Assessing progression of huntington's disease

Abstract

Currently, assessing the severity and progression of symptoms in patients diagnosed with Huntington's disease involves clinical monitoring and testing of these patients every 6 to 12 months. While more frequent monitoring and testing is preferred, increasing the frequency of clinical monitoring and testing can be costly and inconvenient for these patients. Therefore, as described herein, assessing the severity and progression of symptoms in patients outside the clinical setting via remote monitoring and testing offers advantages in terms of cost, ease of patient monitoring, and convenience for patients. The systems, methods, and apparatus according to this disclosure provide a diagnostic method for assessing one or more symptoms of Huntington's disease in a patient through passive monitoring and / or active testing of the patient.

Description

Technical Field

[0001] The aspects described herein generally relate to medical diagnostic methods and devices for improving patient testing and patient analysis. More specifically, the aspects described herein provide diagnostic devices, systems, and methods for assessing the severity and progression of symptoms of Huntington's disease in patients through active testing and / or passive monitoring. Background Technology

[0002] Huntington's disease is a genetic disorder that causes progressive degeneration of nerve cells in the brain. Diagnosis of Huntington's disease can be based on neurological tests, genetic testing, and imaging, as well as family history and symptoms. The disease causes a variety of symptoms associated with a patient's motor, cognitive, behavioral, and functional abilities. Symptoms associated with motor function can include both involuntary movement problems and voluntary movement disorders, such as involuntary tics or twisting movements (chorea), muscle problems (such as stiffness or muscle contractures (dystonia)), slow or abnormal eye movements, impaired gait, posture, and balance, and difficulty speaking or swallowing. Voluntary movement disorders can affect a person's ability to work, perform daily activities, communicate, and maintain independence. Although there is no known cure, treatments such as medications, therapies, and lifestyle modifications can help patients cope with the symptoms. Furthermore, the onset, severity, and progression of Huntington's disease symptoms can vary between individuals. Therefore, early detection of subtle changes in symptom severity and progression is crucial for guiding treatment and therapy selection.

[0003] Several standardized methods and tests exist for measuring the severity and progression of symptoms in patients diagnosed with Huntington's disease. Each test involves a physician who measures the subject's ability to perform various mental and physical functions in different ways. These standardized tests provide an assessment of a range of symptoms associated with a patient's cognition, behavior, motor function, and abilities, and help depict how these symptoms change over time. Therefore, using standardized methods and tests to assess symptom severity and progression can help guide treatment and therapy selection.

[0004] Currently, assessing the severity and progression of symptoms in patients diagnosed with Huntington's disease involves clinical monitoring and testing every 6 to 12 months. While more frequent monitoring and testing would be ideal, increasing the frequency of clinical monitoring and testing can be costly and inconvenient for patients. Summary of the Invention

[0005] The following is a simplified overview of the various aspects described herein. This overview is not extensive and is not intended to identify key or essential elements or to outline the scope of the claims. The following overview presents only some concepts in a simplified form as an introductory preface to the more detailed descriptions provided below. The aspects described herein describe specialized medical devices for assessing the severity and progression of symptoms in patients diagnosed with Huntington's disease. Testing and monitoring can be performed remotely outside the clinical setting, providing patients with lower costs, higher frequency, and simplified ease and convenience, improving the detection of symptom progression, which in turn leads to better treatment.

[0006] According to one aspect, this disclosure relates to a diagnostic device for assessing one or more symptoms of Huntington's disease in a subject. The device includes at least one processor, one or more sensors associated with the device, and a memory storing computer-readable instructions, which, when executed by the at least one processor, cause the device to receive a plurality of first sensor data via the one or more sensors associated with the device; extract a first plurality of features associated with one or more symptoms of Huntington's disease in the subject from the received first sensor data; and determine a first assessment of one or more symptoms of Huntington's disease based on the extracted first plurality of features.

[0007] One embodiment of the present invention relates to a diagnostic device for assessing one or more symptoms of Huntington's disease in a subject, the device comprising:

[0008] • At least one processor;

[0009] • One or more sensors associated with the device; and

[0010] • A memory storing computer-readable instructions, which, when executed by the at least one processor, cause the device to:

[0011] o Receives multiple first sensor data via one or more sensors associated with the device;

[0012] o Extract a first plurality of features associated with one or more symptoms of Huntington's disease in the subject from the received first sensor data; and

[0013] Based on the extracted first plurality of features, a first assessment of one or more symptoms of Huntington's disease is determined.

[0014] One embodiment of the invention relates to an apparatus as described herein, wherein the computer-readable instructions, when executed by at least one processor, further cause the apparatus to:

[0015] • The subject is prompted to perform one or more diagnostic tasks;

[0016] • In response to the subject performing one or more diagnostic tasks, data from multiple second sensors are received via one or more sensors associated with the device;

[0017] • Extract a second plurality of features associated with one or more symptoms of Huntington's disease from the received second sensor data; and

[0018] • Based on the extracted second plurality of features, a second assessment of one or more symptoms of Huntington's disease is determined.

[0019] One embodiment of the present invention relates to a device as described herein, wherein one or more symptoms of Huntington's disease in a subject include at least one of the following: symptoms indicating cognitive function of the subject, symptoms indicating motor function of the subject, symptoms indicating behavioral function of the subject, or symptoms indicating functional capacity of the subject.

[0020] One embodiment of the present invention relates to a device as described herein, wherein one or more symptoms of Huntington's disease in a subject include at least one of the following: symptoms indicating cognitive function of the subject, symptoms indicating motor function of the subject, symptoms indicating behavioral function of the subject, or symptoms indicating functional capacity of the subject, wherein the patient's activity capacity is assessed at least in part based on GPS location data.

[0021] One embodiment of the invention relates to a device as described herein, wherein one or more symptoms of Huntington's disease in a subject indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor disorder, or bradykinesia of the upper or lower body.

[0022] One embodiment of the present invention relates to a device as described herein, wherein one or more sensors associated with the device include at least one of the following: a first sensor disposed within the device or a second sensor worn by a subject and configured to communicate with the device.

[0023] One embodiment of the present invention relates to an apparatus as described herein, wherein prompting a subject to perform one or more diagnostic tasks includes at least one of the following: prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.

[0024] One embodiment of the present invention relates to an apparatus as described herein, wherein one or more diagnostic tasks are associated with at least one of the following: EQ-5D-5L test, WPAI-HD test, HD-SDI test, rapid tapping test, shape drawing test, chorea test, balance test, U-turn test, SDMT test, and word reading test.

[0025] One embodiment of the present invention relates to a computer-implemented method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising:

[0026] • Receive data from multiple first sensors via one or more sensors associated with the device;

[0027] • Extracting a first plurality of features associated with one or more symptoms of Huntington's disease in the subject from the received first sensor data; and

[0028] • Based on the extracted first plurality of features, a first assessment of the one or more symptoms of Huntington's disease is determined.

[0029] One embodiment of the present invention relates to a computer-implemented method as described herein, the method further comprising:

[0030] • The subject is prompted to perform one or more diagnostic tasks;

[0031] • In response to the subject performing one or more diagnostic tasks, multiple second sensor data are received via the one or more sensors;

[0032] • Extracting a second set of features associated with one or more symptoms of Huntington's disease from the received second sensor data; and

[0033] • Based at least on the extracted second sensor data, determine a second assessment of one or more symptoms of Huntington's disease.

[0034] One embodiment of the present invention relates to a computer-implemented method as described herein, wherein one or more symptoms of Huntington's disease in a subject include at least one of the following: symptoms indicating cognitive function of the subject, symptoms indicating motor function of the subject, symptoms indicating behavioral function of the subject, or symptoms indicating functional ability of the subject, particularly wherein one or more symptoms of Huntington's disease in a subject indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor disorder, and bradykinesia of the upper or lower body.

[0035] One embodiment of the present invention relates to a computer-implemented method as described herein, wherein patient mobility is assessed at least in part based on GPS location data.

[0036] One embodiment of the invention relates to a computer-implemented method as described herein, wherein one or more sensors associated with the device include at least one of the following: a first sensor disposed within the device or a second sensor located at the subject and configured to communicate with the device, particularly wherein prompting the subject to perform one or more diagnostic tasks includes at least one of the following: prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.

[0037] One embodiment of the present invention relates to a computer-implemented method as described herein, wherein one or more diagnostic tasks are associated with at least one of the following: EQ-5D-5L test, WPAI-HD test, HD-SDI test, rapid tapping test, shape drawing test, chorea test, balance test, U-turn test, SDMT test, and text reading test.

[0038] One embodiment of the present invention relates to a non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to perform a method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising:

[0039] • Receives data from multiple sensors via one or more sensors associated with the device;

[0040] • Extract multiple features associated with one or more symptoms of Huntington's disease in the subject from the received sensor data; and

[0041] • Based on the extracted features, an assessment of one or more symptoms of Huntington's disease is determined.

[0042] In some embodiments, computer-readable instructions, when executed by at least one processor, further cause the device to: prompt a subject to perform one or more diagnostic tasks; receive multiple second sensor data via one or more sensors associated with the device in response to the subject performing one or more diagnostic tasks; extract a second plurality of features associated with one or more symptoms of Huntington's disease from the received second sensor data; and determine a second assessment of one or more symptoms of Huntington's disease based on the extracted second plurality of features.

[0043] In some embodiments, one or more symptoms of Huntington's disease in a subject include at least one of the following: symptoms indicating cognitive function, symptoms indicating motor function, symptoms indicating behavioral function, or symptoms indicating functional capacity.

[0044] In some embodiments, one or more symptoms of Huntington's disease in the subject indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor disorder, or bradykinesia of the upper or lower body.

[0045] In some embodiments, one or more sensors associated with the device include at least one of the following: a first sensor disposed within the device or a second sensor worn by a subject and configured to communicate with the device.

[0046] In some embodiments, prompting a subject to perform one or more diagnostic tasks includes at least one of the following: prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.

[0047] In some embodiments, one or more diagnostic tasks are associated with at least one of the following: EQ-5D-5L test (https: / / euroqol.org / eq-5d-instruments / eq-5d-5l-about / ), Work Performance and Activity Disorder (WPAI)-HD test, HD-Force Deployment Inventory (SDI) test, rapid tapping test, drawing shape test, chorea test, balance test, U-turn test, symbolic digit modality test (SDMT), and word reading test.

[0048] According to one aspect, this disclosure relates to a computer-implemented method for assessing one or more symptoms of Huntington's disease (HD) in a subject. The method includes: receiving multiple first sensor data via one or more sensors associated with the device; extracting first plurality of features from the received first sensor data associated with one or more symptoms of Huntington's disease in the subject; and determining a first assessment of one or more symptoms of Huntington's disease based on the extracted first plurality of features.

[0049] In some embodiments, the computer-implemented method further includes: prompting a subject to perform one or more diagnostic tasks; receiving multiple second sensor data via one or more sensors in response to the subject performing one or more diagnostic tasks; extracting a second plurality of features associated with one or more symptoms of Huntington's disease from the received second sensor data; and determining a second assessment of one or more symptoms of Huntington's disease based at least on the extracted second sensor data.

[0050] In some embodiments, one or more symptoms of Huntington's disease in a subject include at least one of the following: symptoms indicating cognitive function, symptoms indicating motor function, symptoms indicating behavioral function, or symptoms indicating functional capacity.

[0051] In some embodiments, one or more symptoms of Huntington's disease in the subject indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor disorder, or bradykinesia of the upper or lower body.

[0052] In some embodiments, one or more sensors associated with the device are at least one of the following: a first sensor disposed within the device or a second sensor located at the subject and configured to communicate with the device.

[0053] In some embodiments, prompting a subject to perform one or more diagnostic tasks includes at least one of the following: prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.

[0054] In some embodiments, one or more diagnostic tasks are associated with at least one of the following: EQ-5D-5L test, WPAI-HD test, HD-SDI test, rapid tapping test, shape drawing test, chorea test, balance test, U-turn test, SDMT test, and word reading test.

[0055] According to one aspect of this disclosure, a non-transitory machine-readable storage medium includes machine-readable instructions for causing a processor to perform a method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising receiving multiple sensor data via one or more sensors associated with a device; extracting multiple features from the received sensor data associated with one or more symptoms of Huntington's disease in the subject; and determining an assessment of one or more symptoms of Huntington's disease based on the extracted multiple features. Attached Figure Description

[0056] A more complete understanding of the aspects and advantages described herein can be obtained by referring to the following description with reference to the accompanying drawings, wherein the same reference numerals denote the same features, and wherein:

[0057] Figure 1 This is an exemplary environment diagram according to an exemplary embodiment, in which a diagnostic apparatus is provided for assessing one or more symptoms of Huntington's disease in a subject.

[0058] Figure 2This is a flowchart of a method for assessing one or more symptoms of Huntington's disease in a subject based on passive monitoring of the subject, according to an exemplary embodiment.

[0059] Figure 3 This is a flowchart of a method for assessing one or more symptoms of Huntington's disease in a subject based on an active test, according to an exemplary embodiment.

[0060] Figures 4A to 4B Exemplary screenshots depicting one or more illustrative aspects described herein illustrate a dropdown menu of an exemplary diagnostic application.

[0061] Figures 5A to 5D Exemplary screenshots depicting one or more illustrative aspects described herein illustrate... Figure 4A The menu options in the first drop-down menu shown are selected.

[0062] Figures 6A to 6D Exemplary screenshots depicting one or more illustrative aspects described herein illustrate... Figure 4B The second drop-down menu shows the selection of various menu options.

[0063] Figures 7A to 7C Exemplary screenshots depicting one or more illustrative aspects described herein illustrate... Figure 4B The selection of the "Settings" menu option in the second drop-down menu shown.

[0064] Figures 8A to 8G Exemplary screenshots depicting one or more illustrative aspects described herein illustrate... Figure 4B The second drop-down menu shown is the "Initialize" menu option.

[0065] Figures 9A to 9F Exemplary screenshots depicting one or more illustrative aspects described herein illustrate... Figure 4B The second drop-down menu shown is the "Begin Full Active Tests" menu option.

[0066] Figure 10A and Figure 10B Exemplary screenshots depicting one or more illustrative aspects described herein illustrate exemplary everyday problems.

[0067] Figures 11A to 11I Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary EQ-5D-5L test.

[0068] Figures 12A to 12J Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary WPAI-HD.

[0069] Figure 13 An exemplary screenshot 1305 depicts one or more illustrative aspects described herein, illustrating an exemplary HD-SDI test.

[0070] Figures 14A to 14J Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary rapid tapping test.

[0071] Figures 15A to 15E Exemplary screenshots depict instructional videos from an exemplary rapid tapping test, illustrating one or more illustrative aspects described herein.

[0072] Figures 16A to 16T Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary shape drawing test.

[0073] Figures 17A to 17D Exemplary screenshots depicting exemplary instructional videos from a shape-drawing test, illustrating one or more illustrative aspects described herein.

[0074] Figures 18A to 18J Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary chorea test.

[0075] Figures 19A to 19E Exemplary screenshots depict exemplary instructional videos from chorea tests, illustrating one or more illustrative aspects described herein.

[0076] Figures 20A to 20H Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary balance test.

[0077] Figures 21A to 21D Exemplary screenshots depict instructional videos from exemplary balance tests, illustrating one or more illustrative aspects described herein.

[0078] Figures 22A to 22H Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary U-turn test.

[0079] Figures 23A to 23F Exemplary screenshots depict instructional videos from an exemplary U-turn test, illustrating one or more illustrative aspects described herein.

[0080] Figures 24A to 24G Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary walking test.

[0081] Figures 25A to 25D Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary SDMT test.

[0082] Figures 26A to 26D Exemplary screenshots depicting one or more illustrative aspects described herein illustrate an exemplary text reading test.

[0083] Figures 27A to 27B Exemplary screenshots depicting instructional videos for exemplary text reading tests based on one or more illustrative aspects described herein.

[0084] Figures 28A to 28Q Exemplary screenshots depicting one or more illustrative aspects described herein illustrate various messages and / or warnings displayed by a diagnostic application.

[0085] Figure 29 This is a table showing the various characteristics of the patient groups participating in the study.

[0086] Figure 30 This is a graph showing the number of patients who completed active testing each week during the first 20 weeks following the clinical screening visit.

[0087] Figure 31 This is a graph showing the number of patients who completed active testing each week during the first 20 weeks following the clinical screening visit.

[0088] Figure 32 This is a graph showing the correlation between the results of SDMT active testing measured using a smartphone app and the results from standard clinical SDMT.

[0089] Figure 33 This is a graph showing the correlation between the results of the Stroop active text reading test measured using a smartphone app and the results from the standard clinical Stroop text reading test.

[0090] Figure 34 This is a graph showing the correlation between the results of rapid tapping tests measured using a smartphone app and the results from standard clinical rapid tapping tests.

[0091] Figure 35 This is a graph showing the correlation between the results of chorea testing measured using a smartphone app and the results from standard clinical chorea assessments.

[0092] Figure 36 An example of a network architecture and data processing apparatus that can be used to implement one or more of the illustrative aspects described herein is shown. Detailed Implementation

[0093] In the following description of various aspects, reference is made to the accompanying drawings, which form a part thereof, and various embodiments in which the aspects described herein can be practiced are illustrated by way of illustration. It should be understood that other aspects and / or embodiments may be utilized, and structural and functional modifications may be made, without departing from the scope of the described aspects and embodiments. The aspects described herein can be used in other embodiments and can be practiced or performed in various ways. Furthermore, it should be understood that the wording and terminology used herein are for illustrative purposes and should not be considered limiting. Rather, the phrases and terms used herein will be given their broadest interpretation and meaning. The use of “comprising” and “including” and variations thereof means to cover the items listed thereafter and their equivalents, as well as other items and their equivalents. The use of the terms “installation,” “connection,” “coupling,” “positioning,” “engagement,” and similar terms is intended to include direct and indirect installation, connection, coupling, positioning, and engagement.

[0094] The systems, methods, and apparatus described herein provide a diagnostic approach for assessing one or more symptoms of Huntington's disease in a patient. In some embodiments, the diagnostic approach may be provided to the patient as a software application installed on a mobile device.

[0095] In some embodiments, the systems, methods, and apparatus described herein provide a diagnostic approach for assessing one or more symptoms of Huntington's disease in a patient based on passive patient monitoring. In some embodiments, the diagnostic approach acquires or receives sensor data from one or more sensors associated with a mobile device while the patient performs daily living activities. In some embodiments, the sensors may be located within the mobile device or as wearable sensors. In some embodiments, sensor features associated with symptoms of Huntington's disease are extracted from the received or acquired sensor data. In some embodiments, the severity and progression of symptoms of Huntington's disease in the patient are assessed based on the extracted sensor features.

[0096] In some embodiments, the systems, methods, and apparatus according to this disclosure provide a diagnostic approach for assessing one or more symptoms of Huntington's disease in a patient based on active patient testing. In some embodiments, the diagnostic approach prompts the patient to perform a diagnostic task. In some embodiments, the diagnostic task is anchored or modeled following established methods and standardized tests; in other cases, new tests or methods may be used. In some embodiments, in response to the patient performing the diagnostic task, the diagnostic approach acquires or receives sensor data via one or more sensors. In some embodiments, the sensors may be located within a mobile device worn by the patient or a wearable sensor. In some embodiments, sensor features associated with symptoms of Huntington's disease are extracted from the received or acquired sensor data. In some embodiments, an assessment of the severity and progression of symptoms of Huntington's disease in the patient is determined based on the extracted sensor data.

[0097] The assessment of the severity and progression of symptoms of Huntington's disease using the diagnostic methods according to this disclosure is sufficiently relevant to clinical outcome-based assessments and can therefore replace clinical patient monitoring and testing. The exemplary diagnostic methods according to this disclosure can be used outside of clinical settings, thus offering advantages in terms of cost, ease of patient monitoring, and patient convenience. This facilitates frequent monitoring and testing of patients, leading to a better understanding of the disease stage and providing insights that are useful to both the clinical and research communities. The exemplary diagnostic methods according to this disclosure can provide earlier detection of subtle changes in the symptoms of Huntington's disease in patients and can therefore be used for better disease management, including personalized therapies.

[0098] Figure 1 This is a diagram of an exemplary environment 100 according to an exemplary embodiment, in which a diagnostic device 105 is provided for assessing one or more symptoms of Huntington's disease in a subject 110. In some embodiments, device 105 may be a smartphone, smartwatch, or other mobile computing device. Device 105 includes a display screen 160. In some embodiments, display screen 160 may be a touchscreen. Device 105 includes at least one processor 115 and a memory 125 storing computer instructions for a symptom monitoring application 130, which, when executed by at least one processor 115, cause device 105 to assess one or more symptoms of Huntington's disease in subject 110 based on passive monitoring of subject 110. Device 105 receives multiple sensor data via one or more sensors associated with device 105. In some embodiments, the one or more sensors associated with the device are at least one of: sensors disposed within the device or sensors worn by the subject and configured to communicate with the device. Figure 1In this device, the sensors associated with the device 105 include a first sensor 120a disposed within the device 105 and a second sensor 120b worn by the subject 110. When the subject 110 performs daily living activities, the device 105 receives multiple first sensor data via the first sensor 120a and multiple second sensor data via the second sensor 120b.

[0099] The device 105 extracts features associated with one or more symptoms of Huntington's disease in the subject 110 from the received first sensor data and second sensor data. In some embodiments, one or more symptoms of Huntington's disease in the subject 110 may include symptoms indicating cognitive function, symptoms indicating motor function, symptoms indicating behavioral function, or symptoms indicating functional capacity. In some embodiments, one or more symptoms of Huntington's disease in the subject 110 indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor dysfunction, and bradykinesia of the upper or lower body.

[0100] In some embodiments, the first sensor 120a or the second sensor 120b (or another sensor) associated with device 105 may include or interact with a satellite-based radio navigation system, such as one that can be used with Global Positioning System (GPS), Galileo, GLONASS, and / or similar systems (collectively referred to herein as GPS), and multiple first sensor data received from the first sensor 120b may include location data associated with device 105. In some embodiments, device 105 extracts location data associated with one or more symptoms of Huntington's disease in subject 110 from the received first sensor data and second sensor data. In some embodiments, assessment of motor function of subject 110 may be based at least in part on the extracted location data (e.g., patient mobility may be assessed in part based on GPS location data). In some embodiments, the sensor 120 associated with device 105 may include sensors associated with Bluetooth and WiFi functionality, and sensor data may include information associated with Bluetooth and WiFi signals received by sensor 120. In some embodiments, device 105 extracts data from the received first sensor data and second sensor data corresponding to the density of Bluetooth and WiFi signals received or transmitted by device 105 or the sensors. In some embodiments, the assessment of the behavioral function or functional ability of the subject 110 may be based on the extracted Bluetooth and WiFi signal data (e.g., the assessment of the patient's social ability may be based in part on the received Bluetooth and WiFi signals).

[0101] Device 105 determines an assessment of one or more symptoms of Huntington's disease in subject 110 based on features extracted from received first and second sensor data. In some embodiments, device 105 transmits the extracted features to server 150 via network 180. Server 150 includes at least one processor 155 and memory 161 storing computer instructions for a symptom assessment application 170, which, when executed by server processor 155, cause processor 155 to determine an assessment of one or more symptoms of Huntington's disease in subject 110 based on the extracted features received by server 150 from device 105. In some embodiments, symptom assessment application 170 may determine an assessment of one or more symptoms of Huntington's disease in subject 110 based on features extracted from sensor data received from device 105 and a patient database 175 stored in memory 160. In some embodiments, patient database 175 may include patient and / or clinical data. In some embodiments, patient database 175 may include baseline and longitudinal clinical and sensor-based measurements of motor and cognitive function from early-stage Huntington's disease patients. In some embodiments, patient database 175 may include clinical and sensor-based measurements of behavior and other symptoms. In some embodiments, patient database 175 may include data from patients at other stages of Huntington's disease. In some embodiments, patient database 175 may be independent of server 150. In some embodiments, server 150 sends an assessment of the determination of one or more symptoms of Huntington's disease in subject 110 to device 105. In some embodiments, device 105 may output the assessment of one or more symptoms of Huntington's disease. In some embodiments, device 105 may communicate information to subject 110 based on the assessment. In some embodiments, the assessment of one or more symptoms of Huntington's disease may be communicated to a clinician who may determine personalized treatment for subject 110 based on the assessment.

[0102] In some embodiments, computer instructions of the symptom monitoring application 130, when executed by at least one processor 115, cause device 105 to assess one or more symptoms of Huntington's disease in subject 110 based on active testing by subject 110. Device 105 prompts subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting subject to perform one or more diagnostic tasks includes prompting subject to answer one or more questions or prompting subject to perform one or more actions. In some embodiments, the diagnostic tasks are anchored or modeled following established methods and standardized tests for assessing and evaluating Huntington's disease.

[0103] In response to subject 110 performing one or more diagnostic tasks, diagnostic device 105 receives multiple sensor data via one or more sensors associated with device 105. As described above, the sensors associated with device 105 may include a first sensor 120a disposed within device 105 and a second sensor 120b worn by subject 110. Device 105 receives multiple first sensor data via first sensor 120a and multiple second sensor data via second sensor 120b. In some embodiments, one or more diagnostic tasks may be associated with at least one of the following: EQ-5D-5L test, WPAI-HD test, HD-SDI test, rapid tapping test, shape drawing test with subject's left hand, shape drawing test with subject's right hand, chorea test, balance test, U-turn test, SDMT test, and / or word reading test.

[0104] Device 105 extracts features associated with one or more symptoms of Huntington's disease in subject 110 from received data from multiple first sensors and multiple second sensors. The one or more symptoms of Huntington's disease in subject 110 may include symptoms indicative of cognitive function, symptoms indicative of motor function, symptoms indicative of behavioral function, or symptoms indicative of functional capacity. In some embodiments, the one or more symptoms of Huntington's disease in subject 110 indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor dysfunction, and bradykinesia of the upper or lower body. As discussed above, location-based data from GPS or similar systems can be used to assess symptoms related to the subject's motor function and / or activity capacity, as well as other location-based assessments. Similarly, as discussed above, WiFi and Bluetooth signal density can be used, for example, to help assess a patient's social abilities.

[0105] Device 105 determines an assessment of one or more symptoms of Huntington's disease in subject 110 based on features extracted from received first and second sensor data. In some embodiments, device 105 transmits the extracted features to server 150 via network 180. Server 150 may include at least one processor 155 and memory 161 storing computer instructions for a symptom assessment application 170, which, when executed by server processor 155, cause processor 155 to determine an assessment of one or more symptoms of Huntington's disease in subject 110 based on the extracted features received by server 150 from device 105. In some embodiments, symptom assessment application 170 may determine an assessment of one or more symptoms of Huntington's disease in subject 110 based on features extracted from sensor data received from device 105 and a patient database 175 stored in memory 160. In some embodiments, patient database 175 may include patient and / or clinical data. In some embodiments, patient database 175 may include baseline and longitudinal clinical and sensor-based measurements of motor and cognitive function from early-stage Huntington's disease patients. In some embodiments, patient database 175 may include clinical and sensor-based measurements of behavior and other symptoms. In some embodiments, patient database 175 may include data from patients at other stages of Huntington's disease. In some embodiments, patient database 175 may be independent of server 150. In some embodiments, server 150 sends an assessment of the determination of one or more symptoms of Huntington's disease in subject 110 to device 105. In some embodiments, device 105 may output the assessment of one or more symptoms of Huntington's disease. In some embodiments, device 105 may communicate information to subject 110 based on the assessment. In some embodiments, the assessment of one or more symptoms of Huntington's disease may be communicated to a clinician who may determine personalized treatment for subject 110 based on the assessment.

[0106] Figure 2 An exemplary method 200 is shown, which is used to... Figure 1 The exemplary device 105 assesses one or more symptoms of Huntington's disease in a subject based on passive monitoring of the subject. Figure 2 refer to Figure 1 When describing this, it should be noted that Figure 2The method steps can be performed by other systems. Method 200 for assessing one or more symptoms of Huntington's disease in a subject includes receiving multiple sensor data via one or more sensors associated with a device (step 205). Method 200 includes extracting multiple features associated with one or more symptoms of Huntington's disease in the subject from the received multiple first sensor data (step 210). Method 200 further includes determining a first assessment of one or more symptoms of Huntington's disease based on the extracted features (step 215).

[0107] Figure 2 Explain the use Figure 1 An exemplary method 200 for assessing one or more symptoms of Huntington's disease using an exemplary device 105. In some embodiments, device 105 may be a smartphone, smartwatch, or other mobile computing device. Device 105 includes at least one processor 115 and memory 125 storing computer instructions for a symptom monitoring application 130, which, when executed by at least one processor 115, cause device 105 to assess one or more symptoms of Huntington's disease in subject 110 based on passive monitoring of subject 110.

[0108] In some embodiments, the symptom monitoring application 130 may provide a diagnostic application that includes a user interface (UI) displayed on a display screen 160 of the device 105. In some embodiments, the display screen 160 may be a touchscreen, and the user interacts with the diagnostic application via the displayed UI. Figures 4 through 28 depict exemplary screenshots illustrating the UI of an exemplary diagnostic application according to the illustrative aspects described herein, and the responsive UI changes to the user interface when the user interacts with the diagnostic application.

[0109] Figures 4A to 4B Exemplary screenshots 405 and 410, depicting one or more illustrative aspects described herein, illustrate a drop-down menu of an exemplary diagnostic application. Figure 4A Screenshot 405 shows a first drop-down menu with menu options “Overall Progress”, “2-Week Progress”, “Backup Overview”, and “Disk Overview”, which the user can select to instruct the diagnostic application to perform the requested menu action, as further described below. Figure 4BScreenshot 410 shows a second dropdown menu 410 with menu options "Help", "About", "Settings", "Set TimeZone", "App ID", "Configure Watch", "Lock Watch", "Unlock Watch", "Initialize", "BeginFull Active Tests", and "Begin Cognitive Tests". Similarly, when the user selects an item from the menu, the diagnostic application performs the requested task or provides the requested information, as further described below.

[0110] Figures 5A to 5D Exemplary screenshots 505, 510, 515, and 520 depict one or more illustrative aspects described herein, illustrating... Figure 4A The first drop-down menu shown allows for the selection of menu options. (See also:) Figure 5A As shown in screenshot 505, selecting the "Overall Progress" menu option displays information about the overall progress of the subjects. Figure 5B As shown in screenshot 510, selecting the "2-week progress" menu option displays information about the subject's bi-weekly progress. Figure 5C As shown in screenshot 515, selecting the "Backup Overview" menu option displays information about file uploads, such as the number of files waiting to be uploaded or the number of files already uploaded. Figure 5D As shown in screenshot 520, selecting the "Disk Overview" menu option displays information about the memory usage of device 105 associated with data from the diagnostic application.

[0111] Figures 6A to 6D Exemplary screenshots 605, 610, 615, and 620 depict one or more illustrative aspects described herein, illustrating... Figure 4B The second drop-down menu shows the selection of various menu options. For example... Figure 6A As shown in screenshot 605, selecting the "Help" menu option displays general information about the diagnostic application. Figure 6B As shown in screenshot 610, selecting the "About" menu option displays information including information about the application (such as application ID, version, contact information, and various copyright information). Figure 6C This is an exemplary screenshot 615, which shows the process of scrolling down through... Figure 6BThe information shown in screenshot 610, along with other information, is displayed. In some embodiments, device 105 may be paired with a second device, such as a smartwatch. Figure 6D As shown in screenshot 620, selecting the "Application ID" menu option displays information about the pairing between device 105 and the second device. In some embodiments, the second device may be one or more wearable sensors, such as... Figure 1 The second sensor 120b is shown. In some embodiments, the second device can be any device including a motion sensor with an inertial measurement unit (IMU). In some embodiments, the second device can be a set of devices or sensors. The second device can be included or incorporated into a wearable device such as a smartwatch, electronic health monitor, etc.

[0112] In some embodiments, the user can select Figure 4B The "Settings" menu option in the second drop-down menu shown is used to specify settings for passive monitoring of the user by device 105. Figures 7A to 7C Exemplary screenshots 705, 710, and 715 depict one or more illustrative aspects described herein, illustrating... Figure 4B The selection of the "Settings" menu option in the second drop-down menu is shown. Figure 7A and Figure 7B As shown in screenshots 705 and 710, selecting the "Settings" menu option displays various settings for "Passive Recording," such as "Pause location recording" and "Location pause duration." Selecting "Pause location recording" allows the user to pause location recording for a period of time. This pauses the passive monitoring of subject 110 by device 105 for the time period specified by "Location pause duration." The time period in hours, minutes, and seconds can be specified via a pop-up window, such as... Figure 7C As shown in screenshot 715. When "Pause Location Recording" is disabled or not enabled, device 105 receives multiple first sensor data via first sensor 120a and multiple second sensor data via second sensor 120b.

[0113] Figures 8A to 8G Exemplary screenshots 805, 810, 815, 820, 825, 830, 835, 840, and 845 depict one or more illustrative aspects described herein, illustrating... Figure 4B The "Initialize" menu option in the second drop-down menu is shown in screenshot 410. Figure 8A As shown in screenshot 805, select Figure 4BThe "Initialize" menu option in the second drop-down menu shown in screenshot 410 displays a request for information to initialize device 105. In some embodiments, the requested information may be initialization code. In some embodiments, the initialization of device 105 is performed when device 105 is initially given to subject 110. Figure 8B As shown in screenshot 810, a keyboard can be displayed to input the requested initialization information. Furthermore, as... Figure 8C As shown in screenshot 815, information about subject 110 can also be provided. Figure 8C As shown in screenshot 815, the requested subject information may include a "Screening ID", a "Subject ID", and a "Site ID". Figure 8D As shown in screenshot 820, a pop-up keyboard can be displayed. Figure 8E An exemplary screenshot 835 is depicted, which requests the subject's information to be re-entered if it is incorrect. (As shown in the image.) Figure 8F and Figure 8G As shown in exemplary screenshots 840 and 845, if subject information is requested, the invalid format of the subject information is marked so that it can be entered again.

[0114] Reference Figure 2 Method 200 begins at step 205, which includes receiving multiple sensor data via one or more sensors associated with device 105. During passive monitoring of subject 110, device 105 receives multiple sensor data via one or more sensors associated with device 105. The sensors associated with device 105 include a first sensor 120a disposed within device 105 and a second sensor 120b worn by subject 110. Device 105 receives multiple first sensor data via first sensor 120a and multiple second sensor data via second sensor 120b.

[0115] Refer back Figures 7A to 7C Various aspects of passive monitoring can be specified via the user interface of the symptom monitoring application 130. For example... Figure 7A and Figure 7B As shown in exemplary screenshots 705 and 710, selecting the "Settings" menu option displays settings associated with "Passive Recording" (such as "Pause Location Recording" and "Location Pause Duration"). Selecting "Pause Location Recording" 705 allows the user to pause location recording for a period of time. This pauses the passive monitoring of subject 110 by device 105 for the time period specified by "Location Pause Duration". The hours, minutes, and seconds of the time period can be specified via a pop-up window, such as... Figure 7CAs shown in screenshot 715. When "Pause Location Recording" is disabled or not enabled, device 105 receives multiple first sensor data via first sensor 120a and multiple second sensor data via second sensor 120b.

[0116] Method 200 proceeds to step 210, which includes extracting a first plurality of features from the received first sensor data that are associated with one or more symptoms of Huntington's disease in the subject. Device 105 extracts features from the received first sensor data and second sensor data that are associated with one or more symptoms of Huntington's disease in the subject 110. One or more symptoms of Huntington's disease in the subject 110 may include symptoms indicative of cognitive function, symptoms indicative of motor function, symptoms indicative of behavioral function, or symptoms indicative of functional capacity. In some embodiments, the features extracted from the plurality of first and second sensor data may indicate symptoms of Huntington's disease, such as visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor dysfunction, and bradykinesia of the upper or lower body.

[0117] Method 200 proceeds to step 215, which includes determining an assessment of one or more symptoms of Huntington's disease based on the extracted features. Device 105 determines an assessment of one or more symptoms of Huntington's disease in subject 110 based on extracted features from received first and second sensor data. In some embodiments, device 105 may transmit the extracted features to server 150 via network 180. Server 150 includes at least one processor 155 and memory 160 storing computer instructions for a symptom assessment application 170, which, when executed by processor 155, determines an assessment of one or more symptoms of Huntington's disease in subject 110 based on the extracted features received by server 150 from device 105. In some embodiments, symptom assessment application 170 may determine an assessment of one or more symptoms of Huntington's disease in subject 110 based on extracted features from sensor data received from device 105 and a patient database 175 stored in memory 160. Patient database 175 may include various clinical and / or patient data. In some embodiments, patient database 175 may include data such as clinical and sensor-based measurements of baseline and longitudinal motor and cognitive function from patients with early-stage Huntington's disease. In some embodiments, patient database 175 may include clinical and sensor-based measurements of behavior and other symptoms. In some embodiments, patient database 175 may include data from patients at other stages of Huntington's disease. In some embodiments, patient database 175 may be independent of server 150. In some embodiments, server 150 sends an assessment of one or more symptoms of Huntington's disease in subject 110 to device 105. In some embodiments, device 105 may output the assessment of one or more symptoms of Huntington's disease on display 160 of device 105. In some embodiments, the assessment of one or more symptoms of Huntington's disease may be communicated to a clinician who may determine personalized treatment for subject 110 based on the assessment.

[0118] Figure 3 An exemplary method 300 is shown, which is used to... Figure 1 The exemplary device 105 assesses one or more symptoms of Huntington's disease in a subject based on the subject's active testing. Figure 3 refer to Figure 1 When describing this, it should be noted that Figure 3The steps of this method can be performed by other systems. Method 300 includes prompting a subject to perform one or more diagnostic tasks (305). Method 300 includes receiving multiple sensor data via one or more sensors in response to the subject performing one or more tasks (step 310). Method 300 includes extracting multiple features from the received sensor data that are associated with one or more symptoms of Huntington's disease in the subject (step 315). Method 300 includes determining an assessment of one or more symptoms of Huntington's disease based at least on the extracted sensor data (step 320).

[0119] Figure 3 Explain the use Figure 1 The exemplary device 105 in the example is an exemplary method 300 for assessing one or more symptoms of Huntington's disease based on active testing by subject 110. In some embodiments, active testing performed by subject 110 using device 105 can be selected via a user interface of symptom monitoring application 130. Figures 9A to 9F Exemplary screenshots 905, 910, 915, 920, 925, and 930 depict one or more illustrative aspects described herein, illustrating... Figure 4B The selection of the "Start Full Active Testing" menu option in the second drop-down menu 410 shown. Figure 9A It is also Figure 4B An exemplary screenshot 905 of the second drop-down menu is shown in screenshot 410. In some embodiments, after selecting the “Start Full Active Testing” menu option, the user interface may display a request for the subject ID, such as... Figure 9B and Figure 9F Exemplary screenshots 910, 915, 920, 925, and 930 are shown. After the requested subject ID information is entered, device 105 begins to perform an active test on subject 110.

[0120] Method 300 begins at step 305, which includes prompting the subject to perform one or more diagnostic tasks. Device 105 prompts subject 110 to perform one or more diagnostic tasks. In some embodiments, prompting the subject to perform one or more diagnostic tasks includes prompting the subject to answer one or more questions or prompting the subject to perform one or more actions. In some embodiments, the diagnostic tasks are anchored or modeled after established methods and standardized tests used to assess and evaluate Huntington's disease.

[0121] In some embodiments, diagnostic tasks may include daily questions to assess a patient’s emotional and physical health during active testing. A patient’s responses to daily questions provide an assessment of the patient’s daily emotional fluctuations and can be used as a control when assessing symptoms associated with the patient’s motor, cognitive, and behavioral functioning. Figure 10A and Figure 10B Exemplary screenshots 1005 and 1010, according to some embodiments, illustrate instances of everyday problems. Figure 10A An exemplary screenshot 1005 is depicted, showing the exemplary everyday question, “How are you feeling physically right now?” Exemplary screenshot 1005 also shows displayed icons labeled “Excellent,” “Good,” “Fine,” “Bad,” and “Horrible.” Subject 110 selects an answer by choosing the icon that best describes their health at that moment. Figure 10A B depicts an exemplary screenshot 1010, which shows another exemplary everyday question, “Overall, how is your mood right now?” Exemplary screenshot 1010 also shows icons labeled “Excellent,” “Good,” “Fair,” “Poor,” and “Very Poor.” Subject 110 selects an answer by choosing the icon that best describes their mood at that moment.

[0122] In some embodiments, the diagnostic task may be performed using standardized tests to measure general health conditions, such as EQ-5D-5L. Figures 11A to 11I Exemplary screenshots 1105, 1110, 1115, 1120, 1125, 1130, 1135, 1140 and 1145 depict one or more illustrative aspects described herein, illustrating an exemplary EQ-5D-5L test. Figure 11A This is an exemplary screenshot 1105 showing general instructions used for the exemplary EQ-5D-5L test. Figure 11B Example screenshot 1110 shows various statements related to activity level, such as "I have no problems walking" and "I have slight problems walking." Subject 110 is instructed to select the statement that best describes subject 110's activity level. Figure 11CExample screenshot 1115 shows statements related to self-care, such as "I have no problems washing or dressing myself" and "I have slight problems washing or dressing myself." Subject 110 is instructed to select the statement that best describes subject 110's self-care. Figure 11D Example screenshot 1120 shows statements related to daily activities (such as work, study, housework, family or leisure activities), such as “I have no problems doing my usual activities”, “I have slight problems doing my usual activities”, etc. Figure 11E It is an exemplary screenshot 1125 relating to the pain and discomfort experienced by subject 110, and includes statements such as “I have no pain or discomfort” or “I have slight pain or discomfort”. Figure 11F Example screenshot 1130 shows various statements related to the anxiety and depression of subject 110. Figures 11G to 11I Exemplary screenshots 1135, 1140, and 1145 are depicted, illustrating some example questions related to the overall health of subject 110.

[0123] In some embodiments, the diagnostic task may be implemented using the Work Performance and Activity Impairment Questionnaire for Huntington's Disease (WPAI-HD). The diagnostic task associated with WPAI-HD measures the impact of Huntington's disease on a subject's ability to work and perform routine activities. Figures 12A to 12JExemplary screenshots 1205, 1210, 1215, 1220, 1225, 1230, 1235, 1240, 1245, and 1250, depicting one or more illustrative aspects described herein, illustrate an exemplary WPAI-HD. In some embodiments, the diagnostic task may prompt a subject to provide information regarding how many hours of work the subject has missed due to problems associated with Huntington's disease, including the number of hours missed during sick leave, the number of times the subject was late for work, and the number of times the subject left work early. In some embodiments, the diagnostic task may prompt a subject 110 to provide information regarding how many hours the subject has missed in the past seven days for reasons other than problems associated with Huntington's disease (e.g., vacations, holidays, and leave to participate in Huntington's disease research). In some embodiments, the diagnostic task may prompt a subject 110 to provide information about the number of hours the subject actually worked in the past seven days. In some embodiments, the diagnostic task may prompt a subject 110 to provide information about how much the symptoms of Huntington's disease affected the subject's efficiency while they were working. In some embodiments, the diagnostic task may prompt the subject to provide information about how much the symptoms of Huntington's disease affect the subject's ability to perform routine daily activities unrelated to the subject's work. Routine daily activities may include activities such as doing housework, shopping, childcare, exercise, and learning.

[0124] In some embodiments, the diagnostic task implements the Huntington's Disease Speaking Difficult Item (HD-SDI). The diagnostic task associated with the HD-SDI test measures the impact of Huntington's disease on a subject's speaking ability. Figure 13 An exemplary screenshot 1305 is depicted, illustrating an exemplary HD-SDI test according to some embodiments. In some embodiments, the subject is prompted to provide information about the frequency with which the subject has recently experienced speaking difficulties.

[0125] In some embodiments, the diagnostic task may be performed using a rapid tapping test. In some embodiments, the diagnostic task prompts the subject 110 to tap the display 160 of the device 105 as quickly and regularly as possible using the index fingers of both the left and right hands. The rapid tapping test measures the speed of finger movement. In some embodiments, the diagnostic task associated with the rapid tapping test may assess symptoms of bradykinesia, chorea, and / or dystonia. In some embodiments, the diagnostic task associated with the rapid tapping test is modeled on a tapping test that has been shown to be sensitive to changes in symptoms of early Huntington's disease (Bechtel et al., 2010; Tabrizi et al., 2012). Similar finger tapping tasks have also been included in assessments using the Unified Huntington's Disease Rating Scale (UHDRS) (Huntington Study Group, 1996).

[0126] Figures 14A to 14J Exemplary screenshots 1405, 1410, 1415, 1420, 1425, 1430, 1435, 1440, 1445, and 1450, depicting one or more illustrative aspects described herein, illustrate an exemplary rapid tapping test. Figure 14A As shown in screenshot 1405, the subject was informed that the test would be performed twice, once for each hand, and prompted to choose one hand to begin. Figure 14B As shown in screenshot 1410, subject 110 selected her right hand. Figure 14C As shown in screenshot 1415, the subject was instructed to tap a button displayed on screen 160 of device 105 as quickly and regularly as possible with their right index finger while placing their wrist and other fingers on a table. Next, as... Figure 14D As shown in screenshot 1420, a countdown to the start of the test is displayed. The test begins, and as... Figure 14E As shown in screenshot 1425, buttons are displayed on the screen 160 of device 105. Subject 110 taps the buttons on the screen 160 of device 105 as quickly and rhythmically as possible with his right index finger. Next, as... Figure 14F As shown in screenshot 1430, subject 110 is prompted to continue the test with their left hand. Figure 14G As shown in screenshot 1435, the subject was instructed to tap a button displayed on screen 160 of device 105 as quickly and regularly as possible with their right index finger while placing their wrist and other fingers on a table. Figure 14H As shown in screenshot 1440, a countdown to the start of the test is displayed. The test begins, and as... Figure 14I As shown in screenshot 1445, a button is displayed on screen 160 of device 105. Subject 110 taps the button on screen 160 as quickly and rhythmically as possible with his right index finger. Then, as... Figure 14J As shown in screenshot 1450, the test is over, and subject 110 is informed that the rapid tapping test is complete.

[0127] In some embodiments, the diagnostic application may include an instructional video of a rapid tapping test that the subject can view on a display screen 160 of the device 105. Figures 15A to 15E Exemplary screenshots 1505, 1510, 1515, 1520, and 1525 depict instructional videos from an exemplary rapid tapping test, illustrating one or more illustrative aspects of the subject matter.

[0128] In some embodiments, the diagnostic task may implement a shape drawing test, prompting the subject 110 to draw a series of increasingly complex shapes on a display screen 160 of device 105. In some embodiments, the shapes may include lines, squares, circles, octagons, and spirals. This test is designed to assess visual-motor coordination and fine motor impairments in patients with early-stage Huntington's disease. The diagnostic task is modeled after a circular drawing task, which has been shown to be sensitive to changes in symptoms in the early stages of Huntington's disease (Say et al., 2011; Tabrizi et al., 2013).

[0129] Figures 16A to 16T Exemplary screenshots 1605, 1610, 1615, 1620, 1625, 1630, 1635, 1640, 1645, 1650, 1655, 1660, 1665, 1670, 1675, 1680, 1685, 1690, 1695, and 1698, depicting one or more illustrative aspects described herein, illustrating an exemplary shape drawing test. Figure 16A As shown in screenshot 1605, subject 110 was informed that the test would be performed twice, once for each hand. Subject 110 was prompted to choose the hand to begin with. Figure 16B As shown in screenshot 1610, subject 110 chose to begin with her right hand. Figure 16C As shown in screenshot 1615, subject 110 was informed that the test measured fine motor skills. Subject 110 was instructed to trace the displayed shape and continue connecting the displayed points along the outline of the shape in the direction of the arrow. Subject 110 was instructed to trace as quickly and accurately as possible using their right index finger. Next, as... Figure 16D As shown in screenshot 1620, a countdown to the start of the test is displayed. Once the test begins, a shape appears on the screen. Figure 16E As shown in screenshot 1625, a line is displayed. Several points are also shown along the line. An arrow indicating direction is also shown, along which subject 110 should trace the shape along the points. Using the right index finger, subject 110 traces the line in the direction of the arrow. Next, as... Figure 16F As shown in screenshot 1630, Figure 16E Screenshot 1625 shows lines, dots, and arrows. However, the arrow is in contrast to... Figure 16E The arrows shown in screenshot 1625 are displayed in the opposite direction. Using the index finger of the right hand, subject 110 traces a line along the direction of the arrows. Next, as... Figure 16G As shown in screenshot 1635, the outline of a square with dots and arrows is displayed. Using the index finger of the right hand, subject 110 traces the square along the direction of the arrows. Next, as... Figure 16HAs shown in screenshot 1640, the outline of a circle with dots and arrows is displayed. Using the index finger of the right hand, subject 110 traces the circle along the direction of the arrows. Next, as... Figure 16I As shown in screenshot 1645, the outline of a shape similar to the number eight with dots and arrows is displayed. Using the index finger of the right hand, subject 110 traced the shape along the direction of the arrows. Next, as... Figure 16J As shown in screenshot 1650, a spiral outline with dots and arrows is displayed. Using the index finger of the right hand, subject 110 traces the shape in the direction of the arrows.

[0130] Repeat the test with your left hand. Figure 16K As shown in screenshot 1655, subject 110 is instructed to continue the test with their left hand. Figure 16L As shown in screenshot 1660, subject 110 was informed that the test measured fine motor skills. Subject 110 was instructed to trace the displayed shape and continue connecting the displayed points along the outline of the shape in the direction of the arrow. Subject 110 was instructed to trace as quickly and accurately as possible using the index finger of their left hand. Next, as... Figure 16M As shown in screenshot 1665, a countdown to the start of the test is displayed. Once the test begins, a shape appears on the screen. Figure 16N As shown in screenshot 1670, a line is displayed. Several points are also shown along the outline of the line. An arrow indicating direction is also shown, along which subject 110 should trace the shape. Using the right index finger, subject 110 traces the line in the direction of the arrow. Next, as... Figure 16O As shown in screenshot 1675, Figure 16N Screenshot 1670 shows lines, dots, and arrows. However, the arrow is in contrast to... Figure 16N The arrows shown in screenshot 1670 are displayed in the opposite direction. Using the index finger of the left hand, subject 110 traces a line along the direction of the arrows. Next, as... Figure 16P As shown in screenshot 1680, the outline of a square with dots and arrows is displayed. Using the index finger of the left hand, subject 110 traces the square along the direction of the arrows. Next, as... Figure 16Q As shown in screenshot 1685, the outline of a circle with dots and arrows is displayed. Using the index finger of the left hand, subject 110 traced the circle along the direction of the arrows. Next, as... Figure 16R As shown in screenshot 1690, the outline of a shape similar to the number eight with dots and arrows is displayed. Using the index finger of the right hand, subject 110 traces the shape along the direction of the arrows. Next, as... Figure 16S As shown in screenshot 1695, a spiral outline with dots and arrows is displayed. Using the index finger of the right hand, subject 110 traces the shape along the direction of the arrows. Next, as... Figure 16T As shown in screenshot 1698, subject 110 was informed that the test was complete.

[0131] In some embodiments, the diagnostic application may include a guided video of a shape drawing test that the subject can view on a display screen 160 of the device 105. Figures 17A to 17D Exemplary screenshots 1705, 1710, 1715, 1720, and 1725 depict exemplary instructional videos of a shape drawing test, illustrating one or more illustrative aspects of the subject matter.

[0132] In some embodiments, the diagnostic task includes a chorea test in which the subject 110 is prompted to extend a corresponding arm while keeping one hand holding device 105 stationary, and simultaneously wearing a wrist-worn wearable device, such as... Figure 1 The second sensor 120b is shown. As a dual task, subject 110 is also prompted to count down aloud. To ensure proper execution, subject 110's voice is recorded. This test is designed to assess chorea and utilizes other sensor-based methods to measure chorea (Reilmann et al., 2010, 2011; Kegelmeyer et al., 2017). Chorea assessment is also part of UHDRS (Huntington Group, 1996).

[0133] Figures 18A to 18J Exemplary screenshots 1805, 1810, 1815, 1820, 1825, 1830, 1835, 1840, 1845, and 1850, depicting one or more illustrative aspects described herein, illustrate an exemplary chorea test. Figure 18A As shown in screenshot 1805, subject 110 was informed that the test would be performed twice, once for each hand. Subject 110 was prompted to choose the hand to begin with. Figure 18B As shown in screenshot 1810, subject 110 chose to begin with her right hand. Figure 16C As shown in screenshot 1815, subject 110 was informed that the test measured arm movement. Subject 110 was instructed to sit upright and hold device 105 in the palm of his right hand, with the display screen 160 of device 105 facing upwards. Subject 110 was instructed to extend his right arm forward. Subject 110 was also instructed to close his eyes and count down loudly from 92 in multiples of seven when the phone's buzzer vibrated. The subject's voice was recorded. Next, as... Figure 18D As shown in screenshot 1820, a countdown to the start of the test is displayed. Once the test begins, as... Figure 18E As shown in screenshot 1825, a countdown to the completion of the test is displayed. The test is repeated using subject 110's left hand. Figures 18F to 18IExemplary screenshots 1825, 1830, 1835, 1840, and 1845 are depicted, illustrating a test with the left hand. Then, as... Figure 18J As shown in screenshot 1850, subject 110 was informed that the test was complete.

[0134] In some embodiments, the diagnostic application may include an instructional video of a chorea test that the subject 110 can view on a display screen 160 of the device 105. Figures 19A to 19E Exemplary screenshots from exemplary instructional videos of the chorea test, depicting one or more illustrative aspects of the subject matter, are provided in 1905, 1910, 1915, 1920, and 1925.

[0135] In some embodiments, the diagnostic task performs a balance test. Subject 110 is instructed to wear a device 105 and a wrist-worn wearable device (such as...) Figure 1 The sensor 120b shown is stationary. A balance test assesses the static balance function of the subject 110. Sensor-based static balance measurements have proven sensitive to symptom differences in early Huntington's disease (Dalton et al., 2013). This test is also part of established scales (e.g., the Berg Balance Scale (Berg et al., 1992), which is used for HD (Busse et al., 2009; Rao et al., 2009)). This test is based on the UHDRS assessment of maximal dystonia, maximal chorea, and longitudinal walking (Huntington Research Group, 1996).

[0136] Figures 20A to 20H Exemplary screenshots 2005, 2010, 2015, 2020, 2025, 2030, 2035, and 2040, depicting one or more illustrative aspects described herein, illustrate an exemplary balance test. Before prompting a subject to perform the balance test, the subject 110 is instructed to answer several questions to assess whether it is safe for the subject 110 to perform the balance test. Figure 20A As shown in the screenshot 2005, subject 110 was asked if they could stand and safely maintain their balance. If subject 110 selected "No," they were informed that the test would be skipped for safety reasons. Figure 20B The screenshot from 2010 shows this. If subject 110 selects the answer "Yes", then as shown... Figure 20C As shown in the screenshot from 2015, participants were asked if they needed a walking aid to stand safely for 30 seconds. Participants selected "yes" or "no." Next, as... Figure 20DAs shown in the 2020 screenshot, the subject was told that the test was a balance test. The subject was instructed to lower their arms, stand upright, and maintain their balance while the buzzer vibrated until the buzzer vibrated again. If standing safely was required, the subject was instructed to use a walking aid. Next, as... Figure 20E As shown in the screenshot 2025, participants were instructed to place their phones in the running belt at waist height. They were also instructed to avoid pressing the phone's "Home" button. Next, as... Figure 20F As shown in screenshot 2030, a countdown to the start of the test is displayed. The phone's buzzer vibrates and the test begins. Figure 20G As shown in screenshot 2035, a countdown to the end of the test is displayed. The subject performs the balance test by lowering their arms, standing upright, and maintaining their balance until the buzzer vibrates again, thus ending the test. Then, as... Figure 20H As shown in screenshot 2040, subject 110 was informed that the test was complete.

[0137] In some embodiments, the diagnostic application may include an instructional video of a balance test that the subject 110 can view on a display screen 160 of the device 105. Figures 21A to 21D Exemplary screenshots 2105, 2110, 2115, and 2120 depict instructional videos from exemplary balance tests that illustrate one or more illustrative aspects of the present document.

[0138] In some embodiments, the diagnostic task may be performed using a U-turn test. The subject 110 is instructed to wear a device 105 and a wrist-worn wearable device (such as...) Figure 1 The second sensor 120b shown simultaneously allows for safe walking and turning between two points at least four steps apart. In some embodiments, the subject 110 is instructed to complete at least five turns. The test is designed to assess gait and lower body bradykinesia, which is also evaluated in the UHDRS. It is modeled after the Timed Up and Go test, which has been clinically validated in individuals with HD (Busse et al., 2009; Rao et al., 2009). The test is based on the UHDRS gait, bradykinesia, and longitudinal walking program (Huntington Group, 1996).

[0139] Figures 22A to 22H Exemplary screenshots 2205, 2210, 2215, 2220, 2225, 2230, 2235, 2240, and 2245, depicting one or more illustrative aspects described herein, illustrate an exemplary U-turn test. Before prompting the subject to perform the U-turn test, the subject 110 is instructed to answer several questions to assess whether it is safe for the subject 110 to perform the actions associated with the U-turn test. Figure 22AAs shown in the screenshot 2005, subject 110 was asked if they could walk and turn safely. If subject 110 selected "No," they were informed that they would skip the test for safety reasons. Figure 22B As shown in screenshot 2210. If subject 110 selects the answer "yes", then as... Figure 20C As shown in screenshot 2215, subject 110 is asked if they need a walking aid to safely walk approximately 30 meters or 100 feet. Subjects are prompted to answer by selecting "yes" or "no." Next, as... Figure 22D As shown in screenshot 2220, the subject was instructed to test their walking and turning abilities. The subject was instructed to walk safely between two points at least four steps apart at a pace normal to them. When a buzzer vibrated, the subject was instructed to begin walking and complete at least five turns within 60 seconds until the buzzer vibrated again. If safe walking was required, the subject should also be instructed to use a walking aid. Next, as... Figure 22E As shown in screenshot 2225, the subject was instructed to carry their phone in a running belt at waist height. The subject was also instructed to avoid pressing the phone's "Home" button. Next, as... Figure 22F As shown in screenshot 2230, a countdown to the start of the test is displayed. The phone's buzzer vibrates and the test begins. Figure 22G Screenshot 2240 shows a countdown to the end of the test. The subject performs a U-turn test until the buzzer vibrates to end the test. Then, as... Figure 22H As shown in screenshot 2240, subject 110 was informed that the test was complete.

[0140] In some embodiments, the diagnostic application may include an instructional video of a U-turn test that the subject 110 can view on a display screen 160 of the device 105. Figures 23A to 23F Exemplary screenshots 2305, 2310, 2315, 2320, 2325 and 2330 are depicted from a guidance video for an exemplary U-turn test, according to some embodiments.

[0141] In some embodiments, the diagnostic task may include a walking test. Subject 110 is instructed to walk 200 meters or 2 minutes as quickly and safely as possible each day. Preferably, the test is performed on a straight path without obstacles (e.g., in a park). Sensor-based gait measurement methods have been shown to be sensitive to differences in early HD symptoms (Dalton et al., 2013). This test is based on UHDRS gait, bradykinesia, and longitudinal walking items (Huntington Research Group, 1996).

[0142] Figures 24A to 24GExemplary screenshots 2405, 2410, 2415, 2420, 2425, 2430, 2435, 2440, and 2445, depicting one or more illustrative aspects described herein, illustrate an exemplary walking test. Before prompting the subject to perform the walking test, the subject 110 is instructed to answer several questions to assess whether it is safe for the subject 110 to perform the actions associated with the walking test. Figure 24A As shown in screenshot 2405, subject 110 is asked if they can safely walk for two minutes, or approximately 200 meters or 650 feet. If subject 110 answers "no," they are informed that the test will be skipped for safety reasons. Figure 24B As shown in screenshot 2410. If subject 110 selects the answer "yes", then as... Figure 24C As shown in screenshot 2415, subject 110 is asked if they need a walking aid to safely walk for two minutes, or approximately 200 meters or 650 feet. Subjects are prompted to answer by selecting "yes" or "no." Next, as... Figure 24D As shown in screenshot 2420, the subject was told that the test helps measure their walking ability. The subject was instructed to find a convenient spot on flat terrain where they could walk for two minutes, or approximately 200 meters (650 feet). The subject was instructed to walk as quickly and safely as possible. The subject was also told that after two minutes, a beeping and buzzing sound from the device would indicate the end of the test. Next, as... Figure 24E As shown in screenshot 2425, a countdown to the start of the test is displayed. The test begins, and as... Figure 24F As shown in screenshot 2230, a countdown to the end of the test is displayed. The subject performs the walking test. Then, as... Figure 24G As shown in screenshot 2435, subject 110 was informed that the test was complete.

[0143] In some embodiments, the diagnostic task includes a Symbolic Digit Modal Test (SDMT) that can be modeled on a pen and paper SDMT (Smith, 1968). The subject 110 is prompted to match symbols with numbers as quickly and accurately as possible based on keys. The keys, symbols, and numbers are displayed on a display screen 160 of device 105. The SDMT test assesses visual-motor integration and measures visual attention and motor speed. SDMT has been shown to be sensitive to changes in symptoms in patients with early-stage Huntington's disease (Tabrizi, 2012) and is part of the Unified Huntington's Disease Rating Scale (UHDRS) assessment (Huntington Research Group, 1996).

[0144] Figures 25A to 25DExemplary screenshots 2505, 2510, 2515, and 2520, depicting one or more illustrative aspects described herein, illustrate an exemplary SDMT test. Figure 25A As shown in screenshot 2505, instructions for the SDMT test are provided to the subject. These instructions direct the test to measure changes in how the subject's brain processes information. The subject 110 is prompted to match symbols with numbers according to the keys as quickly and accurately as possible. The keys, symbols, and numbers are displayed on the display screen 160 of device 105. Next, as... Figure 25B As shown in screenshot 2510, a countdown to the start of the test is displayed. The test begins, and as... Figure 25C As shown in screenshot 2515, keys, symbols, and numbers are displayed on the display screen 160 of device 105. The subject matches the symbols with the numbers as quickly and accurately as possible based on the keys. Then, as... Figure 25D As shown in screenshot 2520, subject 110 was informed that the test was complete.

[0145] In some embodiments, the diagnostic task includes a text reading test. The subject is instructed to read aloud colored text written in black ink on a display screen 160 of device 105. The subject's voice is recorded. This test assesses cognitive processing speed and is modeled on the “text reading” section of the Stroop Text Reading (SWR) test (Ridley, 1935). The “text reading” section of the SWR test is sensitive to changes in symptoms in early Huntington's disease patients (Tabrizi, 2012) and is part of the UHDRS assessment (Huntington Research Group, 1996).

[0146] Figures 26A to 26D Exemplary screenshots 2605, 2610, 2615, and 2620, depicting one or more illustrative aspects described herein, illustrate exemplary text reading tests. Figure 26A As shown in screenshot 2605, instructions for text reading are displayed on the display screen 160 of device 105. These instructions inform the subject that the test measures how well they read text. The subject is instructed to read the text aloud as quickly as possible, line by line, from left to right, on the next screen. If the subject has finished reading all the text, they are instructed to start the test again from the top left until the test is finished. Next, as... Figure 26B As shown in screenshot 2610, a countdown to the start of the test is displayed. The test begins, as... Figure 26C As shown in screenshot 2615, text arranged in lines is displayed on screen 160 of device 105. The subject reads the text according to instructions. Then, as... Figure 26D As shown in screenshot 2620, subject 110 was informed that the test was complete.

[0147] In some embodiments, the diagnostic application may include instructional videos for a text reading test that the subject can view on a display screen 160 of the device 105. Figures 27A to 27B Exemplary screenshots 2705 and 2710 depict instructional videos for exemplary text reading tests, according to some embodiments.

[0148] In some embodiments, diagnostic tasks are automatically scheduled and take approximately 5 minutes per day. In some embodiments, if subject 110 does not complete a diagnostic task on a scheduled date, a scheduled task occurring less frequently than once every two days (e.g., EQ-5D-SL, Level 5 Questionnaire, WPAI-HD, HD-SDI, Walking Test) is rolled over to the next scheduled diagnostic task.

[0149] In some embodiments, the diagnostic application may display various messages or warnings unrelated to active testing. Figures 28A to 28Q Exemplary screenshots depicting one or more illustrative aspects described herein illustrate various messages and / or warnings displayed by a diagnostic application.

[0150] Method 300 proceeds to step 310, which includes receiving multiple second sensor data via one or more sensors in response to the subject performing one or more diagnostic tasks. In response to the subject 110 performing one or more diagnostic tasks, the diagnostic device 105 receives multiple sensor data via one or more sensors associated with the device 105. As described above, the sensors associated with the device 105 include a first sensor 120a disposed within the device 105 and a second sensor 120b worn by the subject 110. The device 105 receives multiple first sensor data via the first sensor 120a and multiple second sensor data via the second sensor 120b.

[0151] Method 300 proceeds to step 315, which includes extracting a second plurality of features from the received sensor data that are associated with one or more symptoms of Huntington's disease in the subject. Device 105 extracts features associated with one or more symptoms of Huntington's disease in the subject 110 from the received first and second sensor data. One or more symptoms of Huntington's disease in the subject 110 may include symptoms indicative of cognitive function, symptoms indicative of motor function, symptoms indicative of behavioral function, or symptoms indicative of functional ability. In some embodiments, the features extracted from the plurality of first and second sensor data may indicate symptoms of Huntington's disease, such as visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor dysfunction, and bradykinesia of the upper or lower body. As discussed above, location-based data from GPS or similar systems can be used to assess symptoms related to the subject's motor function and / or activity level, as well as other location-based assessments. Similarly, WiFi and Bluetooth signal density can be used to help assess a patient's social abilities, etc.

[0152] Method 300 proceeds to step 320, which includes determining an assessment of one or more symptoms of Huntington's disease based at least on the extracted sensor data. Device 105 determines an assessment of one or more symptoms of Huntington's disease in subject 110 based on features extracted from the received first and second sensor data. In some embodiments, device 105 may transmit the extracted features to server 150 via network 180. Server 150 includes at least one processor 155 and memory 160 storing computer instructions for a symptom assessment application 170, which, when executed by processor 155, determines an assessment of one or more symptoms of Huntington's disease in subject 110 based on the extracted features received by server 150 from device 105. In some embodiments, symptom assessment application 170 may determine an assessment of one or more symptoms of Huntington's disease in subject 110 based on features extracted from sensor data received from device 105 and a patient database 175 stored in memory 160. Patient database 175 may include various clinical data. In some embodiments, the second device may be one or more wearable sensors. In some embodiments, the second device may be any device including a motion sensor with an inertial measurement unit (IMU). In some embodiments, the second device may be several devices or sensors. In some embodiments, the patient database 175 may be independent of the server 150. In some embodiments, the server 150 sends an assessment of the determination of one or more symptoms of Huntington's disease in the subject 110 to the device 105. In some embodiments, such as in Figure 1In this embodiment, device 105 may output an assessment of one or more symptoms of Huntington's disease on display 160 of device 105. In some embodiments, the assessment of one or more symptoms of Huntington's disease may be communicated to a clinician who may determine personalized treatment for subject 110 based on the assessment.

[0153] As discussed above, the assessment of symptom severity and progression of Huntington's disease using the diagnostic methods according to this disclosure is sufficiently relevant to clinical outcome-based assessments and can therefore replace clinical patient monitoring and testing. The diagnostic methods according to this disclosure were investigated in a cohort of Huntington's disease patients. A smartphone application was provided to patients, incorporating seven active tests and continuous passive monitoring. Active tests included the SDMT, Stroop word reading test, rapid tapping test, chorea test, balance test, U-turn test, and a two-minute walking test. Data were collected within two weeks following each patient's clinical screening visit. During the clinical screening visit, relevant variables for UHDRS scores, the Symbolic Digit Modal Test (SDMT), the Stroop word test, and demographic information were collected for each patient.

[0154] Figure 29 This is a table showing the various characteristics of the patient groups participating in the study. Figure 30 This is a graph showing the number of patients who completed active testing each week during the first 20 weeks following the clinical screening visit. According to... Figure 30 Patients complete an average of 5 active tests per week. Figure 31 This is a graph showing the number of patients who completed active testing each week during the first 20 weeks following the clinical screening visit. Furthermore, there was no significant effect on active testing adherence by patients across various factors such as age, sex, and disease severity (TMS, TFC, and independent scales) (all p>0.05).

[0155] Sensor features were extracted from sensor data obtained for each active test and aggregated over a two-week period beginning from the baseline clinical visit. Intraclass correlation coefficients and Spearman correlations quantified test-retest reliability and validity compared to equivalent standard clinical tests or the UHDRS program, respectively. Figure 32 This is a graph showing the correlation between the results of SDMT active testing measured using a smartphone app and the results from standard clinical SDMT. Figure 33 This is a graph showing the correlation between the results of the Stroop active text reading test measured using a smartphone app and the results from the standard clinical Stroop text reading test. Figure 34 This is a graph showing the correlation between the results of rapid tapping tests measured using a smartphone app and the results measured from standard clinical rapid tapping tests. Figure 35This is a graph showing the correlation between the results of chorea testing measured using a smartphone app and the results from standard clinical chorea assessments.

[0156] The test-retest reliability of active tests ranged from 0.74 to 0.97, with a median of 0.95. The diagnostic methods of this disclosure showed correlation with clinical tests ranging from r = 0.69 (p < 0.001, Stroop reading) to r = 0.86 (p < 0.001, rapid tapping). Active tests based on specific UHDRS programs showed correlation ranging from r = -0.34 (p = 0.03, U-turn) to r = 0.64 (p < 0.001, chorea non-dominant hand).

[0157] Figure 36 This document illustrates methods that can be used to implement the embodiments described herein (such as...). Figure 1 , Figure 2 and Figure 3 This is an example of a network architecture and data processing apparatus representing one or more illustrative aspects of the described aspects. Various network nodes 3603, 3605, 3607, and 3609 may be interconnected via a wide area network (WAN) 3601 (such as the Internet). Other networks may also be used, including private intranets, corporate networks, LANs, wireless networks, personal networks (PANs), etc. Network 3601 is for illustrative purposes and may be replaced by fewer or other computer networks. The local area network (LAN) may have one or more of any known LAN topologies and may use one or more of a variety of different protocols, such as Ethernet. Devices 3603, 3605, 3607, 3609, and other devices (not shown) may be connected to one or more networks via twisted-pair cables, coaxial cables, optical fibers, radio waves, or other communication media.

[0158] As used herein and depicted in the accompanying figures, the term "network" refers not only to a system in which remote storage devices are coupled together via one or more communication paths, but also to an independent device that may occasionally be coupled to a system with storage capabilities. Therefore, the term "network" includes not only "physical networks" but also "content networks," which consist of data residing in all physical networks (attributed to a single entity).

[0159] Components may include a data server 3603, a network server 3605, and client computers 3607 and 3609. The data server 3603 provides overall access, control, and management of the database and control software used to perform one or more illustrative aspects described herein. The data server 3603 may connect to the network server 3605, through which users interact and obtain data upon request. Alternatively, the data server 3603 may itself act as a network server and be directly connected to the Internet. The data server 3603 may connect to the network server 3605 via a network 3601 (e.g., the Internet), via a direct or indirect connection, or via some other network. Users may interact with the data server 3603 using remote computers 3607 and 3609 (e.g., using a web browser) through one or more externally public websites hosted by the network server 3605 to connect to the data server 3603. Client computers 3607 and 3609 may be used with the data server 3603 to access data stored therein or for other purposes. For example, as is known in the art, a user can access the network server 3605 from a client device 3607 using an internet browser or by executing a software application that communicates with the network server 3605 and / or the data server 3603 via a computer network (such as the internet). In some embodiments, the client computer 3607 may be a smartphone, a smartwatch, or other mobile computing device, and may implement diagnostic devices, such as... Figure 1 The apparatus 105 shown. In some embodiments, the data server 3603 may be implemented as a server, such as Figure 1 Server 150 is shown in the image.

[0160] Servers and applications can be combined on the same physical computer and retain separate virtual or logical addresses, or they can reside on separate physical computers. Figure 1 Only one example of a usable network architecture is shown, and those skilled in the art will understand that the specific network architecture and data processing apparatus used can vary and enhance the functionality they provide, as further described herein. For example, services provided by network server 3605 and data server 3603 can be combined on a single server.

[0161] Each component 3603, 3605, 3607, and 3609 can be any type of known computer, server, or data processing device. The data server 3603 may, for example, include a processor 3611 that controls the overall operation of the data server 3603. The data server 3603 may further include RAM 3613, ROM 3615, a network interface 3617, input / output interfaces 3619 (e.g., keyboard, mouse, monitor, printer, etc.), and memory 3621. I / O 3619 may include various interface units and drivers for reading, writing, displaying, and / or printing data or files. Memory 3621 may further store operating system software 3623 for controlling the overall operation of the data processing device 3603, control logic 3625 for instructing the data server 3603 to perform the aspects described herein, and other application software 3627 that provides auxiliary, support, and / or other functions, which may be used in conjunction with or without the other aspects described herein. The control logic may also be referred to herein as data server software 3625. The functionality of data server software can refer to a combination of operations or decisions made automatically based on rules encoded into control logic, operations made manually by users who provide input to the system, and / or automated processing based on user input (such as queries, data updates, etc.).

[0162] Memory 3621 may also store data for performing one or more aspects described herein, including a first database 3629 and a second database 3631. In some embodiments, the first database may include the second database (e.g., as a separate table, report, etc.). That is, depending on the system design, information may be stored in a single database or distributed across different logical, virtual, or physical databases. Devices 3605, 3607, and 3609 may have architectures similar to or different from those described with respect to device 3603. Those skilled in the art will understand that the functionality of the data processing device 3603 (or devices 3605, 3607, and 3609) as described herein can be distributed across multiple data processing devices, for example, to distribute the processing load among multiple computers to separate processing based on geographic location, user access level, quality of service (QoS), etc.

[0163] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions, executable by one or more computers or other devices described herein, such as in one or more program modules. Typically, program modules include routines, programs, objects, components, data structures, etc., which perform a specific task or implement a specific abstract data type when executed by a processor in a computer or other device. Modules may be written in a source code programming language and then compiled to execute the module, or they may be written in a scripting language (such as, but not limited to, HTML or XML). Computer-executable instructions may be stored on computer-readable media, such as hard disks, optical disks, removable storage media, solid-state storage, RAM, etc. As those skilled in the art will understand, the functionality of a program module may be combined or allocated as needed in various embodiments. Additionally, this functionality may be wholly or partially embodied in firmware or equivalent hardware (such as integrated circuits, field-programmable gate arrays (FPGAs), etc.). Specific data structures may be used to more efficiently implement one or more aspects, and such data structures are included within the scope of computer-executable instructions and computer-usable data described herein.

[0164] Although the subject matter has been described in language specific to structural features and / or methodological effects, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or effects described above. Rather, the specific features and effects described above are disclosed as illustrative forms for implementing the claims.

Claims

1. A diagnostic device for assessing one or more symptoms of Huntington's disease in a subject, the device comprising: At least one processor and display screen; One or more sensors associated with the device; and A memory storing computer-readable instructions, which, when executed by the at least one processor, cause the device to: Receive multiple first sensor data via one or more sensors associated with the device; Extract a first plurality of features from the received first sensor data that are associated with one or more symptoms of Huntington's disease in the subject; as well as Based on the extracted first plurality of features, a first assessment of the one or more symptoms of Huntington's disease is determined. The computer-readable instructions, when executed by the at least one processor, further cause the device to: The subject is prompted to perform one or more diagnostic tasks; In response to the subject performing one or more diagnostic tasks, data from a plurality of second sensors are received via one or more sensors associated with the device; Extract a second plurality of features associated with one or more symptoms of Huntington's disease from the received second sensor data; as well as Based on the extracted second set of features, a second assessment of one or more symptoms of Huntington's disease is determined. The diagnostic task or more described therein is associated with a rapid tapping test, in which the subject is prompted to tap the display screen of the device as quickly and regularly as possible using the index fingers of both the left and right hands, wherein the rapid tapping test measures the speed of finger movement.

2. The apparatus of claim 1, wherein the one or more symptoms of Huntington's disease in the subject include at least one of the following: symptoms indicating the subject's cognitive function, symptoms indicating the subject's motor function, symptoms indicating the subject's behavioral function, or symptoms indicating the subject's functional capacity.

3. The apparatus of claim 1, wherein the one or more symptoms of Huntington's disease in the subject include at least one of the following: symptoms indicating the subject's cognitive function, symptoms indicating the subject's motor function, symptoms indicating the subject's behavioral function, or symptoms indicating the subject's functional capacity, wherein the patient's activity capacity is assessed at least in part based on GPS location data.

4. The device of claim 1, wherein the one or more symptoms of Huntington's disease in the subject indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor disorder, or bradykinesia of the upper or lower body.

5. The device of claim 1, wherein the one or more sensors associated with the device include at least one of: a first sensor disposed within the device or a second sensor worn by the subject and configured to communicate with the device.

6. The apparatus of claim 1, wherein prompting the subject to perform one or more diagnostic tasks comprises at least one of: prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.

7. The apparatus of claim 1, wherein the one or more diagnostic tasks are associated with at least one of the following: EQ-5D-5L test, WPAI-HD test, HD-SDI test, shape drawing test, chorea test, balance test, U-turn test, SDMT test, and word reading test.

8. A computer-implemented method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising: Receive data from multiple first sensors via one or more sensors associated with the device; Extract a first plurality of features from the received first sensor data that are associated with one or more symptoms of Huntington's disease in the subject; as well as Based on the extracted first plurality of features, a first assessment of the one or more symptoms of Huntington's disease is determined. Further includes: The subject is prompted to perform one or more diagnostic tasks; In response to the subject performing one or more diagnostic tasks, data from multiple second sensors are received via the one or more sensors; Extracting second multiple features associated with one or more symptoms of Huntington's disease from the received second sensor data; and Based at least on the extracted second sensor data, a second assessment of one or more symptoms of Huntington's disease is determined. The diagnostic task or more described therein is associated with a rapid tapping test, in which the subject is prompted to tap the display screen of the device as quickly and regularly as possible using the index fingers of both the left and right hands, wherein the rapid tapping test measures the speed of finger movement.

9. The computer-implemented method of claim 8, wherein the one or more symptoms of Huntington's disease in the subject include at least one of the following: symptoms indicating the subject's cognitive function, symptoms indicating the subject's motor function, symptoms indicating the subject's behavioral function, or symptoms indicating the subject's functional capacity.

10. The computer-implemented method of claim 8, wherein the one or more symptoms of Huntington's disease in the subject indicate at least one of the following: visual-motor integration, visual attention, motor speed, cognitive processing speed, chorea, dystonia, visual-motor coordination, fine motor disorder, and bradykinesia of the upper or lower body.

11. The computer-implemented method of claim 8, wherein the patient's mobility is assessed at least in part based on GPS location data.

12. The computer implementation method of claim 8, wherein the one or more sensors associated with the device include at least one of: a first sensor disposed within the device or a second sensor located at the subject and configured to communicate with the device.

13. The computer implementation method of claim 8, wherein prompting the subject to perform one or more diagnostic tasks includes at least one of: prompting the subject to answer one or more questions or prompting the subject to perform one or more actions.

14. The computer implementation method of claim 8, wherein the one or more diagnostic tasks are associated with at least one of the following: EQ-5D-5L test, WPAI-HD test, HD-SDI test, shape drawing test, chorea test, balance test, U-turn test, SDMT test, and text reading test.

15. A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to perform a method for assessing one or more symptoms of Huntington's disease in a subject, the method comprising: Receives data from multiple sensors via one or more sensors associated with the device; Extract multiple features from the received sensor data that are associated with one or more symptoms of Huntington's disease in the subject; as well as Based on the extracted features, an assessment of one or more symptoms of Huntington's disease is determined. The method further includes: The subject is prompted to perform one or more diagnostic tasks; In response to the subject performing one or more diagnostic tasks, data from multiple second sensors are received via the one or more sensors; Extracting second multiple features associated with one or more symptoms of Huntington's disease from the received second sensor data; and Based at least on the extracted second sensor data, a second assessment of one or more symptoms of Huntington's disease is determined. The diagnostic task or more described therein is associated with a rapid tapping test, in which the subject is prompted to tap the display screen of the device as quickly and regularly as possible using the index fingers of both the left and right hands, wherein the rapid tapping test measures the speed of finger movement.

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