Abstract: Embodiments include systems, methods, and computer program products for monitoring progression of Parkinson's disease. Aspects include receiving pressure data from a plurality of pressure sensors, the pressure sensors being positioned on a chair. Aspects also include analyzing the pressure data to determine the severity of a unified Parkinson's disease rating scale factor for a patient.

Abstract: A cognitive illumination system is provided. The cognitive illumination system includes a light source communicatively coupled to a servomechanism, wherein the servomechanism is configured to manipulate the light source in space, a tracking assembly configured to capture and track objects in the space and a processor communicatively coupled to the light source, the servomechanism and the tracking assembly. The processor is configured to receive information about the captured and tracked objects from the tracking assembly, determine a task being undertaken in the space therefrom, develop a lighting plan for the task which is executable by the light source and the servomechanism and control the light source and the servomechanism according to the lighting plan.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method, executed by one or more processors, includes monitoring a plurality of ambient condition sensors configured to sense a plurality of ambient conditions that are relevant to a plurality of communication modes provided by at least one user I/O device, deactivating a first communication mode of the plurality of communication modes and activating a second communication mode of the plurality of communication modes in response to determining, via the plurality of ambient condition sensors, that one or more of the plurality of ambient conditions are not conducive to the first communication mode and that the plurality of ambient conditions are collectively more conducive to the second communication mode than the first communication mode. A corresponding apparatus and computer program product are also disclosed herein.

Abstract: A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving GPS signals from each GPS satellite in view of the UAV, the GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

Abstract: A method, executed by one or more processors, includes monitoring a plurality of ambient condition sensors configured to sense a plurality of ambient conditions that are relevant to a plurality of communication modes provided by at least one user I/O device, deactivating a first communication mode of the plurality of communication modes and activating a second communication mode of the plurality of communication modes in response to determining, via the plurality of ambient condition sensors, that one or more of the plurality of ambient conditions are not conducive to the first communication mode and that the plurality of ambient conditions are collectively more conducive to the second communication mode than the first communication mode. A corresponding apparatus and computer program product are also disclosed herein.

Abstract: A system, computer program product, and method are provided for use with an intelligent computer platform to convert speech intents to one or more physical actions. The aspect of converting speech intent includes receiving audio, converting the audio to text, parsing the text into segments, identifying a physical action and associated application that are proximal in time to the received audio. A corpus is searched for evidence of a pattern associated with the received audio and corresponding physical action(s) and associated application. An outcome is generated from the evidence. The outcome includes identification of an application and production of an affirmative instruction. The instruction is converted to a user interface trace that is executed within the identified application.

Abstract: A system, computer program product, and method are provided for use with an intelligent computer platform to convert audio data intents to one or more physical actions. The aspect of converting audio data intent includes receiving audio, converting the audio to text, parsing the text into segments, identifying a physical action and associated application that are proximal in time to the received audio. A corpus is searched for evidence of the text to identify evidence of a physical user interface trace with a select application. An outcome is generated from the evidence. The outcome includes an instruction to invoke a user interface trace with the select application as a representation of the received audio.

Abstract: A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving raw GPS measurements from each GPS satellite in view of the UAV, the raw GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

Abstract: A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A vehicular alert system includes an autonomous aerial vehicle and a central computer. The autonomous aerial vehicle includes a processor, a display, and a detector. The processor controls a data transceiver. The detector detects one or more vehicular condition. The central computer communicates with the autonomous aerial vehicle via the data transceiver. The central computer includes a memory device. The memory device stores vehicular condition data and road condition data. The central computer communicates one of a vehicular condition or a road condition to the autonomous aerial vehicle. The processor of the autonomous aerial vehicle displays the received condition on the display.

Abstract: A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method, system, and/or computer program product ameliorates mosquito populations. A flying drone is deployed over an area. Sensor readings that identify a presence of water in the area are received, and one or more processors determine a confidence level L that the water in the area is stagnant water. The flying drone is then directed to perform an amelioration action against the mosquito larvae based a value of the determined confidence level L.

Abstract: A cognitive illumination system is provided. The cognitive illumination system includes a light source communicatively coupled to a servomechanism, wherein the servomechanism is configured to manipulate the light source in space, a tracking assembly configured to capture and track objects in the space and a processor communicatively coupled to the light source, the servomechanism and the tracking assembly. The processor is configured to receive information about the captured and tracked objects from the tracking assembly, determine a task being undertaken in the space therefrom, develop a lighting plan for the task which is executable by the light source and the servomechanism and control the light source and the servomechanism according to the lighting plan.

Abstract: Mechanisms are provided for implementing a personalized health care management system. The mechanisms receive a personalized health care plan for a patient having at least one health goal of the patient, and dynamic patient monitoring data from one or more patient monitoring devices associated with the patient. The mechanisms analyze the dynamic patient monitoring data to determine at least one first communication to output to the patient containing content eliciting conformance of the patient with the personalized health care plan to achieve the at least one health goal. The mechanisms send, to a patient care manager computing device of a patient care manager associated with the patient, a second communication based on results of analyzing the dynamic patient monitoring data. The second communication initiates a new communication session, or continues an existing communication session, between the patient care manager computing device and a patient communication device associated with the patient.

Abstract: Mechanisms are provided for implementing a personalized health care management system. The mechanisms receive a personalized health care plan for a patient, and dynamic patient monitoring data from patient monitoring devices associated with the patient. The mechanisms analyze the dynamic patient monitoring data to identify at least one pattern of dynamic patient monitoring data representing a habit of the patient. The mechanisms generate desired pattern data based on results of the analysis. The desired pattern data represents at least one desired habit for the patient. The mechanisms determine at least one communication to output to the patient via a patient computing device or patient communication device to elicit conformance of the patient with the at least one desired habit based on the generated desired pattern data and the personalized health care plan. The mechanisms output the at least one communication to the patient computing device or patient communication device.

Abstract: Mechanisms are provided to implement a personalized health care management system. The mechanisms receive a personalized health care plan for a patient, and dynamic patient monitoring data from one or more patient monitoring devices associated with the patient. The mechanisms analyze the dynamic patient monitoring data to identify at least one pattern of dynamic patient monitoring data representing a habit of the patient, and generate desired pattern data based on results of the analysis. The desired pattern data represents at least one desired habit for the patient. The mechanisms determine at least one deviation of the desired pattern data from the at least one pattern of dynamic patient monitoring data, and perform at least one health management operation to assist the patient in minimizing the determined at least one deviation.

Abstract: Systems and methods for storing in a first database a user personal profile, storing in a second database per-restaurant profiles for a plurality of restaurants, enabling the user to connect to a cognitive computer, enabling the user to interact with the cognitive computer for generating a personalized recipe based on user culinary selections and the user profile in the first database, the personalized recipe including a first list of ingredients, determining by the cognitive computer whether there are one or more first type candidate restaurants for preparing the personalized recipe based on the per-restaurant profiles in the second database, the first type candidate restaurant being determined to be able to prepare the personalized recipe with the first list of ingredients, receiving a selection of a selected restaurant from the first type candidate restaurant and contracting out the preparation of the personalized recipe to the selected restaurant.

Abstract: Aspects of the invention relate to a UAV (drone) data marketplace where requests for UAV services are matched with registered UAVs. Drone operators register in the marketplace drones in their fleet with their capabilities and requesters of drone services make their request in the marketplace. The requests can be a set of data to be collected (e.g., optical images, NIR data, temperatures, etc.) and/or actions to be performed (e.g., deploying spare machinery parts to a tractor in the field), a location from which that data is collected and/or location to which a delivery is to be made, a pipeline of analytics to be performed on the data (e.g., optical recognition, NDVI computation, fertilizer application recommendation), a timeframe in which to collect the data (e.g., “by next week”, “by the end of today”), and a market value for how much the Requester is willing to pay for that data or operation.