Abstract:
A method and corresponding system for increasing the likelihood of inducing behavior change in a lifestyle management program for a user includes sensing at least one behavior parameter of a user; identifying at least one intention-behavior gap based on the sensing of the behavior parameter; via application of a genetic algorithm generating a quantified profile of the intention-behavior gap; via a user interface, suggesting at least one action to accept or reject; and varying the quantified profile based on the action accepted or rejected by the user. The user interface may include a smart phone that includes at least one sensor for sensing location and activity of a user of the user interface, or a dedicated device that communicates with a sensor for sensing a behavior parameter, or the sensor is disposed on the body of the user or on a garment worn by the user.
Abstract:
A method and corresponding system for increasing the likelihood of inducing behavior change in a lifestyle management program for a user includes sensing at least one behavior parameter of a user; identifying at least one intention-behavior gap based on the sensing of the behavior parameter; via application of a genetic algorithm generating a quantified profile of the intention-behavior gap; via a user interface, suggesting at least one action to accept or reject; and varying the quantified profile based on the action accepted or rejected by the user. The user interface may include a smart phone that includes at least one sensor for sensing location and activity of a user of the user interface, or a dedicated device that communicates with a sensor for sensing a behavior parameter, or the sensor is disposed on the body of the user or on a garment worn by the user.
Abstract:
The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.
Abstract:
Disclosed herein is a medical system (100, 300, 500) comprising: a memory (110) storing machine executable instructions, at least one set of predetermined coordinates (124), and a position identifying algorithm (122). The position identifying algorithm is configured for outputting a set of current coordinates (128) for each of the at least one set of predetermined coordinates in response to receiving a current image descriptive of an object (306, 310). The execution of machine executable instructions (120) causes a computational system (104) to repeatedly receive (200) a current image (126) from a camera system (304). The execution of machine executable instructions (120) causes a computational system (104) to perform the following for the current image: receive (202) the set of current coordinates for each of the at least one set of predetermined coordinates in response to inputting the current image into the position identifying algorithm; calculate (204) a positional difference (130) between the at least one set of predetermined coordinates and its set of current coordinates; calculate (206) a one-dimensional value (134) from positional difference using an objective function; and provide (208) a one-dimensional position indicator (136, 314, 600, 602, 608, 800, 900, 1002) for each of and controlled by each one-dimensional value in real time using a user interface (108, 308, 416, 418).
Abstract:
A method and corresponding system for increasing the likelihood of inducing behavior change in a lifestyle management program for a user includes sensing at least one behavior parameter of a user; identifying at least one intention-behavior gap based on the sensing of the behavior parameter; via application of a genetic algorithm generating a quantified profile of the intention-behavior gap; via a user interface, suggesting at least one action to accept or reject; and varying the quantified profile based on the action accepted or rejected by the user. The user interface may include a smart phone that includes at least one sensor for sensing location and activity of a user of the user interface, or a dedicated device that communicates with a sensor for sensing a behavior parameter, or the sensor is disposed on the body of the user or on a garment worn by the user.
Abstract:
A method for coaching a subject, including receiving data corresponding to at least one of a desired behavior of the subject or an undesired behavior of the subject, receiving a level of available self control of the subject via an input source, calculating a level of required self control needed to perform the desired behavior or suppress the undesired behavior, analyzing a risk based on the level of available self control and the level of required self control, where the risk is associated with predicting whether the subject will perform the undesired behavior or not perform the desired behavior, and intervening when the risk analyzed is above a particular threshold.