Abstract: A computer-implemented method of decoding video data in multiple resolution formats includes receiving an encoded video stream including a salient data and a non-salient data, the salient data having a higher resolution format than the non-salient data. The video stream is decoded into the non-salient data in a lower-resolution format and the salient data in the higher-resolution format. The non-salient data is reconstructed to a higher resolution format. The salient data and the reconstructed non-salient data are combined to form a video stream in the higher-resolution format of the salient data.

Abstract: A computer-implemented method of decoding video data in multiple resolution formats includes receiving an encoded video stream including a salient data and a non-salient data, the salient data having a higher resolution format than the non-salient data. The video stream is decoded into the non-salient data in a lower-resolution format and the salient data in the higher-resolution format. The non-salient data is reconstructed to a higher resolution format. The salient data and the reconstructed non-salient data are combined to form a video stream in the higher-resolution format of the salient data.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include identifying one or more joints of a user based on collected data and generating one or more 3D representations of the one or more joints of the user. Embodiments may further include anonymizing the one or more 3D representations, classifying one or more actions of the user based on the one or more 3D representations, wherein the classifying outputs an action score, and exporting at least one of the one or more actions and the action score.

Abstract: A method, a computer program product, and a computer system determine abnormal motion from a patient. The method includes receiving sensory data of the patient and a location in which the patient is present. The sensory data includes video data over a period of time the patient is being monitored. The method includes generating contextual information based on the sensory data. The contextual information is indicative of surroundings of the patient and characteristics of the location. The method includes generating motion information based on the sensory data. The motion information is indicative of movement of the patient in the location. The method includes generating contextual motion data by incorporating the contextual information with the motion information. The method includes determining the abnormal motion based on the contextual motion data.

Abstract: A computer-implemented method of encoding video streams for low-bandwidth transmissions includes identifying a salient data and a non-salient data in a high-resolution video stream. The salient data and the non-salient data is segmented. The non-salient data is compressed to a lower resolution. The salient data and the compressed non-salient data are transmitted in a low-bandwidth transmission.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Abstract: Aspects described herein include a method of generating three-dimensional (3D) spikes. The method comprises receiving a signal comprising time-series data and generating a first two-dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing one or more Parkinson's disease symptoms. The exemplary embodiments may include collecting data of a user's motion, extracting one or more features from the collected data, and assessing one or more Parkinson's disease symptoms of the user based on applying one or more models to the data.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Abstract: A computer-implemented method of encoding video streams for low-bandwidth transmissions includes identifying a salient data and a non-salient data in a high-resolution video stream. The salient data and the non-salient data is segmented. The non-salient data is compressed to a lower resolution. The salient data and the compressed non-salient data are transmitted in a low-bandwidth transmission.

Abstract: A method, a computer program product, and a computer system determine abnormal motion from a patient. The method includes receiving sensory data of the patient and a location in which the patient is present. The sensory data includes video data over a period of time the patient is being monitored. The method includes generating contextual information based on the sensory data. The contextual information is indicative of surroundings of the patient and characteristics of the location. The method includes generating motion information based on the sensory data. The motion information is indicative of movement of the patient in the location. The method includes generating contextual motion data by incorporating the contextual information with the motion information. The method includes determining the abnormal motion based on the contextual motion data.

Abstract: A machine learning model can be optimized for deployment on a device based on hardware specifications of the device. An existing model is acquired and pruned to reduce hardware resource consumption of the model. The pruned model is then trained based on training data. The pruned model is also trained based on a collection of “teacher” models. Performance of the trained model is then evaluated and compared to performance requirements, which can be based on the hardware specifications of a device.

Abstract: A machine learning model can be updated based on collected data (i.e., initially unlabeled data). The unlabeled data can be labeled based on comparisons to labeled data. The newly labeled data, referred to as “weak labeled data” (as it was labeled without direct input of a professional) can then be used as training data in order to retrain the machine learning model.

Abstract: Aspects described herein include a method of generating three-dimensional (3D) spikes. The method comprises receiving a signal comprising time-series data and generating a first two-dimensional (2D) grid. Generating the first 2D grid comprises mapping segments of the time-series data to respective positions of the first 2D grid, and generating, for each position, a spike train corresponding to the respective mapped segment. The method further comprises generating a second 2D grid including performing, for each position, a mathematical operation on the spike train of the corresponding position of the first 2D grid. The method further comprises generating a third 2D grid including performing spatial filtering on the positions of the second 2D grid. The method further comprises generating a 3D grid based on a combination of the first 2D grid, the second 2D grid, and the third 2D grid. The 3D grid comprises one or more 3D spikes.

Abstract: Provided are embodiments for a computer-implemented method. The method includes receiving a sequence of image data, transforming objects in each frame of the sequence of the image data into direction vectors, and clustering the direction vectors based at least in part on features of the objects. The method also includes mapping the direction vectors for the objects in each frame into a position-orientation data structure, and performing tracking using the mapped direction vectors in the position-orientation data structure. Also provided are embodiments of a computer program product and a system for performing object tracking.

Abstract: A method for digital image classification and localization includes receiving a digital image of a biological organism from an imaging apparatus, the digital image comprising a plurality of intensities on a 2-dimensional grid of points, generating a plurality of discriminative representations of the 2D digital image by extracting dominant characteristics of the image from three different viewpoints, where the plurality of discriminative representations form a 3-dimensional digital image, combining the 3D digital image with the 2D digital image in a convolutional neural network that outputs a 3-channel feature map that localizes image abnormalities in each of the three channels and includes a detection confidence that each abnormalities is a neoplasm, providing the 3-channel feature map to a controller of a robotic surgical device where the robotic surgical device uses the 3-channel feature map to locate the neoplasm within the biological organism in a surgical procedure for treating the neoplasm.

Abstract: A method for digital image classification and localization includes receiving a digital image of a biological organism from an imaging apparatus, the digital image comprising a plurality of intensities on a 2-dimensional grid of points, generating a plurality of discriminative representations of the 2D digital image by extracting dominant characteristics of the image from three different viewpoints, where the plurality of discriminative representations form a 3-dimensional digital image, combining the 3D digital image with the 2D digital image in a convolutional neural network that outputs a 3-channel feature map that localizes image abnormalities in each of the three channels and includes a detection confidence that each abnormalities is a neoplasm, providing the 3-channel feature map to a controller of a robotic surgical device where the robotic surgical device uses the 3-channel feature map to locate the neoplasm within the biological organism in a surgical procedure for treating the neoplasm.