Systems and methods for monitoring parkinson's disease using non-imaging sensor bundles

Abstract

Provided herein are systems, devices, and methods for assessing disease progression in an individual having a degenerative movement-related condition. The systems, devices, and methods provided herein may also find relevance in implementing light-based treatment of individuals suffering from a degenerative movement-related condition. In particular aspects, the systems, devices, and methods utilize one or more multi-sensor bundles installed in one or more luminaire assemblies to generate non-imaging sensor data, including data from one or more single-pixel thermopiles, which can be analyzed to evaluate disease progression of the individual. The evaluation of disease progression can be a factor when administering a light-based treatment to the individual. In certain aspects, the light sources of the luminaire assemblies may be modulated to effectuate at least a portion of the light-based treatment.

Description

[0001] SYSTEMS AND METHODS FOR MONITORING PARKINSON’ S DISEASE USING

[0002] NON-IMAGING SENSOR BUNDLES

[0003] FIELD OF THE INVENTION

[0004] The present disclosure relates generally to the continuous assessment of degenerative movement-related disorders, and more specifically to machine learning systems and methods that use non-imaging lighting sensor bundles to monitor said degenerative movement-related disorders.

[0005] BACKGROUND OF THE INVENTION

[0006] Parkinson’s disease (PD) affects about 1-2% of people aged 65 and older and is the fastest growing neurological disorder in the world. It is the prototypic degenerative movement disorder, characterized by a combination of slowness, stiffness, tremor, and postural instability that results in gait dysfunction. Although some therapies exist to lessen symptoms, there are currently no drugs that can prevent or halt Parkinson’s disease. Patient response to therapy is robust early during PD, but as the disease progresses, individuals with PD develop motor fluctuations, characterized by periods of good medication effect (i.e., ON time), and periods of emergent PD symptoms as the medication wears off (i.e., OFF time).

[0007] Objective assessment of clinical progression and the impact of medications on symptoms is essential for both PD drug development and the delivery of medical care. Existing solutions for PD monitoring and assessment suffer from at least two major challenges. First, PD specialists are concentrated in medical centers in urban areas, whereas individuals with PD are spread-out geographically, and have problems traveling to PD centers due to old age, limited mobility, impaired cognition, and decreased driving ability. As a result, many patients with PD (40% of patients with PD in Medicare data) are not seen by a neurologist or PD specialist. Second, even when patients have access to a PD specialist, the resulting assessment is episodic and semi-subjective. In particular, the Movement Disorder Society -Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), widely used for assessing PD motor and non-motor signs, is acquired through infrequent in-clinic assessments, and could be biased by both patient’s state on that day, such as being tired after a long drive to the medical center, and the subjectivity of patient. These semi -subjective episodic assessments are also widely used in clinical trials and as such impact the success of therapeutic development. Other existing solutions include the use of nocturnal breathing signals to detect Parkinson’s disease. However, the breathing signals could be impacted by many different factors, including sleep apnea, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), neuromuscular disorders, obesity, asthma, etc.

[0008] Thus, it is desirable to provide technical solutions to one or more of the challenges associated with assessment of Parkinson’s disease status and progression in individuals.

[0009] SUMMARY OF THE INVENTION

[0010] According to an embodiment of the present disclosure, a system for assessing disease progression in an individual having a degenerative movement-related disease is provided. The system may include at least a first remote processing device configured to assess disease progression information associated collected for the individual. The first remote processing device may include a computer-readable storage medium having stored thereon computer readable instructions to be executed by one or more processors, and one or more processors configured by the computer-readable instructions stored on the computer- readable storage medium to perform the following operations: (i) receive the non-imaging sensor data generated by one or more multi-sensor bundles of one or more luminaire assemblies; (ii) determine movement information for a movement event associated with the individual based on the non-imaging sensor data; (iii) determine whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data; and (iv) store disease progression information for the individual, the disease progression information including the non-imaging sensor data and the movement information for the movement event. The degenerative movement-related disease may be at least one of Parkinson’s disease, dementia, and Alzheimer’s disease.

[0011] In an aspect, the one or more processors of the first remote processing device may be configured by the computer-readable instructions stored on the computer-readable storage medium of the first remote processing device to perform the following additional operations when the one or more exclusion criteria are not met: (v) extract a plurality of movement features for the individual from the non-imaging sensor data associated with the movement event; (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual; and (vii) store the disease progression metric as disease progression information for the individual.

[0012] In an aspect, the multi-sensor bundle of each luminaire assembly may include at least a single-pixel thermopile (“SPT”) sensor and an audio sensor such that the nonimaging sensor data comprises one or more SPT signals generated by the SPT sensors and one or more audio signals generated by the audio sensors.

[0013] According to another embodiment of the present disclosure, another system for assessing disease progression in an individual having a degenerative movement-related disease is provided. The system may include: one or more luminaire assemblies, each luminaire assembly comprising at least one light source configured to produce light and a multi-sensor bundle configured to generate non-imaging sensor data; at least a first electronic device configured to receive the non-imaging sensor data from the one or more luminaire assemblies and transmit the non-imaging sensor data to one or more remote processing devices; at least a first remote processing device configured to receive the non-imaging sensor data from at least the first electronic device; and at least a first user device in communication with at least the first remote processing device. The first remote processing device may include a computer-readable storage medium having stored thereon computer- readable instructions to be executed by one or more processors, and one or more processors configured by the computer-readable instructions stored on the computer-readable storage medium of the remote processing device to perform the following operations: (i) receive, from at least the first electronic device, the non-imaging sensor data generated by the multisensor bundles of the one or more luminaire assemblies; (ii) determine movement information for a movement event associated with the individual based on the non-imaging sensor data; (iii) determine whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data; and (iv) store disease progression information for the individual, the disease progression information including the non-imaging sensor data and the movement information for the movement event. The first user device may include a display device configured to display a graphical user interface comprising disease progression information for the individual, a computer-readable storage medium having stored thereon computer-readable instructions to be executed by one or more processors, and one or more processors configured by the computer-readable instructions stored on the computer-readable storage medium of the first user device to perform the following operations: (i) retrieve disease progression information for the individual from at least the first remote processing device; (ii) generate a graphical user interface comprising disease progression information for the individual; and (iii) display, via the display device, the graphical user interface. The degenerative movement-related disease may be at least one of Parkinson’s disease, dementia, and Alzheimer’s disease.

[0014] In an aspect, the one or more processors of the first remote processing device may be configured by the computer-readable instructions stored on the computer-readable storage medium of the first remote processing device to perform the following additional operations if the one or more exclusion criteria are not met: (v) extract a plurality of movement features for the individual from the non-imaging sensor data associated with the movement event; (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual; and (vii) store the disease progression metric as disease progression information for the individual.

[0015] In an aspect, the one or more processors of the first remote processing device may be configured by the computer-readable instructions stored on the computer-readable storage medium of the first remote processing device to perform the following additional operations if the one or more exclusion criteria are not met: (v) extract a plurality of movement features for the individual from the non-imaging sensor data stored as disease progression information, the non-imaging sensor data including non-imaging sensor data associated with multiple movement events over an assessment period; (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual; and (vii) store the disease progression metric as disease progression information for the individual.

[0016] In an aspect, the assessment period is at least two weeks.

[0017] In an aspect, the disease progression metric for the individual may be indicative of a change in the progression of the degenerative movement-related disease of the individual between one or more movement events over the assessment period.

[0018] In an aspect, the one or more exclusion criteria may include at least one of: (a) whether the individual was responding to a time-sensitive event; (b) whether the individual was talking on a phone or communicating with a third party in proximity of the individual during the movement event; and (c) whether the individual was interacting with an interface of a personal electronic device during the movement event.

[0019] In an aspect, the multi-sensor bundle of each luminaire assembly may include at least a single-pixel thermopile (“SPT”) sensor and an audio sensor, and wherein the non- imaging sensor data comprises an SPT signal generated by the SPT sensor and an audio signal generated by the audio sensor.

[0020] In an aspect, the non-imagining sensor data may include at least an SPT signal and an audio signal from the multi-sensor bundles of a plurality of luminaire assemblies.

[0021] In an aspect, the multi-sensor bundle of one or more luminaire assemblies may further include a WiFi sensor, a daylight sensor, a vibration sensor, a CO2 sensor, a radar sensor, a ToF sensor, and / or a passive infrared sensor.

[0022] According to another embodiment of the present disclosure, a method of assessing disease progression in an individual having a degenerative movement-related disease is provided. The method may include: receiving non-imaging sensor data generated by one or more multi-sensor bundles of one or more luminaire assemblies; determining movement information for a movement event associated with the individual based on the non-imaging sensor data; determining whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data; and when the one or more exclusion criteria are not met, processing the non-imaging sensor data to determine a disease progression metric for the individual. The degenerative movement-related disease may be at least one of Parkinson’s disease, dementia, and Alzheimer’s disease.

[0023] In an aspect, the disease progression metric for the individual may be determined by: extracting a plurality of movement features for the individual from the nonimaging sensor data associated with at least one movement event; and applying a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs the disease progression metric for the individual.

[0024] In an aspect, the method may further include: generating a graphical user interface comprising disease progression information for the individual, the disease progression information including the disease progression metric output by the trained machine learning model; and displaying, via a display device, the graphical user interface.

[0025] These and other aspects of the various embodiments will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0026] BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the various embodiments. FIG. l is a simplified diagram of a system for assessing the disease progression of an individual with a degenerative movement-related condition illustrated in accordance with aspects of the present disclosure.

[0028] FIG. 2 is a partial view of a luminaire assembly having a multi-sensor bundle included therein in accordance with aspects of the present disclosure.

[0029] FIG. 3 is a diagram illustrating a monitored environment where disease progression monitoring may be performed in accordance with aspects of the present disclosure.

[0030] FIG. 4 is another diagram illustrating a monitored environment where disease progression monitoring may be performed in accordance with aspects of the present disclosure.

[0031] FIG. 5A is a plot of a single-pixel thermopile (SPT) sensor signal illustrated in accordance with aspects of the present disclosure.

[0032] FIG. 5B is another plot of a single-pixel thermopile (SPT) sensor signal illustrated in accordance with aspects of the present disclosure.

[0033] FIG. 6A is an illustration demonstrating the use of multiple multi-sensor bundles to track gait characteristics and trajectory in accordance with aspects of the present disclosure.

[0034] FIG. 6B is an illustration demonstrating the use of a single multi-sensor bundle to track gait characteristics and trajectory in accordance with aspects of the present disclosure.

[0035] FIG. 7 is a graph illustrating the step size distribution of one or two individuals walking within a field-of-view (FOV) of a multi-sensor bundle in accordance with aspects of the present disclosure.

[0036] FIG. 8 is a block diagram illustrating a processing device configured to assess disease progression in an individual having a movement-related condition in accordance with aspects of the present disclosure.

[0037] FIG. 9A is a flowchart illustrating a method of assessing disease progression in an individual having a movement-related condition in accordance with aspects of the present disclosure.

[0038] FIG. 9B is a flowchart illustrating a method of assessing disease progression in an individual having a movement-related condition in accordance with further aspects of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] According to various aspects of the present disclosure, systems, devices, and methods for assessing disease progression in an individual having a degenerative movement- related condition are provided. The systems, devices, and methods described herein may find particular application in the continuous, long-term monitoring of individuals with Parkinson’s disease, dementia, Alzheimer’s disease, and / or the like, which have a variety of movement-related symptoms that may worsen and / or improve over time. It is appreciated by the present disclosure that conventional approaches for assessing motor function in such individuals suffer from a number of drawbacks. For example, these motor function assessments are typically acquired through infrequent in-clinic visits, which can be heavily biased by the individual’s acute condition, medication status, and other factors. In contrast, the systems, devices, and methods of the present disclosure provide a convenient, cost- effective, and non-invasive way to not only episodically monitor motor function of such individuals, but to also do so continuously over long periods of time. As such, the systems, devices, and methods described herein enable more accurate assessment of motor function as well as assessment of disease progression over time.

[0040] Turning to FIG. 1, a system 100 for assessing disease progression in an individual 102 having a degenerative movement-related disease is illustrated in accordance with certain aspects of the present disclosure. In embodiments, the degenerative movement- related disease may be a degenerative disease that impacts motor function of the individual 102, including but not limited to Parkinson’s disease, dementia, Alzheimer’s disease, and / or the like.

[0041] As shown in the example of FIG. 1, the system 100 may include at least a first remote processing device 108 configured to assess disease progression information collected for the individual 102. As described in more detail below, the processing device 108 may include: a computer-readable storage medium having stored thereon computer readable instructions to be executed by one or more processors; and one or more processors configured by the computer-readable instructions stored on the computer-readable storage medium to perform the following operations: (i) receive the non-imaging sensor data 106 generated by one or more multi-sensor bundles of one or more luminaire assemblies 104; (ii) determine movement information for a movement event associated with the individual 102 based on the non-imaging sensor data 106; (iii) determine whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data 106; and (iv) store disease progression information for the individual 102, the disease progression information including the non-imaging sensor data 106 and the movement information for the movement event.

[0042] In embodiments, the one or more processors of the processing device 108 may also be configured to: (v) extract a plurality of movement features for the individual 102 from the non-imaging sensor data 106 associated with the movement event; (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual; and (vii) store the disease progression metric as disease progression information for the individual 102.

[0043] In embodiments, the system 100 can include one or more luminaire assemblies 104, each luminaire assembly 104 comprising at least one light source and a multi-sensor bundle. As described herein, each of the luminaire assemblies 104 includes at least one light source configured to produce light, and a multi-sensor bundle configured to generate nonimaging sensor data 106. In particular embodiments, the lights sources can include, but are not limited to, LED bulbs, CFL bulbs, fluorescent bulbs, halogen bulbs, incandescent bulbs, and the like.

[0044] In general, each of the luminaire assemblies 104 may also include a housing or frame and power circuitry for receiving and powering the light source(s) and the multi-sensor bundle. For example, with reference to FIG. 2, a ceiling-mounted luminaire assembly 104 comprising a housing 122, at least two light sources 120, and a multi-sensor bundle 130. However, it should be appreciated that other types of luminaires may be coupled with a multi-sensor bundle 130. For example, in particular embodiments, the luminaire assembly 104 may be a surface wall luminaire, a surface ceiling luminaire, a recessed ceiling luminaire, a pendant luminaire, a recessed downlight, a track light, a decorative luminaire, and the like.

[0045] In embodiments, the multi-sensor bundle 130 may be integral to the luminaire assembly 104 or may be a modular attachment. That is, in some embodiments, the multisensor bundle 130 may be integrated into the luminaire assembly 104, while in other embodiments, the multi-sensor bundle 130 may be plugged into a corresponding interface of the luminaire assembly 104.

[0046] As described herein, the multi-sensor bundle 130 of each luminaire assembly 104 may include one or more types of sensors, including one or more types of non-imaging sensors. For example, in some embodiments, the multi-sensor bundle 130 may include at least a single-pixel thermopile (“SPT”) sensor and an audio sensor. Accordingly, as discussed in more detail below, the non-imaging sensor data 106 generated by the one or more luminaire assemblies 104 can include SPT signals generated by one or more SPT sensors and / or audio signals generated by one or more audio sensors of the multi-sensor bundles 130. As described herein, an SPT signal consists of an object infrared (“IR”) measurement signal, which can exhibit changes in amplitude when one or more persons walk in the field of view of the SPT sensor. The SPT signal can be impacted by factors such as body mass, metabolic rates, and body temperature.

[0047] In further embodiments, one or more of the multi-sensor bundles 130 may include at least one of the following types of sensors: a WiFi sensor; a daylight sensor; a vibration sensor; a CO2 sensor; a radar sensor; a ToF sensor; a passive infrared sensor; and / or the like, including combinations thereof. In some embodiments, the multi-sensor bundles 130 may also include a multiple-pixel thermophile (MPT) sensor. Accordingly, it should be appreciated that the non-imaging sensor data 106 may include data generated by one or more of these additional types of sensors. In specific embodiments, the multi-sensor bundles 130 may not include an optical sensor.

[0048] As mentioned above, each of the luminaire assemblies 104 having a multisensor bundle 130 may generate non-imaging sensor 106, which may be transmitted to a processing device 108 for analysis. Put another way, as shown in the example of FIG. 1, the system 100 may include at least a processing device 108 configured to receive and process the non-imaging sensor data 106 generated by one or more luminaire assemblies 104. In embodiments, the processing device 108 may be a remote processing device 108 that is located outside of the environment (e.g., room, home, facility, etc.) where the luminaire assemblies 104 are installed.

[0049] In particular embodiments, the system 100 may also include at least one local electronic device 112 that is located within proximity to the luminaire assemblies 104 and is in communication with the luminaire assemblies 104 and the remote processing device 108. As such, in embodiments, the at least one local electronic device 112 can be configured to receive the non-imaging sensor data 106 from the one or more luminaire assemblies 104 and to transmit the non-imaging sensor data 106 to the remote processing device(s) 108. That is, the local device 112 may be an intermediary device that collected the sensor data 106 from the luminaire assemblies 104 via one communications network (e.g., via a wired and / or wireless Internet connection, Bluetooth, etc.) and passes the sensor data 106 along to the processing device 108 via another communications network (e.g., via a wired and / or wireless Internet connection, Bluetooth, etc.). In various embodiments, the local electronic device 112 may be at least one of the following: a laptop computer, a desktop computer, a smartphone, or another smart device such as a smart speaker, digital home assistant device, and / or the like.

[0050] With further reference to FIG. 1, the system 100 may also include, in certain embodiments, at least a first user device 114 that is configured to retrieve disease progression information from the processing device 108 and provide the disease progression information to a user 116 such as a physician or other healthcare professional. In embodiments, a user device 114 may include: a display device 118 configured to display a graphical user interface comprising disease progression information for one or more individuals undergoing disease progression monitoring (e.g., individual 102); a computer-readable storage medium having stored thereon computer-readable instructions to be executed by one or more processors; and one or more processors configured by the computer-readable instructions stored on the computer-readable storage medium of the user device 114 to provide the disease progression information to the user 116. In particular embodiments, the one or more processors may be configured to perform the following operations: (i) retrieve disease progression information for the individual 102 from at least the first remote processing device 108; (ii) generate a graphical user interface comprising disease progression information for the individual 102; and (iii) display, via the display device 118, the graphical user interface.

[0051] With reference to FIG. 3 and FIG. 4, the deployment and use of the systems 100 described herein is illustrated in accordance with various aspects of the present disclosure. For example, as shown in FIG. 3, a plurality of luminaire assemblies 104 are installed within a monitored environment 140. In the example of FIG. 3, the monitored environment 140 is a room at an individual’s home, but it should be appreciated that the monitored environment 140 can encompass other locations, such as a hospital room, a clinical room, a hallway, and the like. It should also be appreciated that the plurality of luminaire assemblies 104 do not need to be limited to a single room, but can be distributed throughout one or more floors of a building and throughout multiple buildings. As such, the monitored environment 104 may encompass multiple rooms, one or more floors of a building, a wing or section of a building, an entire building, or multiple buildings.

[0052] As shown in FIG. 3, the plurality of luminaire assemblies 104 may include a light source and a multi-sensor bundle configured to generate non-imaging sensor data 106, which may be communicated to a processing device 108 directly through a communications network or indirectly through a local electronic device 112. The plurality of luminaire assemblies 104 may collect non-imaging sensor data 106 continuously or may detect when an individual 102 enters the monitored environment 140. In embodiments, the non-imaging sensor data 106 may be collected for one or more movement events of the individual 102. For example, as shown in FIG. 4, a plurality of luminaire assemblies (not shown) may track an individual 102 getting up from bed 150, moving to the bathroom 152, and then moving to the living room 154. The trajectory from this movement may be considered a movement event 156. However, as used herein, the term “movement event” more broadly refers to any physical movement of a monitored individual, including movements between rooms, shifting of positions within the same room, or motions made while in one place (e.g., involuntary movements such as twitches or tremors, etc.).

[0053] According to aspects of the present disclosure, one or more of the luminaire assemblies 104 can include a multi-sensor bundle 130 comprising at least a single-pixel thermopile (“SPT”) sensor and an audio sensor. Put another way, the non-imaging sensor data 106 generated by the systems 100 disclosed herein can include at least SPT signals and audio signals from one or more luminaire assemblies 104. For example, as shown in FIG. 5A, SPT signal data for a monitored individual 102 who enters a field-of-view of the multi-sensor bundle and sitting down, working while sitting for a period of time, and then getting-up and walking away. As shown, the SPT signal varies during each of these activities and can be correlated with audio sensor data to make inferences about the context of the movement event(s).

[0054] As shown in FIG. 5 A, the SPT signal exhibits a clear difference between different activities, such as walking in, sitting, and getting up / walking away. Additionally, it is appreciated by the present disclosure that the size of the steps and other statistical features like the rise time and overshoot (shown in FIG. 5B) are strongly correlated with the number of people, their positions, and configurations.

[0055] In particular embodiments, the non-imaging sensor data 106 generated by the multi-sensor bundles may be used to measure more complex movement features, such as gait speed and other gait characteristics, trajectory, step length, step width, step variability, and the like. For example, with reference to FIG. 6A and FIG. 6B, audio and SPT sensors may be used to locate step start and end location, either by using multiple audio sensors with audio signal directivity or using the direction information from the audio sensors and the distance information from the SPT sensors.

[0056] In further embodiments, the non-imaging sensor data 106 (such as the audio and SPT sensor data) may be used to distinguish between the footsteps of different individuals, as shown in FIG. 7. In particular, the inset on the to-left of FIG. 7 shows the distributions of the step sizes for 1 and 2 people as they walk into / out of the FOV of the multi-sensor bundle 130. Positive step sizes correspond to people walking in and negative step sizes corresponding to people walking out of the FOV. The statistical features for two people entering the FOV are general superpositions of the individual effects of the persons. Thus, if both persons are standing, approximately twice the step size for one person standing would be expected.

[0057] With reference to FIG. 8, an exemplary processing device 108 is illustrated in accordance with various aspects of the present disclosure. The processing device 108 can include one or more processors 202 and a computer-readable memory 204 interconnected and / or in communication via a system bus 206 containing conductive circuit pathways through which instructions (e.g., machine-readable signals) may travel to effectuate communication, tasks, storage, and the like. The processing device 108 can be connected to a power source (not shown), which can include an internal power supply and / or an external power supply. In embodiments, the processing device 108 can also include one or more additional components, such as a user interface 208, a display 210, an input / output (VO) interface 212, a networking unit 214, and the like, including combinations thereof. As shown, each of these components may be interconnected and / or in communication via the system bus 206, for example.

[0058] In embodiments, the one or more processors 202 can include one or more high-speed data processors adequate to execute the program components described herein and / or perform one or more operations of the methods described herein. The one or more processors 202 may include a microprocessor, a multi-core processor, a multithreaded processor, an ultra-low voltage processor, an embedded processor, and / or the like, including combinations thereof. The one or more processors 202 can include multiple processor cores on a single die and / or may be a part of a system on a chip (SoC) in which the processor 202 and other components are formed into a single integrated circuit, or a single package. That is, the one or more processors 202 may be a single processor, multiple independent processors, or multiple processor cores on a single die.

[0059] In embodiments, the user interface 208 may be configured to receive various forms of input from a user associated with the processing device 108. The user interface 208 can include, but is not limited to, one or more of a keyboard, keypad, trackpad, trackball(s), capacitive keyboard, controller (e.g., a gaming controller), computer mouse, computer stylus / pen, a voice input device, and / or the like, including combinations thereof.

[0060] In embodiments, the display device 210 may be configured to display information, including text, graphs, and / or the like. In particular embodiments, the display device 210 may be configured to display a graphical user interface (not shown). The display device 210 can include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, a touch screen or other touch-enabled display, a foldable display, a projection display, and so on, or combinations thereof.

[0061] In embodiments, the input / output (I / O) interface 212 may be configured to connect and / or enable communication with one or more peripheral devices (not shown), including but not limited to additional machine-readable memory devices, diagnostic equipment, and other attachable devices. The I / O interface 212 may include one or more I / O ports that provide a physical connection to the one or more peripheral devices. In some embodiments, the I / O interface 212 may include one or more serial ports.

[0062] In embodiments, the networking unit 214 may include one or more types of networking interfaces that facilitate wired and / or wireless communication between the processing device 108 and one or more external devices. That is, the networking unit 214 may operatively connect the processing device 108 to one or more types of communications networks 216, which can include a direction interconnection, the Internet, a local area network (“LAN”), a metropolitan area network (“MAN”), a wide area network (“WAN”), a wired or Ethernet connection, a wireless connection, a cellular network, and similar types of communications networks, including combinations thereof. In some embodiments, the processing device 108 may communicate with one or more remote / cloud-based servers and / or cloud-based services, or with one or more remote devices such as the local electronic device 112, via the communications network 216.

[0063] In embodiments, the memory 204 can be variously embodied in one or more forms of machine accessible and machine-readable memory. In some embodiments, the memory 204 includes a storage device (not shown), which can include, but is not limited to, a non-transitory storage medium, a magnetic disk storage, an optical disk storage, an array of storage devices, a solid-state memory device, and / or the like, as well as combinations thereof. The memory 204 may also include one or more other types of memory, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), Flash memory, and / or the like, as well as combinations thereof. In embodiments, the memory 204 may include one or more types of transitory and / or non- transitory memory.

[0064] The processing device 108 can be configured by software components stored in the memory 204 to perform one or more processes of the methods described herein. More specifically, the memory 204 can be configured to store data / information 220 and computer- readable instructions 222 that, when executed by the one or more processors 202, causes the processing device 108 to perform one or more operations of the processes described herein. Such data 220 and the computer-readable instructions 222 stored in the memory 204 may form a disease monitoring package 224 that may be incorporated into, loaded from, loaded onto, or otherwise operatively available to and from the processing device 108. Thus, in some embodiments, the disease monitoring package 224 and / or one or more individual software packages may be stored in a local storage device of the memory 204. However, in other embodiments, the disease monitoring package 224 and / or one or more individual software packages may be loaded onto and / or updated from a remote server or service via the communications network 216.

[0065] The processing device 108 may also include an operating system component 226, which may be stored in the memory 204. The operating system component 224 may be an executable program facilitating the operation of the processing device 108. Typically, the operating system component 226 can facilitate access of the I / O interface 212, network interface 214, the user interface 208, and the display 210, and can communicate or control other components of the processing device 108.

[0066] Although the processing device 108 has been described and illustrated, it should be appreciated that the local device 112 and the user device(s) 114 may be similarly embodied and adapted for their specific functions.

[0067] Accordingly, provided herein is a computer program product 224 comprising a non-transitory computer-readable storage medium 204 having stored thereon computer- readable instructions 222 that, when executed by one or more processors (such as processors 202), cause the one or more processors to perform one or more operations of the methods described below. For example, in specific embodiments, the computer-readable storage medium 204 may include computer-readable instructions 222 that, when executed by one or more processors (such as processors 202), cause the one or more processors to perform the following operations: (i) receive non-imaging sensor data 106 generated by one or more multi-sensor bundles 130 of one or more luminaire assemblies 104; (ii) determine movement information for a movement event associated with the individual 102 based on the nonimaging sensor data 106; (iii) determine whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data 106; (iv) when the one or more exclusion criteria are not met, process the non-imaging sensor data 106 to determine a disease progression metric for the individual 102. In particular embodiments, processing the non-imaging sensor data 106 may include the following operations: (v) extract a plurality of movement features for the individual 102 from the non-imaging sensor data 106 associated with at least one movement event; and (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs the disease progression metric for the individual 102.

[0068] More particularly, one or more computer-readable storage mediums 204 of the present disclosure can include computer-readable instructions 222 that, when executed by one or more processors (such as processors 202), cause the one or more processors to perform a method for assessing disease progression in an individual having a degenerative movement-related disease as described herein.

[0069] For example, with reference to FIG. 9, a method 300 for assessing disease progression in an individual having a degenerative movement-related disease is illustrated in accordance with certain aspects of the present disclosure. As shown, the method 300 can include: in a step 310, receiving non-imaging sensor data 106; in a step 320, determining movement information for a movement event associated with the individual 102 based on the non-imaging sensor data 106; in a step 330, determining whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data 106; in a step 340, when the one or more exclusion criteria are not met, extracting a plurality of movement features for the individual 102 from the non-imaging sensor data 106 associated with at least one movement event; and in a step 350, applying a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual 102.

[0070] More specifically, in the step 310, the method 300 may include receiving nonimaging sensor data 106 generated by one or more multi-sensor bundles 130 of one or more luminaire assemblies 104. In embodiments, the sensor data 106 may be received from the luminaire assemblies 104 directly or indirectly via a local electronic device 112. In particular embodiments, the non-imaging sensor data 106 includes at least SPT sensor data and audio sensor data generated by one or more multi-sensor bundles 130. As described above, the luminaire assemblies 104 and the multi-sensor bundles 130 may be installed and throughout a monitored environment 140.

[0071] In the step 320, the method 300 may include determining movement information for a movement event associated with the individual 102 based on the nonimaging sensor data 106. In particular embodiments, the non-imaging sensor data 106 may be distinguish between one or more types of movement events. For example, in some embodiments, the non-imaging sensor data 106 can be used to distinguish between the following cases: (1) one person walking; (2) one person walking and one person sitting; (3) more than one person walking; and (4) others.

[0072] In the step 330, the method 300 may further include determining whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data 106. As described herein, the “exclusion criteria” refer to conditions where the nonimaging sensor data 106 is unlikely to correlate with disease progression. In various embodiments, the exclusion criteria can be indicative of situations where the movement, gait speed, trajectory, and the like of the individual 102 are affected in a manner that is not indicative of the individual’s typical movements. For example, in particular embodiments, the exclusion criteria may include one or more of the following: (a) whether the individual was responding to a time-sensitive event; (b) whether the individual was talking on a phone or communicating with a third party in proximity of the individual during the movement event; and (c) whether the individual was interacting with an interface of a personal electronic device during the movement event.

[0073] In embodiments, the non-imaging sensor data 106 may be analyzed to determine whether one or more exclusion criteria are met. For example, the audio sensor may be used to determine whether the individual 102 is alone within the monitored environment 140 but a second voice is heard, which would indicate that the individual 102 is talking on the phone. The audio sensor may also be used to detect different types of alarms or ringing noises. Additionally, the audio sensor may be used to determine whether the individual 102 is carrying heavy objects (e.g., through a combination of louder breathing sounds and louder step sounds, etc.).

[0074] By carefully considering and excluding movement events based on these features, the accuracy and reliability of the movement features as an indicator for disease progression can be enhanced. Accordingly, as shown in FIG. 9A, if one or more of the exclusion criteria are met, then the system (e.g., system 100) may return to monitoring the monitored environment 140. In such embodiments, the non-imaging sensor data 106 associated with the detected movement event may be discarded. If no exclusion criteria are detected, the method 300 can include further processing the non-imaging sensor data 106 in one or more steps (e.g., steps 340, 350, etc.).

[0075] In particular embodiments, the method 300 can include, in the step 340, extracting a plurality of movement features from the non-imaging sensor data 106. In embodiments, the movement features may include, but are not limited to, start-to-finish movement event duration, gait speed and other characteristics, trajectory, step length, step width, step variability, and / or the like.

[0076] For example, in particular embodiments, the audio sensor data may be used to detect a plurality of step sounds and then give SPT sensor data timestamps for each step. Then, the amplitude of the SPT signal may be used to estimate the distance of the step location from the SPT sensor. The distance from the SPT sensor and the step sound direction may be combined to calculate the start and end location of each step. Based on this, more complex gait characteristics may be determined, including step length, step width, step variability, and the like. In some embodiments, one or more of the multi-sensor bundles 130 may include a multiple-pixel thermophile (MPT) sensor, which can be utilized to monitor the height change of the head of a subject in order to estimate surface changes within a monitored environment. The MPT sensor data may then be combined with the sensor data of other sensors to accurately estimate gait speed and trajectory over varying terrains.

[0077] In further embodiments, sensor data from several multi-sensor bundles 130 may be combined to estimate these gait characteristics. For example, with multiple sensor bundles 130, a triangularization method can be used to calculate the start and end locations of each step, which can then be used to estimate gait trajectory and speed with audio timestamps. Alternatively, if the monitored environment 140 only has one multi-sensor bundle 130, the SPT signal with timestamps may be used to estimate the distance to the sensor with SPT amplitude information for start and end locations, which can then be fused with audio sensor data indicating the direction to locate the footsteps and calculate the gait characteristics.

[0078] In still further embodiments, the movement features may encompass micro movements (e.g., shaky hands, tremors, etc.) and / or macro movements (e.g., an individual wandering aimlessly through the monitored environment 140).

[0079] Then, in the step 350, the method 300 can include applying a machine learning model to the plurality of movement features associated with the individual 102 in order to generate a disease progression metric for the individual 102. In embodiments, the machine learning model may be trained to correlate specific gait characteristics (e.g., the movement features) with disease progression and / or severity. In some embodiments, a time-series deep learning model may be used to learn how movement features change over time and predict a disease severity progression for the future.

[0080] The machine learning model may be trained in a variety of ways. For example, in some embodiments, a self-learning method such as auto-regressive predictive coding (APC) may be applied first and then a small batch of labelled data with disease progression levels can be used to finetune the machine learning model for the prediction.

[0081] In particular embodiments, the machine learning model may be trained with labelled data to characterize the statistical features for one or more types of movements, such as the four types of movement events described above. The posterior probability for each class may be computed and the class with the maximum posterior probability may be selected according to the following approach:

[0082] J = arg max Prob Cj \step size, overshoot, rise time,j = 1, 2,3,4) where C,- i = 1, 2, 3, 4 are the four classes described above. When a class 1 and 2 movement event is detected with the subject walking in the room, the gait monitoring process may begin.

[0083] As described herein, the machine learning model may be trained to quantify and monitor the changes in one or more disease progression metrics for an individual based on the extracted movement features. For example, a decrease in gait speed may indicate the progression of a movement-related disease such as Parkinson’s disease, and therefore the disease progression metric may be representative of changes in walking speed over time. Movement-related diseases such as Parkinson’s disease can also affect a person’s gait trajectory by causing irregularities or deviations from a typical walking pattern. Thus, the disease progression metric may be representative of changes that indicate balance and / or coordination issues. In some embodiments, by combining the predicted gait speed and trajectory features, a unique disease progression metric may be developed to correlate specific gait characteristics with disease severity.

[0084] In further embodiments, the machine learning model may be trained to account for different medication states of the individual 102. For example, it is appreciated by the present disclosure that certain medications and treatments can influence gait speed and other motor functions in persons with a movement-related condition. Thus, in particular embodiments, the machine learning model may also receive information about the individual’s medication regiment, determine a medication state of the individual, and assess the individual’s disease status / progression relative to the individual’s medication state.

[0085] In embodiments, the machine learning model may also be trained to account for different contexts of the identified movement event, including, for example, when the sensor data was acquired, the emotional state of the individual, whether the individual is coming from doing strenuous exercise, and / or the like. Thus, in embodiments, the machine learning model may know to compare movement features from similar contexts (e.g., user walking to toilet, user after reading a book going to the kitchen for a drink, etc.) when determining the disease progression metric.

[0086] With reference to FIG. 9B, the method 300 can further include: in a step 360, storing the disease progression metric and other disease progression information; in a step 370, generating a graphical user interface comprising the disease progression information; and in a step 380, displaying the graphical user interface via a display.

[0087] More specifically, in the step 360, the method 300 can include storing the disease progression metric and other disease progression information in a non-transitory computer-readable storage medium (e.g., memory 204) of the processing device 108, or another similar storage medium. In embodiments, the disease progression information that is stored includes at least the disease progression metric generated for the individual 102. In embodiments, the disease progression information that is stored includes the non-imaging sensor data 106, the movement event type classification, and / or the movement features extracted from the non-imaging sensor data 106. In embodiments, the disease progression information for a plurality of individuals (e.g., individual 102) undergoing disease progression monitoring may be securely stored in a database or similar electronic data structure.

[0088] In the step 370, the method 300 can then include generating a graphical user interface comprising disease progression information associated with one or more individuals. In embodiments, the graphical user interface can include the disease progression metric generated for an individual 102, the movement features extracted from one or more movement events associated with the individual 102, and / or the like. The disease progression information may be graphically represented in a variety of ways, such as through charts, graphs, plots, timelines, and / or the like.

[0089] In the step 380, the graphical user interface may then be displayed for a user 116, such as a physician or other healthcare professional. In some embodiments, a user device 114 may be used to retrieve the stored disease progression information for these one or more individuals and generate the graphical user interface that is then displayed on a display 118 of the user device 114. In other embodiments, a remote processing device 108 may retrieve the stored disease progression information and generate a web-based graphical user interface that is then accessed through and displayed on the user device 114.

[0090] In particular embodiments, the graphical user interface may be interactive. For example, the graphical user interface may include multiple disease progression metrics stratified according to different types of monitored activities (i.e., different types of movement events, contexts, etc.), which may be viewed and selected by the user. In embodiments, the user may select, via the graphical user interface, the different types of monitored activities (i.e., types of movement events, contexts, etc.) to use in calculating the disease progression metric.

[0091] In accordance with further aspects of the present disclosure, one or more disease progression metrics generated for an individual 102 may be utilized in developing and / or administering a light-based treatment for the individual 102. That is, the information collected by the system 100 can be used to generate one or more disease progression metrics that automatically adjust or inform a light-based treatment. In embodiment, the light-based treatment may include modulating a lighting system that includes, but is not limited to, the light sources 120 of the one or more luminaire assemblies 104. In other embodiments, the light-based treatment may be implemented using one or more light sources that are not part of the system 100. In some embodiments, the light-based therapy may be adapted to treatment one or more symptoms and / or conditions of a movement-related disorder, such as Parkinson’s disease. In particular embodiments, the phototherapy can be the one disclosed in co-pending U.S. Provisional Serial No. 63 / 528,483, filed July 24, 2023, and entitled “LIGHTING SYSTEM FOR PHOTOTHERAPY OF DEMENTIA”, the contents of which are incorporated herein by reference.

[0092] In still further embodiments, the systems and methods described herein may include monitoring the effectiveness of a light-based treatment for an individual 102 by, for example, comparing disease progression metrics before and after treatments, or by comparing disease progression metrics over a treatment period.

[0093] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

[0094] All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms. The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

[0095] The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and / or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified.

[0096] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.”

[0097] As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.

[0098] As used herein, although the terms first, second, third, etc. may be used herein to describe various elements or components, these elements or components should not be limited by these terms. These terms are only used to distinguish one element or component from another element or component. Thus, a first element or component discussed below could be termed a second element or component without departing from the teachings of the inventive concept. Unless otherwise noted, when an element or component is said to be “connected to,” “coupled to,” or “adjacent to” another element or component, it will be understood that the element or component can be directly connected or coupled to the other element or component, or intervening elements or components may be present. That is, these and similar terms encompass cases where one or more intermediate elements or components may be employed to connect two elements or components. However, when an element or component is said to be “directly connected” to another element or component, this encompasses only cases where the two elements or components are connected to each other without any intermediate or intervening elements or components.

[0099] In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively.

[0100] It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.

[0101] The above-described examples of the described subject matter can be implemented in any of numerous ways. For example, some aspects can be implemented using hardware, software or a combination thereof. When any aspect is implemented at least in part in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single device or computer or distributed among multiple devices / computers.

[0102] The present disclosure can be implemented as a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0103] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer readable storage medium comprises the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc readonly memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0104] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0105] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, comprising an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions can execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’ s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, comprising a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some examples, electronic circuitry comprising, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0106] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to examples of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0107] The computer readable program instructions can be provided to a processor of a, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture comprising instructions which implement aspects of the function / act specified in the flowchart and / or block diagram or blocks.

[0108] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0109] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various examples of the present disclosure. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the Figures. For example, two blocks shown in succession can, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0110] Other implementations are within the scope of the following claims and other claims to which the applicant can be entitled.

[0111] While several inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and / or structures for performing the function and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and / or configurations will depend upon the specific application or applications for which the inventive teachings is / are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and / or methods, if such features, systems, articles, materials, kits, and / or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.

Claims

CLAIMS1. A system (100) for assessing disease progression in an individual (102) having a degenerative movement-related disease, the system (100) comprising: at least a first remote processing device (108) configured to assess disease progression information collected for the individual (102), the first remote processing device (108) comprising: a computer-readable storage medium (204) having stored thereon computer readable instructions (222) to be executed by one or more processors (202); and one or more processors (202) configured by the computer-readable instructions (222) stored on the computer-readable storage medium (204) to perform the following operations: (i) receive the non-imaging sensor data (106) generated by one or more multi-sensor bundles (130) of one or more luminaire assemblies (104); (ii) determine movement information for a movement event associated with the individual (102) based on the non-imaging sensor data (106); (iii) determine whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data (106); and (iv) store disease progression information for the individual (102), the disease progression information including the non-imaging sensor data (106) and the movement information for the movement event; wherein the one or more processors (202) of the first remote processing device (108) are configured by the computer-readable instructions (222) stored on the computer- readable storage medium (204) of the first remote processing device (108) to perform the following additional operations when the one or more exclusion criteria are not met: (v) extract a plurality of movement features for the individual from the non-imaging sensor data (106) associated with the movement event; (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual (102); and (vii) store the disease progression metric as disease progression information for the individual (102); andwherein the degenerative movement-related disease is at least one of Parkinson’s disease, dementia, and Alzheimer’s disease.

2. The system (100) of claim 1, wherein the multi-sensor bundle (130) of each luminaire assembly (104) comprises at least a single-pixel thermopile (“SPT”) sensor and an audio sensor such that the non-imaging sensor data (106) comprises one or more SPT signals generated by the SPT sensors and one or more audio signals generated by the audio sensors.

3. A system (100) for assessing disease progression in an individual (102) having a degenerative movement-related disease, the system (100) comprising: one or more luminaire assemblies (104), each luminaire assembly (104) comprising at least one light source (120) configured to produce light and a multi-sensor bundle ( ! 30) configured to generate non-imaging sensor data (106); at least a first electronic device (112) configured to receive the non-imaging sensor data (106) from the one or more luminaire assemblies (104) and transmit the nonimaging sensor data (106) to one or more remote processing devices (108); at least a first remote processing device (108) configured to receive the nonimaging sensor data (106) from at least the first electronic device (112), the first remote processing device (108) comprising: a computer-readable storage medium (204) having stored thereon computer-readable instructions (222) to be executed by one or more processors (202); and one or more processors (202) configured by the computer-readable instructions (222) stored on the computer-readable storage medium (204) of the remote processing device (108) to perform the following operations: (i) receive, from at least the first electronic device (112), the non-imaging sensor data (106) generated by the multi-sensor bundles (130) of the one or more luminaire assemblies (104); (ii) determine movement information for a movement event associated with the individual (102) based on the non-imaging sensor data (106); (iii) determine whether one or more exclusion criteria are met for the movement event based on the non-imaging sensor data (106); and (iv) store disease progression information for the individual (102), the disease progression information including the non-imaging sensor data (106) and the movement information for the movement event;wherein the one or more processors (202) of the first remote processing device (108) are configured by the computer-readable instructions (222) stored on the computer-readable storage medium (204) of the first remote processing device (108) to perform the following additional operations if the one or more exclusion criteria are not met: (v) extract a plurality of movement features for the individual (102) from the nonimaging sensor data (106) associated with the movement event; (vi) apply a trained machine learning model to the plurality of movement features, wherein the trained machine learning model outputs a disease progression metric for the individual (102); and (vii) store the disease progression metric as disease progression information for the individual (102); and at least a first user device (114) in communication with at least the first remote processing device (108), the first user device (114) comprising: a display device (118) configured to display a graphical user interface comprising disease progression information for the individual (102); a computer-readable storage medium (204) having stored thereon computer-readable instructions (222) to be executed by one or more processors (202); and one or more processors (202) configured by the computer-readable instructions (222) stored on the computer-readable storage medium (204) of the first user device (114) to perform the following operations: (i) retrieve disease progression information for the individual (102) from at least the first remote processing device (108); (ii) generate a graphical user interface comprising disease progression information for the individual; and (iii) display, via the display device (118), the graphical user interface; wherein the degenerative movement-related disease is at least one of Parkinson’s disease, dementia, and Alzheimer’s disease.

4. The system (100) of claim 3, wherein the non-imaging sensor data (106) includes non-imaging sensor data associated with multiple movement events over an assessment period.

5. The system (100) of claim 4, wherein the assessment period is at least two weeks.

6. The system (100) of claim 4, wherein the disease progression metric for the individual (102) is indicative of a change in the progression of the degenerative movement- related disease of the individual (102) between one or more movement events over the assessment period.

7. The system (100) of claim 3, wherein the one or more exclusion criteria include at least one of: (a) whether the individual (102) was responding to a time-sensitive event; (b) whether the individual (102) was talking on a phone or communicating with a third party in proximity of the individual (102) during the movement event; and (c) whether the individual (102) was interacting with an interface of a personal electronic device during the movement event.

8. The system (100) of claim 3, wherein the multi-sensor bundle (130) of each luminaire assembly (104) comprises at least a single-pixel thermopile (“SPT”) sensor and an audio sensor, and wherein the non-imaging sensor data comprises an SPT signal generated by the SPT sensor and an audio signal generated by the audio sensor.

9. The system (100) of claim 8, wherein the non-imagining sensor data (106) comprises at least an SPT signal and an audio signal from the multi-sensor bundles (130) of a plurality of luminaire assemblies (104).

10. The system (100) of claim 8, wherein the multi-sensor bundle (130) of one or more luminaire assemblies (104) further comprises a WiFi sensor, a daylight sensor, a vibration sensor, a CO2 sensor, a radar sensor, a ToF sensor, and / or a passive infrared sensor.

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