Information processing device, information processing system, information processing method, and program

Abstract

The information processing device includes a data acquisition unit that acquires one or more pieces of user data related to a neuromuscular disease selected from (a) to (k) multiple times during a predetermined period, and a generation unit that generates information to be provided to a predetermined terminal based on the user data: (a) data related to typing, (b) data related to walking, (c) data related to speech, (d) data related to sleep, (e) data related to breathing, (f) data related to facial expressions, (g) data related to fine motor skills, (h) data related to gross motor skills, (i) answers to questionnaires about disease symptoms, (j) information automatically collected by a built-in sensor of the device, and (k) data from a medical institution.

Description

[Technical field]

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 313,084, filed February 23, 2022, the contents of which are incorporated herein by reference in their entirety.

[0002] The present invention relates to an information processing device, an information processing system, an information processing method, and a program. [Background technology]

[0003] Neuromuscular disease is a general term for diseases that cause movement disorders due to lesions in the nerves themselves, such as the brain, spinal cord, and peripheral nerves, or lesions in the muscles themselves, and representative diseases include Parkinson's disease, spinocerebellar degeneration, amyotrophic lateral sclerosis, neuritis and myelitis caused by viruses or bacteria, myasthenia gravis, muscular dystrophy, and polymyositis (Non-Patent Documents 1 to 3). These neuromuscular diseases share movement disorders as a common main symptom.

[0004] For example, amyotrophic lateral sclerosis (hereinafter, also referred to as "ALS"), one example of a neuromuscular disease, is a rapidly progressing, fatal, severe disease in which voluntary movement is impaired due to selective degeneration and loss of upper and lower motor neurons, and weakness of the upper and lower limbs, bulbar paralysis, and respiratory muscle paralysis progress gradually, often resulting in the need for respiratory management due to respiratory failure 2 to 5 years after onset.

[0005] There are individual differences in the type and progression of atrophied muscles in ALS patients. In addition, since there is no specific marker for ALS, current diagnosis is basically a diagnosis of exclusion. As a diagnostic standard, for example, the revised EL Escorial diagnostic criteria are available, but the diagnostic sensitivity is low, and in practice, clinical diagnosis is performed comprehensively. In addition, there is also electrophysiological testing, but this test places a heavy burden on the patient and cannot grasp the progression and severity of the pathology, so the progression of ALS is evaluated visually, making early and highly sensitive diagnosis difficult. Therefore, in order to quantitatively and objectively evaluate limb ability, there is a technology that improves the convenience of measuring limb ability using, for example, motion capture technology (for example, Patent Document 1, etc.). The entire contents of the documents listed below are incorporated herein by reference. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Patent No. 6465419 [Non-Patent Document 1] Rare Disease Information Center Internet<URL:https: / / www.nanbyou.or.jp / entry / 5347♯01> [Non-Patent Document 2] Japan Society of Neurology Internet<URL:https: / / www.neurology-jp.org / public / disease / index.html♯about> [Non-Patent Document 3] Japan Orthopaedic Association, List of Symptoms of Neuromuscular Diseases, Internet<URL:https: / / www.joa.or.jp / public / sick / body / nerve.html> Summary of the Invention [Means for solving the problem]

[0007] An information processing device according to one aspect of the present invention includes: A data acquisition unit that acquires one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period of time; an information generating unit that generates information to be provided to a predetermined terminal based on the user data; Equipped with. (a) Data relating to one or more of your typing actions selected from the group consisting of speed, accuracy, duration and volume of typing. (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) one or more of the following sleep-related data: sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions

[0008] An information processing device according to another aspect of the present invention includes: A data acquisition unit that acquires one or more user data related to a direct or indirect functional abnormality of the motor nervous system selected from the following (a) to (k) multiple times during a predetermined period of time; and an information generating unit that generates information to be provided to a predetermined terminal based on the user data. (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions

[0009] An information processing method according to one aspect of the present invention includes: An information processing method using a computer, comprising: acquiring one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period; generating information to be provided to a predetermined terminal based on the user data; has. (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions

[0010] A program according to one aspect of the present invention comprises: A program for causing a computer to execute an information processing method, The program includes the following steps as the information processing method: acquiring one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period; generating information to be provided to a predetermined terminal based on the user data; to be executed by the computer. (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an information processing system including an information processing device. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Diagram 3] FIG. 3 is a diagram illustrating an example of a software configuration of the information processing device. [Figure 4] FIG. 4 is a diagram illustrating an example of a configuration of user basic information stored in the user information storage unit. [Diagram 5] FIG. 5 is a diagram illustrating an example of the configuration of user data stored in the user data storage unit. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of doctor-input information stored in the doctor-input information storage unit. [Figure 7] FIG. 7 is a diagram illustrating an example of the configuration of the provided information stored in the provided information storage unit. [Figure 8] FIG. 8 is a diagram showing a flow of processing executed in the information processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In this specification and the drawings, the same reference numerals indicate corresponding or identical elements throughout the drawings.

[0013] A specific example of an information processing apparatus 10 according to an embodiment of the present invention will be described with reference to the drawings.

[0014] In this embodiment, examples of the "neuromuscular disease" include Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, spinocerebellar degeneration, progressive supranuclear palsy, multiple system atrophy, multiple sclerosis, neuromyelitis optica, adrenoleukodystrophy, metachromatic leukodystrophy, hyperglutaric acidemia type I, phenylketonuria, GM1 gangliosidosis, GM2 gangliosidosis, mucolipidosis type II (I-cell disease), Angelman syndrome, Krabbe disease, Batten disease, mucopolysaccharidosis, Rett's disease, Niemann-Pick disease A, B, C, spinal cord injury, inclusion body myositis, myasthenia gravis, hereditary spastic paraplegia, primary lateral sclerosis, Charcot-Marie-Tooth disease, spinal muscular atrophy, Friedreich's disease, and Streptococcus aureus. These include ataxia, dermatomyositis, polymyositis, Guillain-Barre syndrome, chronic inflammatory and demyelinating polyneuropathy, Lumber-Eaton myasthenia, multifocal motor neuropathy, anti-MAG peripheral neuropathy, facioscapulohumeral muscular dystrophy, muscular dystrophy, myotonic dystrophy, Duchenne muscular dystrophy, facioscapulohumeral muscular dystrophy, spinal-bulbar muscular atrophy, mitochondrial diseases, Leigh encephalopathy, MELAS (mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes syndrome), fragile X-associated tremor / ataxia syndrome (FXTAS), Pericheis-Merzbach disease (PMD), viral or bacterial neuritis or myelitis, cerebral infarction, cervical spondylosis, and spondylosis. Although cerebral infarction, cervical spondylosis, and myelopathy are not generally included in neuromuscular diseases, they are diseases that cause movement disorders, and in this embodiment, they are considered to be synonymous with neuromuscular diseases.

[0015] In this embodiment, "direct or indirect functional abnormality of the motor nervous system" refers to, for example, symptoms or test results related to functional abnormality of the motor nervous system that a doctor checks on a patient to diagnose a neuromuscular disease. Examples of symptoms or test results directly related to functional abnormality of the motor nervous system include those related to typing, walking, speaking, breathing, facial expressions, or fine or gross movements. Examples of symptoms or test results indirectly related to functional abnormality of the motor nervous system include those related to sleep.

[0016] <Configuration> 1 is a diagram showing an example of an information processing system 1 including an information processing device 10 according to the present embodiment. In this embodiment, the information processing device 10 is communicably connected to a user terminal 20 used by a user such as a patient and a doctor terminal 30 used by a doctor via a network NW.

[0017] In this embodiment, the network NW is, for example, the Internet. The network NW is constructed by, for example, a public telephone line network, a mobile phone line network, a wireless communication network, Ethernet (registered trademark), etc.

[0018] The information processing device 10 is a terminal managed by a medical institution or an organization that provides medical information, and constitutes a part of the information providing system 1 by executing information processing with the user terminal 20 and the doctor terminal 30 via a network NW. The information processing device 10 may be, for example, a workstation or a general-purpose computer such as a personal computer, or may be logically realized by cloud computing. The information processing device 10 may have installed thereon an application or the like that enables communication with the user terminal 20 and the doctor terminal 30, or may have installed thereon a browser for accessing a web service that enables the communication.

[0019] The user terminal 20 is a terminal that is mainly used by the user to input data and the like, and executes information processing with the information processing device 10 and the doctor terminal 30 via the network NW. The user terminal 20 may be, for example, a general-purpose computer such as a workstation or a personal computer, or may be a mobile communication device such as a smartphone. The user terminal 20 may also be a digital device such as a wearable device worn by the user himself. The user terminal 20 may have installed therein an application or the like that enables communication with the information processing device 10 or the doctor terminal 30, or may have installed therein a browser for accessing a web service that enables the communication. The user terminal 20 may also be a smartphone that the user originally owns, or a terminal provided by the hospital, as long as it is used by the user to input data. The person who inputs data into the user terminal 20 is not limited to the user himself, and the user terminal 20 may also be a terminal used by the user's family, a caregiver who cares for the user, or a representative who represents the user.

[0020] The doctor terminal 30 is a terminal used by a doctor working in a medical institution such as a hospital to grasp the user's condition, and executes information processing with the information processing device 10 and the user terminal 20 via a network NW. The doctor terminal 30 may be, for example, a general-purpose computer such as a workstation or a personal computer, or may be a mobile communication device such as a smartphone. An application capable of communicating with the information processing device 10 or the user terminal 20 may be installed on the doctor terminal 30, or a browser for accessing a web service that enables the communication may be installed on the doctor terminal 30.

[0021] <Hardware configuration> 2 is a diagram showing an example of the hardware configuration of a computer that realizes the information processing device 10 according to this embodiment. The computer includes at least a control unit 11, a memory 12, a storage 13, a communication unit 14, and an input / output unit 15. These are electrically connected to each other via a bus 16.

[0022] The control unit 11 is a computing device that controls the operation of the entire information processing device 10, and performs control of transmission and reception of data between each element, and information processing required for application execution and authentication processing, etc. For example, the control unit 11 is a processor such as a CPU (Central Processing Unit), and performs each information processing by executing programs, etc. stored in the storage 13 and deployed in the memory 12.

[0023] The memory 12 includes a main memory configured with a volatile storage device such as a DRAM (Dynamic Random Access Memory) and an auxiliary memory configured with a non-volatile storage device such as a flash memory or an HDD (Hard Disc Drive). The memory 12 is used as a work area for the control unit 11, and also stores a BIOS (Basic Input / Output System) executed when the information processing device 10 is started up, various setting information, and the like.

[0024] The storage 13 stores various programs such as application programs. A database that stores data used in each process may be constructed in the storage 13.

[0025] The communication unit 14 connects the information processing device 10 to a network. The communication unit 14 communicates with an external device directly or via a network access point, for example, by a wired LAN (Local Area Network), a wireless LAN, Wi-Fi (Wireless Fidelity, registered trademark), infrared communication, Bluetooth (registered trademark), short-distance or non-contact communication, or the like.

[0026] The input / output unit 15 is, for example, an information input device such as a keyboard, a mouse, a touch panel, etc., and an output device such as a display.

[0027] A bus 16 is commonly connected to all of the above elements, and transmits, for example, address signals, data signals and various control signals.

[0028] In this embodiment, the hardware configuration of a terminal such as a computer or a smartphone that realizes the user terminal 20 and the doctor terminal 30 is similar to the hardware configuration example of the information processing device 10 shown in FIG. 2, so a description thereof will be omitted.

[0029] <Software configuration> 3 is a diagram showing an example of the software configuration of the information processing device 10 according to this embodiment. The information processing device 10 can include a functional unit including a data acquisition unit 101, an analysis unit 102, an information generation unit 103, an information provision unit 104, a user terminal notification unit 105, and a doctor terminal notification unit 106, and a storage unit including a user information storage unit 111, a user data storage unit 112, a doctor input information storage unit 113, and a provision information storage unit 114.

[0030] The data acquisition unit 101, the analysis unit 102, the information generation unit 103, the information provision unit 104, the user terminal notification unit 105, and the doctor terminal notification unit 106 are realized by the control unit 11 provided in the information processing device 10 reading out programs stored in the storage 13 into the memory 12 and executing them. The user information storage unit 111, the user data storage unit 112, the doctor input information storage unit 113, and the provided information storage unit 114 are each realized as a part of a storage area provided in at least one of the memory 12 and the storage 13.

[0031] The user information storage unit 111 stores, for example, basic information of a user acquired by the data acquisition unit 101. Fig. 4 shows an example of a data configuration of the user basic information stored in the user information storage unit 111. The user basic information may be linked to a user ID. The user basic information may include information on attributes such as the user ID, age, sex, occupation, and place of origin of the user, information on chronic illnesses, medical history, allergies, constitution (obesity, frailty, etc.), diet, drinking, smoking, exercise habits, and other lifestyle habits, and may include the user's name, address, height, and weight as necessary.

[0032] The user data storage unit 112 stores user data acquired multiple times during a predetermined period by the data acquisition unit 101. The user data is one or more items related to direct or indirect functional abnormalities of the motor nervous system, and includes, for example, one or more items of user data related to neuromuscular diseases selected from the following (a) to (j), which are acquired, for example, from the user terminal 20.

[0033] (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device

[0034] 5 is a configuration example of user data stored in the user data storage unit 112. One or more pieces of user data related to one or more neuromuscular diseases of a user selected from the above (a) to (j) may be linked to a user ID.

[0035] (a) Examples of "data related to typing" include the speed, accuracy, time, and amount of typing. Data related to typing is, for example, data on input operations into the user terminal 20, the time and speed required for the input operations, the proportion of re-inputs, what parts are pressed during the input operations, and what words are searched for on LINE or in a browser.

[0036] The data on typing actions may be data automatically acquired from a GPS, an accelerometer, a text log, screen event data, etc. built into the user terminal 20, or data actively acquired from a task for a survey. The data on typing actions may be, for example, keystroke data typed by a user, as disclosed in the specification of U.S. Patent Application Publication No. 2021 / 0236044.

[0037] (b) Examples of "walking data" include the number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, whole body lateral swing, and rate of falls during walking. The walking data is data acquired from a wearable device or a smartphone, and is automatically acquired from an accelerometer built into the wearable device. The walking data is data about the user's walking over a predetermined period of time detected and recorded by a pedometer built into a wearable device or a smartphone, as disclosed in U.S. Pat. No. 9,408,560. The walking data may be images or videos of the user taken using a camera or the like. In order to easily acquire data without using complicated measuring equipment and to more easily detect signs of neuromuscular diseases from daily life activities, it is preferable to include step count data as data related to the above (b).

[0038] (c) Examples of "data related to speech" include voice data of conversations, call records, speech rate, speech time, sustained speech, number of words, speech disorder, frequency of unclear speech, pause period, non-speech sounds (such as nasal sounds), and frequency of coughing. Data related to speech includes data that continuously acquires voice recordings of the voice and evaluates the deterioration of the voice over time. These data are acquired by a voice recording device that is a smartphone, a smart watch, a wearable sensor, a computing device, a headset, a headband, or a combination thereof, as disclosed in International Publication No. 2021 / 150989. In order to more easily capture signs of neuromuscular diseases from everyday conversations or short voice recordings, it is preferable to include pause period data as data related to the above (c).

[0039] (d) Examples of "sleep data" include sleep time, sleep efficiency, eye movement, and frequency of awakening. The sleep data is, for example, data on the circadian rhythm of sleep, sleep onset and wake-up times, and duration obtained using a wearable electronic device, as disclosed in International Publication No. 2019 / 106230. The sleep data may be data on sleep patterns, sleep time, wake-up times, sleep depth, and number of REM sleep periods obtained using a smartphone, smart watch, wearable sensor, etc., or may be images or videos of the user taken using a camera, etc.

[0040] (e) Examples of "respiratory data" include data on lung function, such as vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency. Respiratory data is collected using, for example, a monitoring device such as a smartphone spirometer or a spirometer (pulmonary function test). Data is collected by having the subject hold a mouthpiece in their mouth, pinch their nose, and breathe in response to the technician's instructions, measuring the volume and speed of air entering and leaving their lungs.

[0041] (f) Examples of "data on facial expressions" include the opening and width between the upper and lower lips, the movement of the lips, the speed and acceleration of the opening, convulsions, the surface of the mouth, the average symmetry ratio of the left and right surfaces of the mouth, the vertical position of the eyebrows, the opening of the eyes, the translation and rotation vector of the head tilt, and the movement of the eyeballs. Data on facial expressions is obtained by analyzing the facial expressions of a person shown in a video prepared by a user using an emotion recognition AI and outputting the emotions of the person that can be read from the video as numerical data, as disclosed in JP 2020-537579 A, ​​which is the Japanese translation of the PCT international application publication. Also, as disclosed in JP 2018-007792 A, data on facial expressions is obtained from a facial image of a person shown in a video. Data on facial expressions may be images or videos of a user taken using a camera or the like.

[0042] (g) "Fine motor" generally refers to movements required for fine and precise movements using hands or fingers, and includes writing, using chopsticks, fastening buttons, grasping small objects, etc. In this embodiment, "data on fine motor" is data obtained from a test in which, for example, a Drawing test, which is usually performed in analog, is performed on a digital device. Specific tests include tests in which a picture is drawn on the screen surface of a smartphone or the like, tracing a presented picture, and moving a geometric figure from right to left. Signs can be found from the accuracy of the response in each test or the time required for the test. Data on fine motor is obtained using a technology for inputting with a finger into a mobile terminal, such as the Drawing test described in JP 2021-77412 A. In order to more easily capture signs of neuromuscular disease from daily life activities using digital devices, it is preferable to include data on fine motor movements performed using the thumb and index finger as data on the above (g).

[0043] (h) "Gross movement" refers to movement using the whole body such as maintaining posture and moving, and includes movements such as walking, running, jumping, and throwing. In this embodiment, examples of "data related to gross movement" include the movement of changing the position of the arms, going up and down stairs, standing up from a sitting position, and the frequency of leg cramps. Here, data related to gross movement can be data obtained by measuring the movement of the body three-dimensionally using a camera or a sensor, such as motion capture, or data indicating the movement of a part of the body detected using a video of the body. The data related to gross movement can be obtained by capturing the movement of the body. Note that the data related to gross movement may be, for example, self-reported information input using a smartphone app, and for example, limb symptom scale data such as the Modified Norris Scale can be used.

[0044] (i) The answers to the questionnaire on disease symptoms are data that can be acquired by, for example, having the user or a third party other than the user input the answers to the user terminal 20. The answers to the questionnaire on disease symptoms may be, for example, self-reported information input using a smartphone app. Examples of such questionnaires (surveys) include the ALSFRS-R Questionnaire, Rasch Overall ALS Disability Scale (ROADS), CPIB Questionnaire, Neurological Fatigue Index-Motor Neuron Disease (NFIMND), ALS Depression inventory (ADI-12) Questionnaire, ALS Quality of Life Survey (QoL), Survey on demographic and phone usage info., ALS CBS (ALS Cognitive Behavioral Screen), and the like.

[0045] (j) "Information automatically collected by the built-in sensor of the device" is, for example, data automatically acquired from a sensor or an app installed in the user terminal 20. More specifically, these are data automatically acquired from a GPS, an accelerometer, call and text logs, and screen event data, etc. Such data is, for example, the above-mentioned (b) "data related to walking" acquired by a GPS and an accelerometer built into a smartphone using a smartphone app, as disclosed in JMIR Mental Health.2016 Apr-Jun;3(2):e16. In order to more effectively detect signs of disease or motor function abnormalities while easily acquiring data with a device carried in daily life or a wearable device, it is preferable to include data acquired by a GPS or an accelerometer as data related to the above-mentioned (j).

[0046] Among the above, the user data (a) to (i) may be self-reported information directly input to the user terminal 20 by the user who uses the user terminal 20. The self-reported information may be information input on behalf of the user by a third party. The self-reported information may be input at a hospital or a testing site, or may be a compilation of responses to a questionnaire. The self-reported information may be automatically acquired from the user terminal 20. The self-reported information may include the height and weight of the user in addition to the above (a) to (i).

[0047] The doctor-input information storage unit 113 stores doctor-input information acquired by the data acquisition unit 101 multiple times from the doctor terminal 30 during a predetermined period, and / or data automatically acquired from the doctor terminal 30. FIG. 6 shows an example of the configuration of doctor-input information stored in the doctor-input information storage unit 113. Examples of doctor-input information include user consultation information, i.e., the user's consultation date and time, or (k) "data from medical institution," which is one of the user data related to the above-mentioned user's neuromuscular disease. The doctor-input information may be linked to a user ID.

[0048] (k) "Data from a medical institution" is medical data that cannot be obtained from the user terminal 20, or data such as medical history. These data from a medical institution may be used alone as user data, or may be used in combination with the above user data (a) to (j) in order to improve the accuracy of the information provided. More specifically, the data from a medical institution is, for example, information obtained from a clinical trial information database (not shown) that stores data (clinical trial data) obtained in clinical trials conducted at a medical institution or the like. In the case of information obtained from the clinical trial information database, the user data may include the date the clinical trial was conducted or the date the data was obtained. In addition, the data from a medical institution may be information obtained from the doctor input information storage unit 113 that stores information input by a doctor who examined the user using the doctor terminal 30. Furthermore, the data from a medical institution may include data indicating the type or prescribed amount of medicine administered to the user, obtained from the doctor input information storage unit 113, and may include data indicating the period of taking the medicine. In addition, it may include an image or video of the patient (user) taken using a camera or the like, and the height and weight of the user.

[0049] Any one or more of the user data (a) to (k) above is data regarding direct or indirect functional abnormalities of the motor nervous system, and may be, for example, data regarding motor function included in the ALS Functional Rating Scale (ALSFRS-R).

[0050] Many ALS patients complain of irregular, asymmetric symptoms, including hand or foot spasms, and muscle weakness and atrophy. Muscle weakness progresses to the forearms, shoulders, and legs. Soon, fasciculations, spasticity, increased deep tendon reflexes, extensor plantar responses, impaired dexterity, rigid movements, weight loss, fatigue, and difficulty controlling facial expressions and tongue movements occur. Other symptoms include hoarseness, difficulty swallowing, and slurred speech; as swallowing becomes more difficult, saliva appears to increase and patients tend to choke on liquids. In the later stages of the disease, a pseudobulbar affect occurs, with inappropriate, involuntary, and uncontrollable excessive laughter and crying. Sensory systems, consciousness, cognition, spontaneous eye movements, sexual function, and urethral and anal sphincters are usually preserved. Thus, the motor function data included in the ALSFRS-R can be used as user data on the user's neuromuscular disease.

[0051] Here, the "ALSFRS-R" (ALS Functional Rating Scale-Recvised) is an assessment scale for grasping the daily living activities of ALS patients, and includes a total of 12 assessment items related to motor dysfunction of the limbs, medullary dysfunction (bulbar dysfunction), respiratory dysfunction, etc. Each item is scored on a 5-point scale from 0 to 4, and is used to evaluate the overall severity and disease progression of ALS patients. The assessment items include 12 items, for example, 1) language (speech), 2) saliva secretion, 3) swallowing, 4) writing, 5) feeding behavior (cutting food and handling utensils, etc.), 6) dressing / personal activities, 7) bed activities (turning over and adjusting bedding, etc.), 8) walking, 9) climbing stairs, 10) dyspnea, 11) orthopnea, and 12) respiratory failure. In addition to the ALSFRS-R, other methods for assessing ALS include the 40-item Amyotrophic Lateral Sclerosis (ALS) Assessment Questionaire (ALSAQ-40), the Japanese ALS Severity Classification, the Modified Norris Scale, etc. By using these items as user data, it is possible to easily grasp the signs of neuromuscular diseases.

[0052] The above user data (a) to (k) may be used in appropriate combination. The user data may be updated based on the self-reported information. The self-reported information may be updated every time the self-reported information is acquired.

[0053] In one embodiment, the information processing device or program is preferably configured to acquire user data including one or more selected from the above (a), (b), (c), (f), (g), (h) and (j) as the user data related to the above (a) to (k). Most of these user data are related to direct dysfunction of the motor nervous system. Therefore, by acquiring such user data, it becomes possible to easily detect early signs of a disease even before the user or a third party recognizes the signs of the disease.

[0054] In one embodiment, the information processing device and the program are preferably configured to acquire at least one of (c) speech data and (g) fine motor data as the user data related to the above (a) to (k). These data have a high verification level and are highly likely to be reliable data. Therefore, by acquiring one or more of these data, it is possible to easily detect symptoms of a disease with high reliability based on the reliable data.

[0055] Furthermore, in one embodiment, the information processing device and the program are preferably configured to acquire user data including one or more data classified into group 1 shown below among the user data related to (a) to (k) above. Moreover, it is more preferable that the information processing device and the program are configured to acquire one or more data classified into group 2 shown below instead of or in addition to one or more data classified into group 1. In particular, by acquiring data of each group in combination, data related to the motor nervous system in multiple parts can be acquired. As a result, it becomes possible to easily detect symptoms of disease at an earlier stage and with higher accuracy. In addition, in this embodiment, when acquiring user data of multiple categories, for example, only data classified into group 1 and / or group 2 may be acquired, or one or more user data other than data classified into group 1 and group 2 may be additionally acquired.

[0056] Group 1: one or more selected from (c) data related to speech and (f) data related to facial expressions, and preferably includes at least (c) data related to speech. Group 2: (b) data regarding gait, (h) data regarding gross motor skills, and (j) one or more selected from information automatically collected by the device's built-in sensors, and preferably includes at least (b) data regarding gait.

[0057] Furthermore, in one embodiment, it is preferable that the information processing device and program are configured to acquire user data relating to the above (a) to (k), which includes, in addition to the data belonging to the above group 1 and / or group 2, one or more data classified into group 3 shown below. Group 3: one or more selected from (a) data regarding typing motion and (g) data regarding fine motor movements, and preferably includes at least (g) data regarding fine motor movements.

[0058] It is considered that the data in group 1 mainly corresponds to motor functions (such as facial movements) controlled by the medulla oblongata, the data in group 2 mainly corresponds to motor functions of the lower limbs, and the data in group 3 mainly corresponds to motor functions of the upper limbs. Therefore, by combining and acquiring these user data, data corresponding to the motor functions of the entire body can be comprehensively acquired. As a result, even if a symptom of a disease appears in a specific part, the symptom can be easily detected earlier and with higher accuracy. Furthermore, by combining these user data, the onset or progression of a disease that does not appear in an evaluation score such as the ALSFRS-R can be detected earlier. As a result, the quality of life of the user can be improved.

[0059] Examples of preferred combinations of user data related to the above (a) to (k) include, but are not limited to, the following (I) to (V). In any case, it becomes possible to easily detect early signs of disease with higher accuracy. (I): A combination including (c) speech data, (b) walking data, and (g) fine motor data. (II): A combination including (c) speech data, (h) gross motor data, and (g) fine motor data. (III): (c) a combination including speech data, (b) walking data, and (a) typing data (IV): (f) facial expression data, (b) walking data, and (g) fine motor data (V): (c) speech data, (j) information collected automatically by the device’s built-in sensors, and (a) a combination of data about typing actions.

[0060] The provided information storage unit 114 stores the provided information generated based on the user data stored in the user information storage unit 111 and the user data stored in the user data storage unit 112. Fig. 7 is a diagram showing an example of the configuration of the provided information stored in the provided information storage unit 114. Examples of the provided information include symptoms of neuromuscular diseases, prediction of onset, prediction of progression, patient stratification, information on medical examinations, and a score value related to the progression of disease symptoms. Examples of the score value related to the progression of disease symptoms include a score value used when the amount of variation in typing motion obtained by the analysis of the analysis unit 102 described later is quantified and a value equal to or greater than a threshold value is determined as a disease score.

[0061] The data acquisition unit 101 acquires one or more pieces of user data related to a neuromuscular disease selected from (a) to (k) multiple times during a predetermined period. By acquiring the user data multiple times during a predetermined period, changes in the user's behavior over time are quantified. The data acquisition unit 101 may acquire the user data directly from the user terminal 20 or the doctor terminal 30, or may acquire the user data via another data server. The information acquired by the data acquisition unit 101 is stored in the user information storage unit 111, the user data storage unit 112, or the doctor input information storage unit 113, respectively. The data acquisition unit 101 may accept user data, for example, by input by the user himself or by input by a person other than the user, such as a family member, friend, caregiver, or representative of the user, or may accept input of data by two or more people.

[0062] The data acquisition unit 101 may passively acquire user data or may actively acquire user data. Here, "passively acquired data" refers to data automatically acquired from GPS, accelerometer, call and text logs, screen event data, etc., as in (j) above, and refers to data generated without the subject's direct involvement, such as GPS traces and call records. Also, "actively acquired data" refers to data acquired from tasks (answers to questionnaires, input actions using fingers, etc.), and refers to data that requires the subject's active involvement for generation. For example, by using passively acquired data such as sensors such as GPS and accelerometers of the user terminal 20, and logs such as telephone usage logs and communication logs, and actively acquired data from tasks such as answers to questionnaires and input actions using fingers in a mutually complementary manner, more accurate information can be provided.

[0063] The data acquisition unit 101 may continuously acquire the user data for a predetermined period. By continuously acquiring the user data, data on the amount of fluctuation in the user data can be acquired. Patients with neuromuscular diseases tend to get tired easily and may have changes in their daily life patterns. Therefore, it is considered that by looking at patterns unique to a user, such as sleep patterns and breathing patterns, it is easier to detect symptoms of neuromuscular diseases. Therefore, the more user data that is continuously acquired, the better.

[0064] The analysis unit 102 analyzes the signs of neuromuscular disease from the amount of variation in the user data acquired by the data acquisition unit 101. That is, the analysis unit 102 analyzes the quality of data obtained from one or more pieces of user data selected from (a) to (k) and captures subtle signs of neuromuscular disease from the analysis results. For example, the analysis unit 102 detects the presence or absence of an abnormal value pattern from the amount of variation in the user data and predicts signs of functional abnormality from these data using AI or the like. The signs of neuromuscular disease are generated by the information generation unit 103 as information to be provided to the user or a doctor.

[0065] The information generating unit 103 generates information to be provided to a predetermined terminal based on the user data acquired by the data acquiring unit 101. That is, based on the user data (a) to (k), that is, using the user data (a) to (k) alone or in a complex combination, generates information to be provided to a user or a doctor. The combination of user data can be appropriately selected according to the quality of the desired information to be provided or the acquired user data. In addition, the information generating unit 103 generates information on the symptoms of a neuromuscular disease as information to be provided based on the analysis result by the analyzing unit 102. The generated information is transmitted to the user terminal 20 and the doctor terminal 30 by the information providing unit 104. The information generated by the information generating unit 103 is stored in the information to be provided storage unit 114.

[0066] As described above, the information provided to the user may include, for example, not only symptoms of neuromuscular disease, but also prediction of onset, prediction of progression, patient stratification, information regarding visits to medical institutions, and scores regarding the progression of disease symptoms, etc. Providing such information enables the user to seek medical treatment early, and enables doctors to make an accurate diagnosis and consider care according to differences in the site of onset.

[0067] The information providing unit 104 provides the provided information generated by the information generating unit 103 to a predetermined terminal used by the user, the user's family, or a doctor. The terminal to which the information providing unit 104 provides information may be the user terminal 20 used by the user, or the doctor terminal 30, or may be a terminal used by a third party such as an insurance company, a pharmaceutical company, a patient group, a research institute, or a financial institution.

[0068] The user terminal notification unit 105 notifies the user of a message urging the user to obtain user data at a preset timing, for example. This timing may be set for each user, and for example, intervals or time periods may be set to notify at a predetermined time every day, or to notify once a week, etc. Also, a warning may be issued based on the provided information notified from the information providing unit 104.

[0069] For example, when the provided information of the user is acquired from the information providing unit 104 at a preset timing, the doctor terminal notifying unit 106 notifies the doctor terminal 106 of a message prompting the user to confirm the provided information. Also, for example, when a reservation for a medical examination is input from the user terminal 20 and the reservation information is acquired via the information processing device 10, the doctor terminal notifying unit 106 may notify the user of the reservation information.

[0070] <Information processing flow> FIG. 8 is a diagram showing the flow of processing executed in the information processing device 10 according to this embodiment.

[0071] First, as a pre-processing of this process, the data acquisition unit 101 of the information processing device 10 accepts input of information about a user and stores the user basic information in the user information storage unit 111. If the user basic information has already been stored in the user information storage unit 111, it is also possible to refer to the user basic information required for the information processing according to this embodiment by accepting input of a user ID, etc.

[0072] Next, the data acquisition unit 101 acquires user data related to one or more neuromuscular diseases selected from the above (a) to (k) (S101). The data may be acquired by performing an operation for acquiring data held by the user terminal 20, the doctor terminal 30, or another server, or may be automatically acquired by an installed app.

[0073] The information generating unit 103 generates information to be provided to a predetermined terminal (S102) based on the user data acquired by the data acquiring unit 101. That is, based on one or more pieces of user data selected from (a) to (k), information on symptoms of neuromuscular disease to be provided to a user, a doctor, or the like is generated.

[0074] The information providing unit 104 provides the provided information generated by the information generating unit 103 to a predetermined terminal (S103). The terminal to which the information providing unit 104 provides the information may be the user terminal 20 used by the user, the doctor terminal 30, or a terminal of a third party.

[0075] In this manner, in the information processing device 10 of the present embodiment, user data related to a neuromuscular disease is acquired multiple times in a predetermined period, changes in behavior over time are quantified, and information to be provided to a predetermined terminal is generated based on the user data, and the result is provided to the predetermined terminal. As a result, it becomes possible to easily and early catch symptoms related to a neuromuscular disease in a user. This makes it possible to detect a neuromuscular disease early, provide an appropriate treatment to the user, and prevent the progression of the disease. In addition, when the user data is acquired via a digital device such as a wearable or a smartphone, the user data can be continuously, non-invasively, and easily used as a digital biomarker, and it becomes possible to easily catch symptoms of a neuromuscular disease.

[0076] Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention, and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and the present invention also includes its equivalents. For example, in addition to the information processing device, this specification also discloses an information processing system, an information processing method using a computer, and an embodiment related to a program for causing a computer to execute the information processing method. With regard to points not specifically described in relation to these contents, each embodiment described in this specification may be adopted alone, or two or more embodiments may be adopted in appropriate combination.

[0077] For example, in this embodiment, for convenience of explanation, one each of the information processing device 10, the user terminal 20, and the doctor terminal 30 is illustrated, but a plurality of user terminals 20 or a plurality of doctor terminals 30 may be connected to the information processing device 10 via a network NW. Also, the information processing device 10 is one computer, but this is not limited thereto, and the system may be configured such that the functional units and the storage units are distributed among a plurality of computers. For example, each storage unit of the information processing device 10 may be provided in a database server, and the information processing device 10 may access the database server. Also, each functional unit may be provided in a distributed manner among a plurality of computers.

[0078] Furthermore, the information processing device 10 may be configured as a user terminal used by a user, and the user terminal may access a separately provided database server.

[0079] Furthermore, it may include configurations other than the functional units and components included in Fig. 3. Also, it may add other steps than the steps included in Fig. 9. For example, in S101, user data may be continuously acquired, the analysis unit 102 may analyze the amount of variation in the data, and in S102, the information generation unit 103 may use the analysis result to generate information to be provided to a predetermined terminal.

[0080] Also, in S103 or after S103, a warning may be issued to the user terminal 20 based on the provided information. Furthermore, an evaluation unit may be provided that evaluates the efficacy of a drug in a clinical trial based on the provided information, and the evaluation result may be notified to the doctor terminal 30. Also, based on the evaluation result or the provided information, the system may be set to encourage the user to visit a doctor. In that case, a reservation for a visit may be accepted and notified to the doctor terminal 30.

[0081] A method for treating a neuromuscular disease may be provided to a user who is suspected of having a neuromuscular disease as a result of the above evaluation. For example, the method may include a step of administering a neuromuscular disease therapeutic drug. Examples of the neuromuscular disease therapeutic drug include ALS therapeutic drugs such as edaravone and riluzole, spinocerebellar degeneration therapeutic drugs such as taltirelin and protirelin, and Parkinson's disease therapeutic drugs such as L-dopa and apomorphine. Furthermore, the above evaluation may be used to evaluate efficacy in clinical trials (new clinical indicators / patient stratification).

[0082] In addition, the effects described in this specification are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that are apparent to a person skilled in the art from the description of this specification in addition to or in place of the above effects.

[0083] The present invention relates to an information processing device that can objectively and easily grasp symptoms of neuromuscular diseases. An information processing apparatus according to an embodiment of the present invention includes: A data acquisition unit that acquires one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period of time; and an information generating unit that generates information to be provided to a predetermined terminal based on the user data. (a) Data relating to one or more of your typing actions selected from the group consisting of speed, accuracy, duration and volume of typing. (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) one or more of the following sleep-related data: sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions The information processing device according to the embodiment of the present invention can easily detect symptoms of neuromuscular diseases.

[0084] [Item 1] An information processing apparatus according to an embodiment of the present invention includes: A data acquisition unit that acquires one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period of time; and an information generating unit that generates information to be provided to a predetermined terminal based on the user data. (a) Data relating to one or more of your typing actions selected from the group consisting of speed, accuracy, duration and volume of typing. (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) one or more of the following sleep-related data: sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions [Item 2] In the information processing device of item 1, the data acquisition unit may continuously acquire the user data during the predetermined period. [Item 3] In the information processing device of item 1 or 2, the data acquisition unit may passively or actively acquire the user data. [Item 4] In the information processing device of items 1 to 3, the provided information may be information regarding at least one of symptoms of a neuromuscular disease, prediction of onset, prediction of progression, patient stratification, information regarding visits to medical institutions, and a score value regarding the progression of disease symptoms. [Item 5] The information processing device of items 1 to 4 may include an analysis unit that analyzes a symptom of the neuromuscular disease from a variation amount of the user data, The information generation unit may generate the symptom as the provided information. [Item 6] In the information processing device of items 1 to 5, the user data may include data on motor function included in the ALS Functional Rating Scale (ALSFRS-R). [Item 7] In the information processing device of item 1-6, the user data may include self-reported information of the user. [Item 8] In the information processing device of item 7, the self-reported information may be acquired from a user terminal used by the user. [Item 9] The information processing device of items 1 to 8 may further include an information providing unit, The information providing unit may notify the provided information to a terminal used by any one of the user, the user's family member, and a doctor. [Item 10] In the information processing device of items 1 to 9, the user data may be data relating to a direct or indirect functional abnormality of the motor nervous system. [Item 11] In the information processing device of items 1 to 10, the neuromuscular disease may include amyotrophic lateral sclerosis (ALS). [Item 12] An information processing device according to another embodiment of the present invention includes: A data acquisition unit that acquires one or more user data related to a direct or indirect functional abnormality of the motor nervous system selected from the following (a) to (k) multiple times during a predetermined period of time; and an information generating unit that generates information to be provided to a predetermined terminal based on the user data. (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions [Item 13] An information processing system according to another embodiment of the present invention includes: A data acquisition unit that acquires one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period of time; an information generating unit that generates information to be provided to a predetermined terminal based on the user data; Equipped with. (a) Data relating to one or more of your typing actions selected from the group consisting of speed, accuracy, duration and volume of typing. (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions [Item 14] According to another embodiment of the present invention, there is provided an information processing method using a computer, comprising: acquiring one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period; and generating information to be provided to a predetermined terminal based on the user data. (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions [Item 15] Yet another embodiment of the present invention is a program for causing a computer to execute an information processing method. The information processing method includes the steps of: acquiring one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period; generating information to be provided to a predetermined terminal based on the user data; to be executed by the computer. (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, duration, and volume; (b) Data on walking selected from one or more of the following: number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, lateral swing of the whole body, and rate of falls while walking. (c) speech data selected from one or more of the following: audio data of the conversation, call transcripts, speech rate, speech duration, sustained vocalizations, word count, speech disorders, frequency of slurred speech, duration of pauses, non-speech sounds, and frequency of coughs; (d) sleep-related data selected from one or more of sleep duration, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper and lower lips, the width between the upper and lower lips, lip movement, the velocity of the gap, the acceleration of the gap, spasms, mouth surfaces, the average symmetry ratio between the left and right mouth surfaces, vertical position of eyebrows, eye opening, translation and rotation vectors of head tilt, and eye movements; (g) Data about a user's fine motor movements selected from one or more of tapping, typing, swiping, and drawing input into a digital device. (h) Data on one or more gross motor activities selected from the group consisting of arm positioning, stair climbing, rising from a sitting position, and frequency of leg cramps. (i) Answers to questionnaires about disease symptoms (j) Information collected automatically using built-in sensors on your device (k) Data from medical institutions [Item 16] In the information processing device of items 1 to 12, The user data may include one or more pieces of data selected from (a), (b), (c), (f), (g), (h), and (j) above. [Item 17] In the information processing device of item 16, The user data may include one or more pieces of data selected from (c) and (g) above. [Item 18] In the information processing device of item 16, the user data may include the following group 1 data. Group 1: One or more of the data in (c) and (f) above [Item 19] In the information processing device of item 18, the user data may include at least the data (c) above. [Item 20] In the information processing device of item 16, 18, or 19, the user data may further include the following group 2 data. Group 2: One or more of the data (b), (h), and (j) above [Item 21] In the information processing device of item 16 and items 18 to 20, the user data may further include the following group 3 data. Group 3: One or more of the data in (a) and (g) above [Item 22] In the information processing device of item 21, the user data may include at least the data (g) above. [Item 23] In the information processing devices of items 18 to 22, the user data may include one or more pieces of data selected from each of the group 1, the group 2, and the group 3. [Item 24] In the information processing device of item 16, the user data may include any combination of the following (I) to (V). (I): A combination including the data (c), (b), and (g) above (II): A combination including the data of (c), (h), and (g) above. (III): A combination including the data of (c), (b), and (a) above. (IV): A combination including the data (f), (b), and (g) above. (V): A combination including the data of (c), (j), and (a) above. [Item 25] In the information processing system of item 13, The user data may include one or more pieces of data selected from (a), (b), (c), (f), (g), (h), and (j) above. [Item 26] In the information processing system of item 25, The user data may include one or more pieces of data selected from (c) and (g) above. [Item 27] In the information processing system of item 25, the user data may include the following data of group 1. Group 1: One or more of the data in (c) and (f) above [Item 28] In the information processing system of item 27, the user data may include at least the data (c) above. [Item 29] In the information processing system of item 25, 27, or 28, the user data may further include the following data of group 2. Group 2: One or more of the data (b), (h), and (j) above [Item 30] In the information processing systems of items 25 and 27 to 29, the user data may further include the following group 3 data. Group 3: One or more of the data in (a) and (g) above [Item 31] In the information processing system of item 30, the user data may include at least the data (g) above. [Item 32] In the information processing systems of items 25 to 31, the user data may include one or more pieces of data selected from each of group 1, group 2, and group 3. [Item 33] In the information processing system of item 25, the user data may include any combination of the following (I) to (V). (I): A combination including the data (c), (b), and (g) above (II): A combination including the data of (c), (h), and (g) above. (III): A combination including the data of (c), (b), and (a) above. (IV): A combination including the data (f), (b), and (g) above. (V): A combination including the data of (c), (j), and (a) above. [Item 34] In the information processing method of item 14, the user data may include one or more data selected from (a), (b), (c), (f), (g), (h), and (j) above. [Item 35] In the information processing method of item 34, the user data may include one or more pieces of data selected from (c) and (g) above. [Item 36] In the information processing method of item 34, the user data may include the following group 1 data. Group 1: One or more of the data in (c) and (f) above [Item 37] In the information processing method of item 36, the user data may include at least the data (c) above. [Item 38] In the information processing method of item 34, 36, or 37, the user data may further include the following group 2 data. Group 2: One or more of the data (b), (h), and (j) above [Item 39] In the information processing methods of items 34 and 36 to 38, the user data may further include the following group 3 data. Group 3: One or more of the data in (a) and (g) above [Item 40] In the information processing methods of items 34 to 39, the user data may include one or more pieces of data selected from each of group 1, group 2, and group 3. [Item 41] In the information processing method of item 34, the user data may include any combination of the following (I) to (V). (I): A combination including the data (c), (b), and (g) above (II): A combination including the data of (c), (h), and (g) above. (III): A combination including the data of (c), (b), and (a) above. (IV): A combination including the data (f), (b), and (g) above. (V): A combination including the data of (c), (j), and (a) above. [Item 42] In the program of item 15, the user data may include one or more data selected from (a), (b), (c), (f), (g), (h), and (j) above. [Item 43] In the program of item 42, the user data may include one or more pieces of data selected from (c) and (g) above. [Item 44] In the program of item 42, the user data may include the following data of group 1. Group 1: One or more of the data in (c) and (f) above [Item 45] In the program of item 44, the user data may include at least the data (c) above. [Item 46] In the program of item 42, 44, or 45, the user data may further include the following group 2 data. Group 2: One or more of the data (b), (h), and (j) above [Item 47] In the programs of item 42 and items 44 to 46, the user data may further include the following group 3 data. Group 3: One or more of the data in (a) and (g) above [Item 48] In the program of item 47, the user data may include at least the data (g) above. [Item 49] In the programs of items 42 to 48, the user data may include one or more pieces of data selected from each of group 1, group 2, and group 3. [Item 50] In the program of item 42, the user data may include any combination of the following (I) to (V). (I): A combination including the data (c), (b), and (g) above (II): A combination including the data of (c), (h), and (g) above. (III): A combination including the data of (c), (b), and (a) above. (IV): A combination including the data (f), (b), and (g) above. (V): A combination including the data of (c), (j), and (a) above.

[0085] Obviously, numerous modifications and variations of the present invention are possible in light of the above teachings. It is therefore to be understood that, within the scope of the appended claims, the invention may be practiced other than as specifically described herein.

Claims

[Claim 1] a data acquisition unit that acquires one or more pieces of user data related to a neuromuscular disease selected from the following (a) to (k) multiple times during a predetermined period; an information generating unit that generates information to be provided to a predetermined terminal based on the user data; An information processing device comprising: (a) data relating to one or more typing actions selected from the group consisting of typing speed, accuracy, time, and amount; (b) one or more walking-related data selected from the number of steps, walking speed, leg swing angle, ankle movement angle, stride length, arm swing, leg swing, whole body lateral swing, and rate of falls while walking (c) speech data selected from one or more of the following: conversational audio data, call records, speech rate, speech duration, sustained speech, word count, speech disturbances, frequency of slurred speech, pauses, non-speech sounds, and cough frequency; (d) one or more sleep-related data selected from sleep time, sleep efficiency, eye movement, and frequency of awakenings; (e) one or more respiratory data selected from vital capacity, forced vital capacity, dyspnea, orthopnea, respiratory failure, and cough frequency; (f) data on one or more facial expressions selected from the gap between the upper lip and the lower lip, the width between the upper lip and the lower lip, lip movement, the velocity of the gap, the acceleration of the gap, convulsions, the surface of the mouth, the average symmetry ratio of the left and right surfaces of the mouth, the vertical position of the eyebrows, the opening of the eyes, the translation and rotation vectors of the head tilt, and the movement of the eyeballs; (g) data regarding one or more fine motor movements selected from the user's taps, typing, swipes, and drawings input into the digital device; (h) data on one or more gross motor skills selected from the frequency of arm repositioning, stair climbing, rising from a sitting position, and leg cramps; (i) Answers to questionnaires regarding disease symptoms (j) Information collected automatically by your device's built-in sensors (k) Data from medical institutions

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