Parkinson's disease detection based on user devices

Abstract

A method and user device for providing a Unified Parkinson's Disease Rating Scale (UPDRS) value through a user device, the method comprising: generating sensor data by a sensor of the user device based on a user manipulating the user device; determining a UPDRS value based on the sensor data and a deep neural network (DNN) model; and providing the UPDRS value to allow the user to be evaluated based on the UPDRS value.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Patent Application No. 16 / 431,828, filed in the U.S. Patent and Trademark Office on June 5, 2019, which is incorporated herein by reference in its entirety. Background Art

[0003] Parkinson's disease is a long-term and progressive disease of the central nervous system that primarily affects the motor system. Motor-related symptoms appear gradually, usually with a barely noticeable tremor in only one hand, which gradually worsens over time. In the early stages of Parkinson's disease, motor-related symptoms such as tremor, shaking, slow movement, and stiffness are very common. In the later stages, as the disease progresses, non-motor symptoms may appear. Patients may experience difficulty walking and symptoms related to thinking and behavior, such as sensory difficulties, sleep disorders, and emotional problems. In short, motor problems are the main symptoms of Parkinson's disease. Assessment of symptoms and disease stage has become a major focus in clinical practice. Summary of the Invention

[0004] According to one aspect of the present disclosure, a method for determining a Unified Parkinson's Disease Rating Scale (UPDRS) value associated with a user of a user device is provided, the method being performed by the user device, including: generating, by a sensor of the user device, sensor data based on manipulation of the user device by the user of the user device; determining, by a processor of the user device, a UPDRS value based on the sensor data and a deep neural network (DNN) model after generating the sensor data based on manipulation of the user device by the user of the user device; and providing, by the processor of the user device, the UPDRS value after determining the UPDRS value to allow evaluation of the user based on the UPDRS value.

[0005] According to one aspect of the present disclosure, a user device is provided, comprising: at least one memory configured to store program code; at least one processor configured to read the program code and operate according to instructions of the program code, wherein the program code comprises: a receiving code configured to cause the at least one processor to receive sensor data generated based on a user manipulating the user device from a sensor of the user device; a determining code configured to cause the at least one processor to determine a Unified Parkinson's Disease Rating Scale (UPDRS) value based on the sensor data and a deep neural network (DNN) model after receiving the sensor data generated based on the user manipulating the user device; and a providing code configured to cause the at least one processor to provide the UPDRS value after determining the UPDRS value, so as to allow the user to be evaluated based on the UPDRS value.

[0006] According to some possible implementations, a non-transitory computer-readable medium stores instructions, which include one or more instructions that, when executed by one or more processors of a user device, cause the one or more processors to: receive sensor data generated from a sensor of the user device based on user manipulation of the user device; after receiving the sensor data generated based on user manipulation of the user device, determine a Unified Parkinson's Disease Rating Scale (UPDRS) value based on the sensor data and a deep neural network (DNN) model; and after determining the UPDRS value, provide the UPDRS value to allow the user to be evaluated based on the UPDRS value. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a schematic diagram of an example implementation described in this application;

[0008] Figure 2 is a diagram of an example environment in which the systems and / or methods described herein may be implemented;

[0009] Figure 3 yes Figure 2 diagrams of example components of one or more devices; and

[0010] Figure 4 is a flow chart of an example process for determining a Unified Parkinson's Disease Rating Scale (UPDRS) value associated with a user of a user device. DETAILED DESCRIPTION

[0011] In 2015, Parkinson's disease affected 6.2 million people, resulting in 117,400 deaths worldwide. Parkinson's disease is common in people over the age of 60, affecting approximately 1% of the population. The average life expectancy after diagnosis is between 7 and 14 years. The cost of Parkinson's disease to society is significant, and Parkinson's disease reduces the quality of life for those with the disease and their caregivers. Therefore, the present disclosure utilizes one or more motion sensors of a user device to monitor the development of movements associated with Parkinson's disease, thereby assisting patients and their caregivers with an accessible and easy-to-use health monitoring tool.

[0012] Recent advances in sequence data analysis using deep neural network (DNN) methods have made significant progress. In addition, the popularity of powerful mobile devices has reached a point where applications based on artificial intelligence (AI) are accessible to most of the general public. By allowing user devices to be used as auxiliary medical diagnostic tools, the present disclosure attempts to initially screen for signs of movement-related Parkinson's disease symptoms. The present disclosure allows, among other benefits, the following: assessment of Parkinson's disease movement-related symptoms; provision of multiple types of movement tests ranging from 0 to 4 as in the Unified Parkinson's Disease Rating Scale (UPDRS) clinical test; and provision of a mobile application that is easy to install and does not require additional hardware requirements of the user device.

[0013] The medical diagnosis assistance application of the present disclosure is configured to regularly and quantitatively measure the disease state of Parkinson's disease patients.Motion sensors such as accelerometers, gyroscope sensors, etc. have been effectively embedded in most modern user devices such as mobile phones, smart watches, etc.

[0014] Multidimensional motion data can measure the movement of a body tightly attached to a user device at a sampling rate of 10Hz to 50Hz, which is sufficient to quantitatively measure motor function in Parkinson's patients. In particular, recent advances in deep neural networks and long short-term memory networks (LSTMs) have shown promising results in analyzing time series data, increasing the possibility of more accurate AI-based motion analysis for Parkinson's disease.

[0015] The present disclosure addresses the following challenges: (1) the trade-off between misdiagnosis and false positives; and (2) the architecture of the front-end and back-end communicating through an AI-based data analysis processing pipeline.

[0016] The present disclosure addresses the technical barriers involved in bringing AI-driven advanced technologies to reality and provides practical applications that are accessible and easy to use. The present disclosure separates the AI service and client application into their own, self-contained and loosely coupled components. The separation of the AI service and the client sensor collection module allows multiple components to be developed, tested and deployed in a parallel manner. In addition, with this configuration, the user device can save processing and hardware resources by eliminating the need to perform computationally expensive calculations, where the computing power and capabilities of the user device can be a constraining factor. Because the user device executing the client application is able to perform motion testing and data collection, the user is able to gain the flexibility to perform testing at a convenient location such as a home care environment.

[0017] The present disclosure is directed to practical applications that allow accurate and real-time testing of user equipment.

[0018] The proposed disclosure provides a preliminary screening tool for early detection of Parkinson's disease by assessing motor function. Furthermore, the disclosure allows for regular monitoring of Parkinson's disease status for better patient care and management. According to a first scenario of the disclosure, healthcare professionals can use the UPDRS scoring system to quantitatively assess Parkinson's disease and, if necessary, assess Parkinson's disease over a long period of time. According to another scenario of the disclosure, users can periodically perform assessments for early signs of motor dysfunction.

[0019] The model is an end-to-end deep learning model using a unidirectional LSTM-based DNN that utilizes sequential motion sensor time series data.

[0020] The application architecture follows the Model-View-ViewModel (MVVM) approach, where the data model is distinct from the DNN model described above. The DNN model is used for reasoning during the application lifecycle and provides communication between the data model and the DNN model, both of which feed into the viewer model. The viewer model is configured to serve the user interface (UI) through data binding. The DNN model can be stored in the cloud and / or locally on the mobile device.

[0021] For example, motion sensor data is recorded in real time at an adjustable rate of 10 Hz to 50 Hz, and can be analyzed locally on the user's device or remotely via a cloud service. For example, a score of 0 to 4 is provided after evaluating the sensor data. This score is correlated with the MDS-UPDRS, the Parkinson's Motor Rating Scale used to assess Parkinson's disease status.

[0022] According to one embodiment, sensor data is recorded using the user device's built-in gyroscope sensor and accelerometer along with predetermined gestures and motion guidance.

[0023] Quantitative and continuous monitoring of Parkinson's disease remains a challenge for Parkinson's disease caregivers. The present disclosure allows for patient management through readily available and easy-to-use applications that make health monitoring possible and convenient.

[0024] This disclosure provides insights for improved treatment plans and ultimately improves the quality of life for Parkinson's disease patients. This disclosure provides an AI-based mobile application that users can use to assess motor function prior to Parkinson's disease screening. The success of the DNN model can be further expanded to applications that facilitate comprehensive diagnosis. The application's MVVM design can be used to deliver better DNN models over time, ultimately making mobile devices reliable and easy-to-use health monitoring tools that can fill the emerging market for online diagnostics.

[0025] Figure 1This is a schematic diagram of the embodiment described in this application. Figure 1 As shown, a user device 100 (e.g., a mobile phone) may determine a Unified Parkinson's Disease Rating Scale (UPDRS) value associated with a user of the user device 100. For example, the user device 100 may execute an application that allows the user device 100 to determine a UPDRS value associated with a user manipulating the user device 100.

[0026] The user may correspond to a person being tested for Parkinson's disease, being monitored for symptoms of Parkinson's disease, etc. In this case, the user device 100 may execute an application allowing evaluation of the user by determining the UPDRS value of the user.

[0027] like Figure 1 As shown, sensors (e.g., accelerometers, gyroscopes, etc.) of user device 100 may generate sensor data 110 based on the user of the user device manipulating user device 100. For example, user device 100 may provide a prompt via an output component (e.g., a display, a speaker, etc.) instructing the user to manipulate user device 100 in a particular manner. As a specific example, the prompt may instruct the user to keep the phone relatively still, perform a predetermined gesture or action, etc. In response to the user manipulating user device 100, the sensors of user device 100 may generate sensor data.

[0028] Further Figure 1 As shown, the processor of the user device 100 may determine whether the measurement condition 120 is satisfied. For example, the measurement condition may correspond to a threshold amount of time that has passed, a threshold amount of sensor data that has been generated, a predetermined gesture that has been performed, a threshold amount of accurate sensor data that has been generated, a set of prompts that have been completed, etc. Figure 1 As shown, as a specific example, the measurement condition may correspond to 15 seconds having passed since user device 100 began measuring sensor data, since a user caused an application to execute, since a sensor began generating data, and so on.

[0029] Further Figure 1 As shown, the processor of the user device 100 may determine a UPDRS value 130 based on the sensor data and a deep neural network (DNN) model. For example, the user device 100 may input the generated sensor data into the DNN model and determine the UPDRS value 130 based on the output of the model. The UPDRS value 130 may include a Movement Disorder Society (MDS) UPDRS value and may include values from 1, 2, 3, 4, etc.

[0030] The user device 100 may receive a trained model from a cloud-based platform (e.g., a server) and store the model. In this manner, the user device 100 may conserve processor and / or memory resources by reducing the need to perform computationally expensive calculations. The cloud-based platform may utilize machine learning techniques to analyze data and generate a model. For example, the cloud-based platform may analyze millions, billions, trillions, etc. of data points and generate a model that correlates sensor data with UPDRS values. In this manner, the user device 100 may receive the model and utilize it in conjunction with an application that allows the determination of UPDRS values 130.

[0031] Further Figure 1 As shown, the processor of the user device 100 may provide the UPDRS value 130 to allow the user to be evaluated based on the UPDRS value 130. For example, the user device 100 may provide information identifying the UPDRS value 130 via an output component (e.g., a user interface) to allow the user, a doctor, a caregiver, a medical professional, etc. to identify the UPDRS value.

[0032] In other cases, user device 100 may provide UPDRS value 130 to a group of other devices in real time via a network. In some implementations, user device 100 may provide UPDRS value 130 in a standardized format to allow various databases and records to be updated based on the UPDRS value.

[0033] The user equipment 100 may use a standardization technique to standardize the UPDRS value 130 so that a group of devices can utilize the UPDRS value 130 .

[0034] The user device 100 may collect the user's medical information and convert and consolidate the medical information into a standardized format. In addition, the user device 100 may generate a UPDRS value 130 associated with the standardized format. The user device 100 may store the standardized UPDRS value 130 in a set of network-based storage devices (e.g., a cloud-based platform) and generate a message whenever a UPDRS value is generated, updated, etc., which may notify healthcare providers, doctors, medical personnel, patients, etc.

[0035] Furthermore, the user device 100 may provide the UPDRS value 130 to a group of devices in real time (e.g., substantially simultaneously with the generation of the UPDRS value 130) to allow the group of devices to update and / or utilize the UPDRS value 130 in real time. In this manner, each user of a group of devices may immediately access the user's latest UPDRS value 130.

[0036] In this way, and compared to non-standardized medical information associated with different medical providers, some implementations of the present invention allow standardized medical information and / or UPDRS values 130 to be generated in real time and provided to multiple different devices, thereby allowing different users to share medical information and / or UPDRS values 130.

[0037] Furthermore, in this manner, some implementations herein allow for the real-time provision of complete and accurate medical information and / or UPDRS values 130. Compared to a situation where multiple different medical professionals have incomplete or inaccurate medical or diagnostic information, some implementations herein allow for complete and accurate medical information and UPDRS values 130 to be disseminated and easily shared among medical professionals.

[0038] Figure 2 FIG is a diagram of an example environment 200 in which the systems and / or methods described herein may be implemented. Figure 2 As shown, environment 200 may include user device 210, platform 220, and network 230. The devices of environment 200 may be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections.

[0039] User device 210 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information associated with platform 220. For example, user device 210 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smartphone, a wireless phone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or the like. In some implementations, user device 210 may receive information from platform 220 and / or send information to platform 220.

[0040] The platform 220 includes one or more devices capable of determining a Unified Parkinson's Disease Rating Scale (UPDRS) value associated with a user of a user device, as described elsewhere herein. In some implementations, the platform 220 may include a cloud server or a group of cloud servers. In some implementations, the platform 220 may be designed as a modular platform so that certain software components can be swapped in or out based on specific needs. Thus, the platform 220 can be easily and / or quickly reconfigured for different uses.

[0041] In some implementations, as shown, the platform 220 can be hosted in a cloud computing environment 222. It should be noted that while the implementations described herein describe the platform 220 as being hosted in a cloud computing environment 222, in some implementations, the platform 220 is not cloud-based (i.e., can be implemented outside of a cloud computing environment) or can be partially cloud-based.

[0042] Cloud computing environment 222 includes an environment hosting platform 220. Cloud computing environment 222 can provide computing, software, data access, storage, and other services that do not require end users (e.g., user devices 210) to be aware of the physical location and configuration of the systems and / or devices hosting platform 220. As shown, cloud computing environment 222 may include a set of computing resources 224 (the set of computing resources is collectively referred to as "computing resources 224," and each computing resource is referred to as "computing resource 224").

[0043] Computing resources 224 include one or more personal computers, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, computing resources 224 may control platform 220. Cloud resources may include computing instances running on computing resources 224, storage devices provided on computing resources 224, data transmission devices provided by computing resources 224, and the like. In some implementations, computing resources 224 may communicate with other computing resources 224 via wired connections, wireless connections, or a combination of wired and wireless connections.

[0044] Further Figure 2 As shown, the computing resources 224 include a set of cloud resources, such as one or more applications ("APP") 224-1, one or more virtual machines ("VM") 224-2, virtualized storage ("VS") 224-3, one or more hypervisors ("HYP") 224-4, etc.

[0045] The applications 224-1 include one or more software applications that can be provided to or accessed by the user device 210 and / or the sensor device 220. The applications 224-1 can eliminate the need to install and run software applications on the user device 210. For example, the applications 224-1 can include software associated with the platform 220 and / or any other software that can be provided through the cloud computing environment 222. In some implementations, one application 224-1 can send information to / receive information from one or more other applications 224-1 through the virtual machine 224-2.

[0046] Virtual machine 224-2 includes a software implementation of a machine (e.g., a computer) that runs a program, similar to a physical machine. Depending on the degree to which virtual machine 224-2 corresponds to any actual machine and its purpose, virtual machine 224-2 can be a system virtual machine or a process virtual machine. A system virtual machine can provide a complete system platform that supports the operation of a complete operating system ("OS"). A process virtual machine can run a single program and can support a single process. In some implementations, virtual machine 224-2 can run on behalf of a user (e.g., user device 210) and can manage the infrastructure of cloud computing environment 222, such as data management, synchronization, or long-term data transfer.

[0047] The virtualized memory 224-3 includes one or more storage systems and / or one or more devices that use virtualization technology within the storage system or device of the computing resource 224. In some implementations, in the context of the storage system, the types of virtualization may include block virtualization and file virtualization. Block virtualization may refer to the abstraction (or separation) of logical storage from physical storage, making it possible to access the storage system without considering physical storage or heterogeneous structures. Separation allows administrators of the storage system to have flexibility in how the administrator manages the storage of end users. File virtualization eliminates the dependency between data accessed at the file level and the location where the file is physically stored. This can optimize storage usage, server consolidation, and / or non-intrusive file migration performance.

[0048] Hypervisor 224-4 can provide hardware virtualization technology that allows multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer, such as computing resource 224. Hypervisor 224-4 can present a virtual operating platform to the guest operating systems and can manage the operation of the guest operating systems. Multiple instances of each operating system can share virtualized hardware resources.

[0049] The network 230 includes one or more wired networks and / or wireless networks. For example, the network 230 may include a cellular network (e.g., a fifth generation (5G) network, a long term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-optic-based network, etc., and / or a combination of these or other types of networks.

[0050] Figure 2 The number and arrangement of devices and networks shown are provided as examples. In practice, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or different Figure 2The devices and / or networks shown may be arranged differently. Figure 2 Two or more of the devices shown may be implemented in a single device, or Figure 2 The single device shown may be implemented as multiple distributed devices. Additionally or alternatively, one set of devices (eg, one or more devices) of environment 200 may perform one or more functions described as being performed by another set of devices of environment 200.

[0051] Figure 3 is a diagram of example components of a device 300. Device 300 may correspond to user device 210 and / or platform 220. Figure 3 As shown, device 300 may include a bus 310 , a processor 320 , a memory 330 , a storage component 340 , an input component 350 , an output component 360 , and a communication interface 370 .

[0052] The bus 310 includes components that allow communication between components of the device 300. The processor 320 is implemented in hardware, firmware, or a combination of hardware and software. The processor 320 is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or another type of processing component. In some implementations, the processor 320 includes one or more processors that can be programmed to perform functions. The memory 330 includes random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions for use by the processor 320.

[0053] The storage component 340 stores information and / or software related to the operation and use of the device 300. For example, the storage component 340 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette, a magnetic tape, and / or another type of non-transitory computer-readable medium, and a corresponding drive.

[0054] Input components 350 include components that allow device 300 to receive information, such as input via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, buttons, switches, and / or a microphone). Additionally or alternatively, input components 350 may include sensors for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and / or an actuator). Output components 360 include components that provide output information from device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs)).

[0055] The communication interface 370 includes transceiver-like components (e.g., a transceiver and / or a separate receiver and transmitter) that enable the device 300 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection. The communication interface 370 may allow the device 300 to receive information from another device and / or provide information to another device. For example, the communication interface 370 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0056] Device 300 can perform one or more of the processes described herein. Device 300 can perform these processes in response to processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and / or storage component 340. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space distributed across multiple physical storage devices.

[0057] The software instructions may be read into the memory 330 and / or storage component 340 from another computer-readable medium or from another device via the communication interface 370. When executed, the software instructions stored in the memory 330 and / or storage component 340 may cause the processor 320 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, the implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0058] Figure 3 The number and arrangement of components shown are provided as examples. In practice, device 300 may include additional components, fewer components, different components, or components that are different from the components shown. Figure 3 Additionally or alternatively, one set of components (eg, one or more components) of device 300 may perform one or more functions described as being performed by another set of components of device 300.

[0059] Figure 4 is a flow chart of an example process 400 for determining a Unified Parkinson's Disease Rating Scale (UPDRS) value associated with a user of a user device. In some implementations, the process may be performed by the user device 210. Figure 4 In some implementations, the process may be performed by another device or group of devices (e.g., platform 220) that is separate from or includes the user device 210. Figure 4One or more process blocks.

[0060] like Figure 4 As shown, process 400 may include generating sensor data by a sensor of a user device based on a user of the user device manipulating the user device (block 410).

[0061] Further Figure 4 As shown, process 400 may include determining, by a processor of the user equipment, whether a measurement condition is met (block 420).

[0062] Further Figure 4 As shown, if the measurement condition is not met (block 420 —No), process 400 may include generating additional sensor data (returning to block 410 ).

[0063] Further Figure 4 As shown, if the measurement condition is met (block 420 —Yes), process 400 may include determining, by a processor of the user device, a UPDRS value based on the sensor data and a deep neural network (DNN) model (block 430 ).

[0064] Further Figure 4 As shown, process 400 may include providing, by a processor of the user device, a UPDRS value to allow evaluation of the user based on the UPDRS value (block 440).

[0065] Although Figure 4 Example blocks of process 400 are shown, but in some implementations, process 400 may include additional blocks, fewer blocks, different blocks, or different blocks. Figure 4 The depicted blocks are blocks that are arranged differently. Additionally or alternatively, two or more blocks of process 400 may be performed in parallel.

[0066] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations.

[0067] As used herein, the term "component" is intended to be broadly interpreted as hardware, firmware, or a combination of hardware and software.

[0068] Obviously, the systems and / or methods described herein can be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to specific software code - it is understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0069] Even if particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features can be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may only be directly dependent on one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

[0070] Unless explicitly described in themselves, the elements, actions or instructions used herein should not be understood to be essential or necessary. In addition, as used herein, the articles "a" and "an" are intended to include one or more items and can be used interchangeably with "one or more". In addition, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, a combination of related items and unrelated items, etc.), and can be used interchangeably with "one or more". In the case where the intention is only one item, the term "a" or similar language is used. In addition, as used herein, the terms "have", "have", "contain" or similar terms are intended to be open terms. In addition, the meaning of the phrase "based on" is intended to be "based at least in part on", unless otherwise expressly stated.

Claims

1. A method for determining a Unified Parkinson's Disease Rating Scale (UPDRS) value associated with a user of a user device, the method being performed by the user device, the method comprising: providing a prompt for a predetermined gesture via an output component of the user device, wherein the user device executing an application is used to perform motion testing and data collection; generating sensor data by a sensor built into the user device based on manipulation of the user device by a user of the user device; determining whether the sensor data satisfies measurement conditions; the measurement conditions comprising: a threshold amount of time that has elapsed, a threshold amount of the sensor data that has been generated, the predetermined gesture that has been performed, a threshold amount of accurate sensor data that has been generated, and a set of the predetermined gestures that have been completed; displaying on the user device a time series of the sensor data and an indication of whether the sensor data has reached the threshold amount of elapsed time; After the sensor data satisfying the measurement condition is generated based on manipulation of the user device by the user of the user device, a processor of the user device determines the UPDRS value based on the sensor data satisfying the measurement condition and a deep neural network (DNN) model, wherein the deep neural network (DNN) model is stored in a cloud or on the user device, and the DNN model is updated using a Model-View-ViewModel (MVVM) design of the application; and After determining the UPDRS value, the processor of the user device provides the UPDRS value to allow the application to evaluate the user based on the UPDRS value, converts the UPDRS value into a standardized format, generates a standardized UPDRS value associated with the standardized format, and provides the standardized UPDRS value to a group of devices to allow the group of devices to update and / or utilize the standardized UPDRS value. The user device provides the UPDRS value to the group of devices in real time to allow the group of devices to update and / or utilize the UPDRS value in real time, so as to allow each user of the group of devices to access the user's latest UPDRS value.

2. The method according to claim 1, characterized in that The sensor is an accelerometer.

3. The method according to claim 1, characterized in that The sensor is a gyroscope.

4. The method according to any one of claims 1 to 3, characterized in that The UPDRS value is the Movement Disorder Society MDS UPDRS value.

5. The method according to any one of claims 1 to 3, characterized in that The UPDRS value is at least one of zero, one, two, three or four.

6. The method according to any one of claims 1 to 3, characterized in that Before the processor of the user equipment determines the UPDRS value based on the sensor data that meets the measurement condition and a deep neural network (DNN) model, the method further includes: The DNN model is received from a server device.

7. The method according to any one of claims 1 to 3, characterized in that The generating of sensor data by the sensor of the user device based on a user of the user device manipulating the user device includes generating the sensor data based on providing a prompt for the predetermined gesture via an output component of the user device.

8. A user equipment comprising: at least one memory configured to store program code; at least one processor configured to read the program code and operate according to instructions of the program code, wherein the program code includes: providing code configured to cause the at least one processor to provide a prompt for a predetermined gesture from an output component of the user device, wherein the user device executing an application is used to perform motion testing and data collection; receiving code configured to cause the at least one processor to receive, from a sensor built into the user device, sensor data generated based on manipulation of the user device by a user of the user device; determining code configured to cause the at least one processor, after receiving sensor data generated based on a user operating the user device, to determine whether the sensor data satisfies a measurement condition; the measurement condition comprising: a threshold amount of time that has elapsed, a threshold amount of sensor data that has been generated, the predetermined gesture that has been performed, a threshold amount of accurate sensor data that has been generated, and a set of the predetermined gestures that have been completed; display code configured to cause the at least one processor, upon receiving sensor data generated based on a user of the user device operating the user device, to display on the user device a time series of the sensor data and a prompt indicating whether the sensor data has reached the threshold amount of elapsed time; The determination code is further configured to cause the at least one processor to determine a Unified Parkinson's Disease Rating Scale (UPDRS) value based on the sensor data that meets the measurement condition and is generated based on a user of the user device manipulating the user device, after receiving the sensor data that meets the measurement condition and is generated based on the user device being manipulated by the user device, wherein the deep neural network (DNN) model is stored in a cloud or on the user device, and the DNN model is updated using a Model-View-ViewModel (MVVM) design of the application; and The providing code is further configured to enable the at least one processor to provide the UPDRS value after determining the UPDRS value, so as to allow the application to evaluate the user based on the UPDRS value, convert the UPDRS value into a standardized format, generate a standardized UPDRS value associated with the standardized format, and provide the standardized UPDRS value to a group of devices, so as to allow the group of devices to update and / or utilize the standardized UPDRS value. The user device provides the UPDRS value to the group of devices in real time, so as to allow the group of devices to update and / or utilize the UPDRS value in real time, so as to allow each user of the group of devices to access the user's latest UPDRS value.

9. The user equipment according to claim 8, wherein: The sensor is an accelerometer.

10. The user equipment according to claim 8, wherein: The sensor is a gyroscope.

11. The user equipment according to any one of claims 8 to 10, characterized in that: The UPDRS value is the Movement Disorder Society MDS UPDRS value.

12. The user equipment according to any one of claims 8 to 10, characterized in that: The UPDRS value is at least one of zero, one, two, three or four.

13. The user equipment according to any one of claims 8 to 10, characterized in that: The receiving code is further configured to cause the at least one processor to receive the DNN model from a server device.

14. The user equipment according to any one of claims 8 to 10, characterized in that: The receiving code is further configured to cause the at least one processor to receive the sensor data from a sensor of the user device based on providing a prompt for the predetermined gesture via an output component of the user device.

15. A non-transitory computer-readable medium storing instructions, the instructions comprising one or more instructions which, when executed by one or more processors of a user device, cause the one or more processors to cooperate with sensors of the user device to implement a method according to any one of claims 1 to 7.

16. A user equipment comprising: providing a module configured to provide a prompt for a predetermined gesture from an output component of the user device, wherein the user device executing an application is used to perform motion testing and data collection; a receiving module configured to receive sensor data generated based on a user of the user device manipulating the user device from a sensor built into the user device; a determination module configured to, after receiving the sensor data generated based on a user of the user device manipulating the user device, determine whether the sensor data satisfies a measurement condition; the measurement condition comprising: a threshold amount of time that has elapsed, a threshold amount of sensor data that has been generated, the predetermined gesture that has been performed, a threshold amount of accurate sensor data that has been generated, and a set of the predetermined gestures that have been completed; a display module configured to, after receiving sensor data generated based on a user of the user device operating the user device, display on the user device a time series of the sensor data and a prompt indicating whether the sensor data reaches the threshold amount of elapsed time; The determination module is configured to, after receiving the sensor data that satisfies the measurement condition and is generated based on a user of the user device manipulating the user device, determine a Unified Parkinson's Disease Rating Scale (UPDRS) value based on the sensor data that satisfies the measurement condition and a deep neural network (DNN) model, wherein the deep neural network (DNN) model is stored in a cloud or on the user device, and the DNN model is updated using a Model-View-ViewModel (MVVM) design of the application; and The providing module is configured to, after determining the UPDRS value, provide the UPDRS value to allow the application to evaluate the user based on the UPDRS value, convert the UPDRS value into a standardized format, generate a standardized UPDRS value associated with the standardized format, and provide the standardized UPDRS value to a group of devices to allow the group of devices to update and / or utilize the standardized UPDRS value. The user device provides the UPDRS value to the group of devices in real time to allow the group of devices to update and / or utilize the UPDRS value in real time, so as to allow each user of the group of devices to access the user's latest UPDRS value.

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