System for evaluating upper limb musclar exercise

Abstract

An upper limb muscle movement assessment system includes a wearable device and a cloud computing / analysis system. The wearable device includes a motion sensor that senses and collects motion energy-related data generated by muscle movements of an upper limb movement of a subject, thereby generating a record file. The subject's upper limb movement system includes performing a plurality of specified tasks on a Revised Upper Limb Module (RULM) assessment scale. The cloud computing / analysis system is connected to the wearable device to receive the record file, and analyzes the record file to obtain at least one detection result graph.

Description

Technical Field

[0001] This invention relates to a muscle movement assessment system, and more particularly to an upper limb muscle movement assessment system for evaluating disease-modifying therapy (DMT) medications used to treat spinal muscular atrophy (SMA). Prior Technology

[0002] Spinal muscular atrophy (SMA) attacks the motor nerve cells in the spinal cord, causing children to lose the ability to walk, eat, or breathe. It is the leading cause of infant mortality due to genetic disorders. SMA patients often use expensive disease-modifying therapy (DMT) medications to slow disease progression; however, poor patient adherence can lead to ineffective treatment.

[0003] US Patent Publication No. US20230402180A1 discloses a technique for using artificial intelligence (AI) to facilitate the treatment of subjects with spinal muscular atrophy (SMAS). US Patent Publication No. US7848797B2 discloses a method for assessing neuromuscular function by estimating the number of motor units. US Patent Publication No. US11474113B2 discloses a method using neurofilaments as biomarkers for the diagnosis and prognosis of SMAS. PCT Patent Publication No. WO2022248939A2 discloses a method and apparatus for treating patients with neuromotor disorders, which involves using multiple sensors to capture kinematic data from the subject within a specified time period and converting the kinematic data into postural scores, one or more of which may lead clinicians to create or modify treatment plans. Summary of the Invention

[0004] This invention provides an upper limb muscle movement assessment system that collects upper limb movement energy-related data from SMA patients performing specified movements, and analyzes and calculates the aforementioned movement energy-related data to obtain a detection graph that can assess the upper limb movement power of SMA patients.

[0005] This invention provides an upper limb muscle movement assessment system that collects upper limb movement energy-related data by having SMA patients wear a mobile device on their upper limbs. The aforementioned movement energy-related data is transmitted to the access point designated by the test subject and, after being processed and analyzed by a cloud system, can generate detection graphics that can assess the upper limb movement power of SMA patients.

[0006] According to the above, an upper limb muscle movement assessment system includes a wearable device and a cloud computing / analysis system. The wearable device includes a motion sensor that senses and collects motion energy-related data generated by muscle movements during an upper limb movement of a subject, thereby generating a record file. The subject's upper limb movement system includes performing a plurality of specified tasks on a Revised Upper Limb Module (RULM) assessment scale. The cloud computing / analysis system is connected to the wearable device to receive the record file, and analyzes the record file to obtain at least one detection result graph.

[0007] According to the above, an upper limb muscle movement assessment system includes: a record block storing a record file of a subject, wherein the subject performs a plurality of specified operational tasks on an upper limb muscle strength test (Revised Upper Limb Module, RULM) assessment scale, and the record file includes the motion energy-related data generated by the muscle movements of the upper limb; a processing block analyzing the motion energy-related data in the record file to generate at least one detection result graph, wherein the detection result graph corresponds to a result of the subject's upper limb muscle strength test assessment scale; a cloud storage block storing the detection result graph; and a query interface connected to the processing block, which provides a queryer with the option to view the detection result graph stored in the cloud storage block.

[0008] According to the above, a computer-readable storage medium is characterized in that the computer-readable storage medium stores a computer program, wherein the computer program causes a computer to perform the following steps: storing a record file of a test subject, wherein the test subject performs a plurality of specified operational tasks of an upper limb muscle strength test (Revised Upper Limb Module, RULM) assessment scale, and the record file includes the motion energy-related data; analyzing the motion energy-related data of the record file to generate at least one detection result graph, wherein the detection result graph corresponds to a result of the upper limb muscle strength test assessment scale of the test subject; and storing the detection result graph.

[0009] In one embodiment, the cloud computing / analysis system includes a query interface to provide a graphical display of the detection results on a display interface.

[0010] In one embodiment, the kinetic energy data includes an acceleration or, more specifically, an angular velocity.

[0011] In one embodiment, the cloud computing / analysis system includes storing the specified operational tasks of the upper limb muscle strength test assessment scale, which are used by the wearable device to prompt the test subject to perform the specified operational tasks.

[0012] In one embodiment, the cloud computing / analysis system includes analyzing the motion energy-related data and the specified operational tasks to generate the detection result graph.

[0013] In one embodiment, the upper limb muscle movement assessment system further includes a database connected to the processing block, the database storing personal information of the test subject and personal information of the queryer.

[0014] In one embodiment, the upper limb muscle movement assessment system further includes a communication block connecting the recording block and the processing block, wherein the communication block transmits the recording file to the processing block.

[0015] In one embodiment, the computer program causes the computer to perform actions that further include providing a user interface to display the detection result graph.

[0016] In one embodiment, the computer program causes the computer to perform actions that further include providing a subject interface to prompt the subject to perform a plurality of specified operational tasks of the upper limb muscle strength test assessment scale. Simple Explanation of the Diagram

[0017] Figure 1 is a block diagram of the upper limb muscle movement assessment system of the present invention.

[0018] Figure 2 is a schematic diagram of the software architecture of the upper limb muscle movement assessment system of the present invention.

[0019] Figure 3 is a schematic diagram of the cloud data processing flow of the upper limb muscle movement assessment system of the present invention.

[0020] Figure 4 is a partial schematic diagram of an embodiment of the trend chart obtained by the upper limb muscle movement assessment system of the present invention.

[0021] Figure 5 is a partial schematic diagram of an embodiment of the trend chart obtained by the upper limb muscle movement assessment system of the present invention.

[0022] Figure 6 is a partial schematic diagram of an embodiment of the trend chart obtained by the upper limb muscle movement assessment system of the present invention. Implementation

[0023] The following embodiments are illustrative. Although the following description refers to one, one, or several implementations, it does not imply that every such reference is the same implementation, or that such features are applicable only to a single implementation. Individual features of different embodiments may be combined to provide other implementations. The features of the invention will be described below by way of simple examples of various implementation device architectures that can carry out the invention, and only the relevant elements for the examples will be described in detail. However, the structure and fabrication process of silicon photonic wafers, which are mostly known to those skilled in the art, may not be specifically described herein.

[0024] Figure 1 is a system block diagram of the present invention for evaluating treatment (DMT) drugs for improving disease progression in the treatment of spinal muscular atrophy (SMA), and Figure 2 is a software architecture diagram of the upper limb muscle movement assessment system of the present invention. Referring to Figures 1 and 2, an embodiment of the upper limb muscle movement assessment system 1 of the present invention includes a wearable device 10, a display interface 20, and a cloud computing / analysis system 30. The wearable device 10 can be connected to the cloud computing / analysis system 30 via wired or wireless means, and the display interface 20 can also be connected to the cloud computing / analysis system 30 via wired or wireless means. In terms of software architecture, the upper limb muscle movement assessment system 1 includes: a presentation layer including a subject interface 40 and a query interface 42; a business logic layer including a subject business logic layer 50 and a query business logic layer 52; a cloud server 60; and a database layer 70. In one embodiment, the wearable device 10 includes at least a sensor 12, a prompting unit 14, a storage unit 16, a transmission unit 18, and a processing unit 19. In one embodiment, the wearable device 10 is fixed to an upper limb of the subject. The sensor 12, such as a motion sensor like a gyroscope or accelerometer, can automatically or be activated to sense and collect motion energy-related data, such as acceleration and angular velocity, generated by the muscle movements of the subject's upper limb. The motion energy-related data generated by the muscle movements can be stored in the storage unit 16. The subject's business logic layer 50 includes a subject login block 53, a recording block 54, and a cloud communication block 56. The subject login block 53 includes the processing and flow control of the de-identified personal information and wearable device 10 identification information corresponding to the subject; the recording block 54 includes the recording and processing of motion energy related data generated by the muscle movements of the subject's daily situation and specified actions when wearing the wearable device 10; and the cloud communication block 56 includes the processing and flow control of the subject transmitting device, personal information and motion energy related data to the cloud server through the wearable device 10.

[0025] Referring again to Figures 1 and 2, the prompting unit 14, such as a display screen, provides information to the test subject or communicates it via voice. It prompts the test subject to perform and select specified actions within the test scenario provided by the recording block 54, which can assess muscle strength. These specified actions can be taken from one or more specified operational tasks in the Revised Upper Limb Module (RULM) assessment scale, such as tearing paper, raising an arm, or tossing a coin. Similarly, the sensor 12 can automatically or be activated to sense and collect motion energy-related data generated by the upper limb muscle movements of the test subject performing the specified actions, and stores this data in the storage unit 16. The transmission unit 19, under the control of the control unit 19, transmits the motion energy-related data stored in the storage unit 16 to the connected cloud computing / analysis system 30. Furthermore, the control unit 19, such as a processor, IC, or ASIC, controls and coordinates the operation of the sensor 12, the prompting unit 14, the storage unit 16, and the transmission unit 18.

[0026] Referring again to Figures 1 and 2, in this embodiment, an assessment application is installed in the wearable device 10. Through the subject interface 40, the subject can receive prompts from the prompting unit 14, such as displaying or emitting audio messages on the screen, or connecting the wearable device 10 to the cloud computing / analysis system 30 (e.g., displaying "connection in progress"), or manually uploading motion energy-related data generated by muscle movements stored in the storage unit 16 to the cloud computing / analysis system 30 via the cloud communication block 56 (e.g., displaying "connection in progress"). Secondly, the motion energy-related data generated by muscle movements captured by the sensor 12 can be combined with information related to the specified action / task of the specified action that can assess muscle strength to form a record file. Optionally, if the wearable device 10 can measure / record the subject's physiological or other subject data, such as heart rate, pulse, etc., the physiological data can also be integrated into the record file. The record file can also be uploaded to the cloud computing / analysis system 30 via the transmission unit 18 for analysis / processing. Optionally, the wearable device 10 can also communicate with other electronic devices of the test subject (not shown in the figure), such as a smartphone, via wired / wireless connection. Furthermore, the relevant firmware update program provided by the cloud server 60 can be downloaded through the cloud communication block 56 to update the evaluation application of the wearable device 10.

[0027] Referring again to Figures 1 and 2, the upper limb muscle movement assessment system 1 includes a user interface 42 to allow the user of the wearable device 10 to connect to the cloud computing / analysis system 30 via other electronic devices and view data such as records or analysis results through the display interface 20 of the other electronic devices. In one embodiment, the user interface 42 is connected to a user service layer 52, which includes a user login block 55 and a user query block 57. The user login block 55 includes the processing and flow control of the user's personal information or corresponding de-identified personal information and the identification information of the display interface 20. The user query block 57 includes the processing and flow control of the data and analysis information that the user can query and view according to their permissions. The test subject or the queryer can view user information (including Wi-Fi name, password, etc.) and log files through the queryer interface 42 displayed on the display interface 20. With access restrictions such as identity login or verification, the queryer interface 42 also provides the test subject or the queryer with editing functions such as setting / changing user information and downloading / deleting log files.

[0028] Referring again to Figures 1 and 2, the cloud computing / analysis system 30 includes a cloud server 60, which may include or be connected to a database layer 70. In one embodiment, the cloud server 60 includes a mobile application development platform, such as Firebase provided by Google, which provides server management and maintenance, including, but not limited to, functions such as database, website deployment, file storage, and membership management. The database layer 70 includes a database 72 and a cloud storage block 74, wherein the database 72 stores de-identified personal information corresponding to the subject, personal information of the querier, etc., and the cloud storage block 74 stores data including motion energy related to muscle movements, specified action options, record files, and the results of subsequent processing and analysis of motion energy related data. Optionally, the database layer 70 may also be another database server, and the cloud computing / analysis system 30 connects to the cloud server 60. It is understood that the cloud server 60 has at least a processing unit or processing block, such as one or more processors and their associated components and circuits, to handle the above-mentioned tasks.

[0029] Referring again to Figures 1 and 2, the upper limb muscle movement assessment system 1 can perform the necessary data collection, uploading, processing / analysis processes through the execution of an application. In one embodiment, when the assessment application is installed in the wearable device 10 and started, it first performs initialization setup and executes the main loop and GUI events thread, where the GUI events include at least recording, login, and cloud communication functions. When the subject wants to perform one or more specified actions and selects to perform the recording function, the assessment application begins to store the motion energy related data generated by the muscle movements sensed by the sensor 12 into the buffer of the storage unit 16 until the subject decides to stop. The assessment application can further integrate the data and information content temporarily stored in the buffer into a record file. When the subject performs the communication function, the webpage events thread of the assessment application will start the communication transmission until manually stopped, so that other devices can connect and perform related operations through a browser. When the test subject performs cloud communication functions, the system can synchronize network time, and the web task execution threads will automatically stop after completing their work. Understandably, the software architecture of the upper limb muscle movement assessment system shown in Figure 2 can be implemented through a storage medium / device storing computer-executable program code and a processor coupled to the storage medium / device, such as an electronic device.

[0030] Figure 3 is a schematic diagram of the cloud data processing flow of the evaluation system of the present invention. Referring to Figures 1 to 3, the record file uploaded by the wearable device 10 is raw data. Considering that the record file content involves personal privacy data, the record file content (including data related to the kinetic energy generated by muscle movements) can be format-converted and packet-encrypted before uploading to become raw data. The raw data transmitted to the cloud computing / analysis system 30 is decrypted and combined in process 82 to obtain plaintext data. This plaintext data is then processed in process 84 to obtain data parameters that serve as the kinetic energy data in the present invention. Process 84, for example but not limited to, uses mathematical statistical and inductive methods to integrate the acceleration, angular velocity, etc., sensed by the sensor 12 to obtain velocity and position, and then calculates the root mean square, harmonic mean velocity, and energy per unit time, etc. Furthermore, other data / information included in the record file content, for example but not limited to, such as personal information settings, specified actions, etc., can also obtain corresponding data parameters through process 84. Next, the kinetic data undergoes correlation analysis 86, such as analyzing each specified movement and its corresponding data parameters, comparing them with the scores obtained by qualified physical therapists using the RULM assessment scale, and analyzing the correlation between the two through weighted calculations or linear regression to obtain statistical / characteristic charts as the detection result graphs of this invention. These charts can be further processed using machine learning methods 88 to obtain trend charts. In one embodiment, for example but not limited to, machine learning methods such as K-Nearest Neighbor (KNN), logistic regression, and boosting algorithms are used to analyze and classify the subject's motion data, ultimately presenting it as a trend chart. The chart includes: a time axis (horizontal axis) with selectable periods, and the degree of motion calculated using multiple parameters (vertical axis). This, combined with data such as the date of medication administration, provides physicians with a reference for understanding the patient's upper limb mobility. Furthermore, plaintext data, data parameters, statistical / characteristic charts, and trend charts can be stored in cloud storage block 74 for subjects or queryers to access after logging into the cloud server 60.

[0031] Figure 4 is a partial schematic diagram of an embodiment of the trend chart obtained by analyzing the original data of the present invention through the evaluation system. Figure 5 is a partial schematic diagram of an embodiment of the trend chart obtained by analyzing the original data of the present invention through the evaluation system. Figure 6 is a partial schematic diagram of an embodiment of the trend chart obtained by analyzing the original data of the present invention through the evaluation system. Please refer to Figures 1, 2, 4, 5, and 6 simultaneously. The test subject or the queryer can view the trend chart obtained by the evaluation system analysis through the display interface 20 and enter the queryer interface 42. For example, in Figure 4, it can be observed that the motion trend (Y-axis is kinetic energy data) of the test subject gradually increases with the increase of time; in Figure 5, it can be observed that the motion trend of the test subject remains flat with the increase of time; and in Figure 6, it can be observed that the motion trend of the test subject decreases with the increase of time.

[0032] Based on the above, this invention, through a wearable device, allows the subject to perform a designated action after activating the system app. The wearable device then collects data related to the kinetic energy generated by the muscle movements of the upper limbs during these actions. This kinetic energy data is analyzed and processed using algorithms to generate corresponding upper limb muscle movement detection results graphs (statistical / characteristic charts). This provides medical personnel with information on changes in upper limb muscle function in SMA patients' daily lives, including movements such as upper limb translation, vertical movement, and fingertip / palm grasping. This serves as an assessment of upper limb motor strength in SMA patients. Furthermore, trend charts allow observation of changes and trends in muscle strength during the medication process, providing a basis for evaluating the effectiveness of SMA medication.

[0033] The embodiments described above are merely for illustrating the technical ideas and features of the present invention. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be used to limit the patent scope of the present invention. That is, all equivalent changes or modifications made in accordance with the spirit disclosed in the present invention should still be covered within the patent scope of the present invention.

[0034] 1: Upper Limb Muscle Movement Assessment System 10: Wearable devices 12: Sensor 14: Prompt Unit 16: Storage Unit 18: Transmission Unit 19: Processing Unit 20: Display Interface 30: Cloud computing / analysis system 40: Testee Interface 50: Testee's business logic layer 52: Queries' Business Logic Layer 53: Testee Login Block 54: Record Block 55: Inquirer Login Block 56: Cloud Communication Block 57: The queryer queries the block 60: Cloud Server 70: Database Layer 72: Database 74: Cloud storage block 82: Decryption and Combination Processing 84: Calculation and Processing 86: Association Analysis and Processing 88: Machine Learning Methods

Claims

1. An upper limb muscle movement assessment system, comprising: a wearable device including a motion sensor that senses and collects motion energy-related data generated by muscle movements of an upper limb of a subject, thereby generating a record file, wherein the subject's upper limb movement includes performing a plurality of specified tasks on an upper limb strength test (Revised Upper Limb Module, RULM) assessment scale; and a cloud computing / analysis system connected to the wearable device to receive the record file uploaded by the wearable device, wherein the cloud computing / analysis system analyzes the record file to obtain at least one detection result graph.

2. The upper limb muscle movement assessment system as described in claim 1, wherein the cloud computing / analysis system includes a query interface to provide a graphical display of the detection results on a display interface.

3. The upper limb muscle movement assessment system as claimed in claim 1, wherein the motion energy related data includes an acceleration or more specifically an angular velocity.

4. The upper limb muscle movement assessment system as claimed in claim 1, wherein the cloud computing / analysis system includes storing the specified operational tasks of the upper limb muscle strength test assessment scale, and the specified operational tasks are prompted by the wearable device to prompt the test subject to perform the specified operational tasks.

5. The upper limb muscle movement assessment system as claimed in claim 1, wherein the cloud computing / analysis system includes a cloud storage block for storing the record file and the detection result graph, and a database for storing personal information corresponding to the test subject.

6. The upper limb muscle movement assessment system as claimed in claim 1, wherein the cloud computing / analysis system includes analyzing the movement energy-related data and the specified operational tasks to generate the detection result graph.

7. An upper limb muscle movement assessment system, comprising: a record block storing a record file of a subject, wherein the subject performs a plurality of specified tasks on an upper limb muscle strength test (Revised Upper Limb Module, RULM) assessment scale, and the record file includes the motion energy-related data generated by the muscle movements of the upper limb; a processing block analyzing the motion energy-related data of the record file to generate at least one detection result graph, wherein the detection result graph is derived from a result of calculating the motion energy-related data and comparing it with the subject's upper limb muscle strength test assessment scale; a cloud storage block storing the detection result graph; and a query interface connected to the processing block, which provides a queryer with the option to view the detection result graph stored in the cloud storage block.

8. The upper limb muscle movement assessment system as described in claim 7 further includes a database connected to the processing block, the database storing personal information of the test subject and personal information of the queryer.

9. The upper limb muscle movement assessment system as described in claim 7 further includes a communication block connecting the recording block and the processing block, wherein the communication block transmits the record file to the processing block.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein, The computer program causes the computer to perform the following steps: storing a record file of a subject, wherein the record file includes motion energy-related data of muscle movement of the upper limbs when the subject performs multiple specified tasks of an upper limb strength test (Revised Upper Limb Module, RULM) assessment scale; analyzing the motion energy-related data of the record file to generate at least one detection result graph, wherein the detection result graph is derived from the calculation of the motion energy-related data and a comparison with the subject's upper limb strength test assessment scale; and storing the detection result graph.

11. The computer-readable storage medium as claimed in claim 10, wherein the computer program causes a computer to perform further including providing a user interface to display the detection result graph.

12. The computer-readable storage medium as claimed in claim 10, wherein the computer program causes the computer to perform a plurality of specified operational tasks, including providing a subject interface to prompt the subject to perform the upper limb muscle strength test assessment scale.

13. The computer-readable storage medium as claimed in claim 10, wherein the motion energy related data includes an acceleration or more specifically an angular velocity.

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