Abstract: Systems and methods of progression (e.g. continuous ongoing and long term) assessment of a neurological disease of a patient, including: receiving, from a device that is wearable by the patient, 3D acceleration data and at least one physiological signal; determining, by a server, with a dedicated deep learning algorithm a progression of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, wherein determination of the progression of the neurological disease includes: determining a first metric for patient's activity, determining a second metric based on a measured gait speed, and aggregating the first metric and the second metric into a combined approximation of progression of the neurological disease.

Abstract: Systems and methods of determining a mis-step of a user with gait abnormality, including: calibrating signals corresponding to motion by the user in a controlled environment to identify the gait of the user, detecting a movement of the user to determine a change from the determined gait, detecting at least one physiological signal of the user, identifying motion carried out by the user based on data from at least one wearable sensor, and determining at least one mis-step carried out by the user using a machine learning algorithm trained to determine steps based on the identified motion.

Abstract: A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

Abstract: A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

Abstract: Systems and methods for automatically adjusting a picture or a video containing a face, by detecting the face in a picture, constructing a face-structure mask for the face, obtaining a set of model face-structure masks and modification instructions, and modifying the face. The disclosure further provides for displaying the modified picture, receiving user feedback regarding the modifications, and processing the user feedback.