Abstract: A system includes a heads up display (HUD) device for a vehicle, a cloud provisioned computing system, and one or more service provider devices is provided. The HUD device is configured to obtain biometric feedback data of a user; input the biometric feedback data to a remote photoplethysmography (rPPG) model to determine biometric condition data of the user; and generate an output for the user. The cloud provisioned computing system is configured to input a prompt to a machine learning-artificial intelligence (ML-AI) language model to determine a service provider system and provide to a common interface a request for service provider data; receive the service provider data; and provide the control signal to the HUD. The service provider device is configured to receive the request; determine the service provider data associated with the request; and provide the service provider data.

Abstract: A method and system provide for automated counting of prescription product and enables virtual verification of the dispensed prescription product. The method and system include capturing by a camera at a first site an image of the prescription product to be dispensed according to a prescription to a patient, electronically displaying the image on a display at a second site remote from the first site, and electronically transmitting a verification from the second site to the first site in response to the image of the prescription product being determined at the second site to be consistent with the prescription.

Abstract: A system includes a heads up display (HUD) device for a vehicle, a cloud provisioned computing system, and one or more service provider devices is provided. The HUD device is configured to obtain biometric feedback data of a user; input the biometric feedback data to a remote photoplethysmography (rPPG) model to determine biometric condition data of the user; and generate an output for the user. The cloud provisioned computing system is configured to input a prompt to a machine learning-artificial intelligence (ML-AI) language model to determine a service provider system and provide to a common interface a request for service provider data; receive the service provider data; and provide the control signal to the HUD. The service provider device is configured to receive the request; determine the service provider data associated with the request; and provide the service provider data.

Abstract: A method is provided. The method comprises obtaining, using electroencephalogram (EEG) sensors, a first set of EEG signals that comprises a plurality of first waveforms, and each of the plurality of first waveforms is associated with a frequency band from a plurality of frequency bands; training a plurality of machine learning-artificial intelligence (ML-AI) models using the first set of EEG signals, wherein each of the plurality of ML-AI models is trained for a different frequency band; obtaining, using the EEG sensors, a second set of EEG signals, wherein the second set of EEG signals comprises a plurality of second waveforms; inputting each of the plurality of second waveforms associated with the frequency band into a corresponding ML-AI model associated with the respective frequency band to generate a plurality of outputs; and performing one or more actions based on the plurality of outputs.

Abstract: A method is provided, the method including obtaining one or more vision machine learning—artificial intelligence (ML-AI) models associated with a product and obtaining one or more images of a product label of the product. The product label indicates a variable ink that changes colors based on environmental aspects. The method also includes determining condition information indicating a status of the product label based on the one or more vision ML-AI models and the one or more images of the product label indicating the variable ink, and outputting an indicator indicating the status of the product label based on the condition information.

Abstract: In some instances, an accessibility headset for visually impaired or hearing impaired users is provided. The accessibility headset comprises one or more light detection and ranging (LIDAR) devices configured to obtain LIDAR measurements; one or more infrared (IR) sensors configured to obtain IR feedback from one or more products; an output system comprising: one or more haptic devices configured to provide haptic feedback to a user; and a second output device configured to provide alternative feedback to the user; and a processor. The processor is configured to: determine a set of navigation instructions to direct the user to a first product by avoiding one or more first obstacles and provide, to the user, one or more first instructions from the set of navigation instructions using an output system based on an indication of whether the user is visually impaired or hearing impaired.

Abstract: A method is provided. The method comprises obtaining, using electroencephalogram (EEG) sensors, a first set of EEG signals that comprises a plurality of first waveforms, and each of the plurality of first waveforms is associated with a frequency band from a plurality of frequency bands; training a plurality of machine learning-artificial intelligence (ML-AI) models using the first set of EEG signals, wherein each of the plurality of ML-AI models is trained for a different frequency band; obtaining, using the EEG sensors, a second set of EEG signals, wherein the second set of EEG signals comprises a plurality of second waveforms; inputting each of the plurality of second waveforms associated with the frequency band into a corresponding ML-AI model associated with the respective frequency band to generate a plurality of outputs; and performing one or more actions based on the plurality of outputs.

Abstract: A method is provided, the method including obtaining one or more vision machine learning—artificial intelligence (ML-AI) models associated with a product and obtaining one or more images of a product label of the product. The product label indicates a variable ink that changes colors based on environmental aspects. The method also includes determining condition information indicating a status of the product label based on the one or more vision ML-AI models and the one or more images of the product label indicating the variable ink, and outputting an indicator indicating the status of the product label based on the condition information.

Abstract: A system comprising a first expired drug usability device is provided. The first expired drug usability device comprises a first spectrometer and one or more first processors. The first processors are configured to: obtain at least one expired drug machine learning-artificial intelligence (ML-AI) model associated with a pharmaceutical drug; obtain drug expiration information of a sample of the pharmaceutical drug, wherein the drug expiration information comprises spectrometer data associated with using the first spectrometer on the sample; input the drug expiration information into the at least one expired drug ML-AI model to determine usability information associated with the sample of the pharmaceutical drug; and perform one or more actions based on the usability information.

Abstract: In some instances, an accessibility headset for visually impaired or hearing impaired users is provided. The accessibility headset comprises one or more light detection and ranging (LIDAR) devices configured to obtain LIDAR measurements; one or more infrared (IR) sensors configured to obtain IR feedback from one or more products; an output system comprising: one or more haptic devices configured to provide haptic feedback to a user; and a second output device configured to provide alternative feedback to the user; and a processor. The processor is configured to: determine a set of navigation instructions to direct the user to a first product by avoiding one or more first obstacles and provide, to the user, one or more first instructions from the set of navigation instructions using an output system based on an indication of whether the user is visually impaired or hearing impaired.

Abstract: In some instances, a user device for determining whether an individual is sick is provided. The user device is configured to obtain a facial image of an individual; obtain an audio file comprising a voice recording of the individual; determine a facial recognition confidence value associated with whether the individual is sick based on inputting the facial image into a facial recognition machine learning dataset that is individualized for the individual; determine a voice recognition confidence value associated with whether the individual is sick based on inputting the audio file into a voice recognition machine learning dataset that is individualized for the individual; determine whether the individual is sick based on the facial recognition confidence value and the voice recognition confidence value; and causing display of a prompt indicating whether the individual is sick.

Abstract: In some instances, the disclosure provides a method performed by a smart vital device. The method comprises receiving sensor information indicating one or more health characteristics associated with an individual, wherein the sensor information comprises audio information indicating audio signals from a surrounding environment and temperature information indicating temperature readings from the surrounding environment, determining one or more health audio characteristics of the individual based on inputting the audio signals into one or more health condition machine learning datasets, determining one or more health temperature characteristics of the individual based on the temperature readings from the surrounding environment, determining one or more health conditions of the individual based on the one or more health audio characteristics and the one or more health temperature characteristics, and outputting the one or more health conditions of the individual.

Abstract: In some instances, the disclosure provides a method performed by a smart vital device. The method comprises receiving sensor information indicating one or more health characteristics associated with an individual, wherein the sensor information comprises audio information indicating audio signals from a surrounding environment and temperature information indicating temperature readings from the surrounding environment, determining one or more health audio characteristics of the individual based on inputting the audio signals into one or more health condition machine learning datasets, determining one or more health temperature characteristics of the individual based on the temperature readings from the surrounding environment, determining one or more health conditions of the individual based on the one or more health audio characteristics and the one or more health temperature characteristics, and outputting the one or more health conditions of the individual.

Abstract: In some instances, a fall detection system comprising a first fall detection device and a user device is provided. The fall detection device is configured to: detect an occurrence of a fall event associated with an individual based on sensor information from the one or more sensors and a fall detection model; and provide a first indication indicating the occurrence of the fall event. The user device is configured to: receive the first indication; cause display of a prompt requesting user feedback as to whether the individual fell based on the first indication and a second indication from a second fall detection device; provide update information indicating for the first fall detection device to update the fall detection model based on the user feedback; and provide user fall information associated with the occurrence of the fall event based on the user feedback.

Abstract: In some instances, a user device for determining whether an individual is sick is provided. The user device is configured to obtain a facial image of an individual; obtain an audio file comprising a voice recording of the individual; determine a facial recognition confidence value associated with whether the individual is sick based on inputting the facial image into a facial recognition machine learning dataset that is individualized for the individual; determine a voice recognition confidence value associated with whether the individual is sick based on inputting the audio file into a voice recognition machine learning dataset that is individualized for the individual; determine whether the individual is sick based on the facial recognition confidence value and the voice recognition confidence value; and causing display of a prompt indicating whether the individual is sick.

Abstract: In some instances, a fall detection system comprising a first fall detection device and a user device is provided. The fall detection device is configured to: detect an occurrence of a fall event associated with an individual based on sensor information from the one or more sensors and a fall detection model; and provide a first indication indicating the occurrence of the fall event. The user device is configured to: receive the first indication; cause display of a prompt requesting user feedback as to whether the individual fell based on the first indication and a second indication from a second fall detection device; provide update information indicating for the first fall detection device to update the fall detection model based on the user feedback; and provide user fall information associated with the occurrence of the fall event based on the user feedback.

Abstract: In some instances, a method performed by a smart pill bottle delivery device is provided. The method comprises determining one or more prescription timing intervals indicating times for the user to take one or more medication units of the medication based on retrieving a prescription for the user from memory, providing one or more visual notifications indicating for the user to take the one or more medication units based on the one or more prescription timing intervals, subsequent to providing the one or more visual notifications, detecting position information indicating an action performed by the user, and causing display of a second display screen based on the position information indicating the action performed by the user.

Abstract: In some instances, a method performed by a smart pill bottle delivery device is provided. The method comprises determining one or more prescription timing intervals indicating times for the user to take one or more medication units of the medication based on retrieving a prescription for the user from memory, providing one or more visual notifications indicating for the user to take the one or more medication units based on the one or more prescription timing intervals, subsequent to providing the one or more visual notifications, detecting position information indicating an action performed by the user, and causing display of a second display screen based on the position information indicating the action performed by the user.

Abstract: In some instances, the disclosure provides a method performed by a smart vital device. The method comprises receiving sensor information indicating one or more health characteristics associated with an individual, wherein the sensor information comprises audio information indicating audio signals from a surrounding environment and temperature information indicating temperature readings from the surrounding environment, determining one or more health audio characteristics of the individual based on inputting the audio signals into one or more health condition machine learning datasets, determining one or more health temperature characteristics of the individual based on the temperature readings from the surrounding environment, determining one or more health conditions of the individual based on the one or more health audio characteristics and the one or more health temperature characteristics, and outputting the one or more health conditions of the individual.

Abstract: In some instances, a fall detection system comprising a first fall detection device and a user device is provided. The fall detection device is configured to: detect an occurrence of a fall event associated with an individual based on sensor information from the one or more sensors and a fall detection model; and provide a first indication indicating the occurrence of the fall event. The user device is configured to: receive the first indication; cause display of a prompt requesting user feedback as to whether the individual fell based on the first indication and a second indication from a second fall detection device; provide update information indicating for the first fall detection device to update the fall detection model based on the user feedback; and provide user fall information associated with the occurrence of the fall event based on the user feedback.