Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing intervention recommendation operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform intervention recommendations by using at least one of reinforcement learning machine learning models and event scoring machine learning models.

Abstract: Solutions for more efficient and effective optical character recognition with respect to an input text segment are disclosed. In one example, a method includes processing an input text image using a deep character overlap detection machine learning model in order to generate a character map for the input text image, an overlap map for the input text image, and an affinity map for the input text image; generating an overlap-aware word boundary recognition output based at least in part on the character map, the overlap map, and the affinity map, wherein the overlap-aware word boundary recognition output describes one or more inferred word regions of the input text image; and performing one or more prediction-based actions based at least in part on the overlap-aware word boundary recognition output.

Abstract: There is a need to accurately and dynamically predicting a probability for an event and a likely cause for the event prior to the event occurring using collected data from disparate data sources. This need can be addressed, for example, by generating an event prediction data object by utilizing an event prediction machine learning model, wherein the event prediction data object describes an event likelihood prediction and in an instance where the event likelihood prediction is an affirmative likelihood prediction, one or more fall cause predictions; and performing one or more prediction-based actions based at least in part on the event likelihood prediction.

Abstract: There is a need to accurately and dynamically predicting a probability for an event and a likely cause for the event prior to the event occurring using collected data from disparate data sources. This need can be addressed, for example, by generating an event prediction data object by utilizing an event prediction machine learning model, wherein the event prediction data object describes an event likelihood prediction and in an instance where the event likelihood prediction is an affirmative likelihood prediction, one or more fall cause predictions; and performing one or more prediction-based actions based at least in part on the event likelihood prediction.

Abstract: Solutions for more efficient and effective optical character recognition with respect to an input text segment are disclosed. In one example, a method includes processing an input text image using a deep character overlap detection machine learning model in order to generate a character map for the input text image, an overlap map for the input text image, and an affinity map for the input text image; generating an overlap-aware word boundary recognition output based at least in part on the character map, the overlap map, and the affinity map, wherein the overlap-aware word boundary recognition output describes one or more inferred word regions of the input text image; and performing one or more prediction-based actions based at least in part on the overlap-aware word boundary recognition output.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Abstract: There is a need for more effective and efficient anomalous text detection.

Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

Abstract: Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.

Abstract: Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.

Abstract: There is a need for more reliable and efficient disease spread forecasting. This need can be addressed by, for example, solutions for performing optimization-based disease spread forecasting using a multi-risk-level disease spread forecasting machine learning model.

Abstract: There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.