second database 331. In some embodiments, the first database may include a second database (e.g., as another 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 system design. Devices 305, 307, and 309 can have an architecture similar to or different from that described with respect to device 303. Those skilled in the art will appreciate that the functionality of data processing device 303 (or devices 305, 307, and 309) described herein can be distributed across multiple data processing devices, for example, to distribute processing load across multiple computers, segregate transactions based on geographic location, user access level, quality of service (QoS), etc.

[0055] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions, such as in one or more program modules executed by one or more computers or other devices described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that, when executed by a processor of a computer or other device, perform particular tasks or implement particular abstract data types. Modules may be written in source code programming languages ​​that are subsequently compiled for execution, or in scripting languages, such as, but not limited to, HTML or XML. Computer-executable instructions may be stored in computer-readable media, such as hard disks, optical disks, removable storage media, solid-state memory, RAM, etc. Those skilled in the art will appreciate that functionality of the program modules may be combined or distributed as desired in various embodiments. Furthermore, functionality may be embodied, in whole or in part, in firmware or hardware equivalents, such as integrated circuits, field programmable gate arrays (FPGAs), etc. Certain data structures may be used to more efficiently 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] 4 depicts an example illustrating a diagnostic test according to one or more exemplary embodiments described herein. The user must select "Start" to begin the task.

[0057] Figure 5 is a plot showing the sensor feature results from the "Turn the phone" diagnostic test of Example 5 shown in Figures 9A-9C. The sensor feature results (duration of turning the phone in seconds) are consistent with the clinical anchor (time from picking up the tennis ball to turning the hand) in both studies.

[0058] Although the present 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 particular features or acts described above. Rather, the specific features and acts described above are disclosed merely as exemplary forms of implementing the claims. [Example]

[0059] Example 1 Characteristics of the analyzed patient populations collected in the two different studies. i) OLEOS study (https: / / clinicaltrials.gov / ct2 / show / NCT02628743) Participants analyzed: 20 Data analysis period: Smartphone data between the last two visits (176 days) TIFF0007728184000001.tif25128

[0060] ii) JEWELFISH study (https: / / clinicaltrials.gov / ct2 / show / NCT03032172?term=BP39054) Participants analyzed: 19 TIFF0007728184000002.tif25128

[0061] Dataset acquisition using the axial motor function test, a computer-implemented test (Test: Turn the Phone) to determine the time required to turn the phone. TIFF0007728184000003.tif25585 Covariates: 1:MFM_9_15_20_21 = MFM score 9, 15, 20, 21; 2: Sum of 32 = MFM total score; 3: MFM010; 4: MFM_D2; 5: MFM021 ICC: Intraclass correlation coefficient, SD = standard deviation

[0062] The test was performed with a mobile phone (iPhone). The patient should turn the phone face up and face down in their preferred hand for 10 seconds. The phone should be held in the preferred hand. The arm should be extended in front of the patient as far as possible. The patient should indicate the arm position: outstretched, elbow bent but in the air, elbow resting on the armrest, or hand resting on a table. The rotation speed of a single turn and the number of turns in 10 seconds are measured.

[0063] Figure 5 shows the correlation between the results (maximum velocity of a single turn in seconds) from the clinical anchor test and the phone turning test. The sensor feature results are clearly related to the clinical anchor (picking up a tennis ball and then turning the hand) in both studies. For clinical anchor, there are no units. It is a scale of 0, 1, 2, 3, or 4. Values ​​between 2 and 3 indicate the average of clinical measurements from two consecutive visits. The feature selected is the average maximum turning velocity as a measure of angular velocity per turn (rad / sec). The feature (maximum velocity of a single turn in seconds) was calculated based on the detected and segmented turns.

Claims

1. 1. A diagnostic device for assessing axial motor function in a subject with a muscle disorder, particularly SMA, comprising: The device, at least one processor; one or more first sensors associated with the device, the first sensors being a motion sensor, a gyroscope sensor, a position sensor, or a pressure sensor; a memory for storing computer readable instructions; Including, The computer-readable instructions, when executed by the at least one processor, cause the device to: receiving a plurality of first sensor data via the one or more first sensors associated with the device; extracting a first plurality of features that are measurements from the received first sensor data; determining a first assessment of the axial motor function of the subject using the extracted first plurality of features and a subject database including subject data and / or clinical data; prompting the subject to perform a diagnostic task of reorienting the device as much as possible; receiving a plurality of second sensor data via one or more second sensors worn by the subject in response to the subject performing the diagnostic task; extracting a second plurality of features that are measurements from the received second sensor data; and determining a second assessment of the axial motor function of the subject using the extracted second plurality of features and a subject database including subject data and / or clinical data; A device that performs the following.

2. The device of claim 1 , wherein the device is a smartphone.

3. The device of claim 1 or 2, wherein the diagnostic task is associated with at least one of motor function tests.

4. 1. A computer-implemented method for assessing axial motor function in a subject with a muscle disorder, particularly SMA, comprising: receiving a plurality of first sensor data via one or more first sensors associated with the device, the first sensors being a motion sensor, a gyroscope sensor, a position sensor, or a pressure sensor; extracting a first plurality of features that are measurements from the received first sensor data; determining a first assessment of axial motor function for a subject with a muscle disorder, particularly SMA, using the extracted first plurality of features and a subject database including subject data and / or clinical data; prompting the subject to perform a diagnostic task of reorienting the device as much as possible; receiving a plurality of second sensor data via one or more second sensors worn by the subject in response to the subject performing the diagnostic task; extracting a second plurality of features that are measurements from the received second sensor data; determining a second assessment of axial motor function for a subject with a muscle disorder, particularly SMA, using at least the extracted second plurality of features and a subject database including subject data and / or clinical data; 11. A computer-implemented method comprising:

5. The computer-implemented method of claim 4 , wherein the subject's axial motor function is assessed based on an active task.

6. The apparatus of any one of claims 1 to 3 or the computer-implemented method of claim 4 or 5, wherein the subject is a human.

7. 1. A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method for assessing axial motor function in a subject with a muscle disorder, particularly SMA, comprising: The method comprises: receiving a plurality of first sensor data via one or more first sensors associated with the device, the first sensors being a motion sensor, a gyroscope sensor, a position sensor, or a pressure sensor; extracting a first plurality of features that are measurements from the received first sensor data; determining a first assessment of axial motor function for a subject with a muscle disorder, particularly SMA, using the extracted first plurality of features and a subject database including subject data and / or clinical data; prompting the subject to perform a diagnostic task of reorienting the device as much as possible; receiving a plurality of second sensor data via one or more second sensors worn by the subject in response to the subject performing the diagnostic task; extracting a second plurality of features that are measurements from the received second sensor data; determining a second assessment of axial motor function for a subject with a muscle disorder, particularly SMA, using at least the extracted second plurality of features and a subject database including subject data and / or clinical data; 1. A non-transitory machine-readable storage medium, including:

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