Abstract: A non-invasive health monitoring system comprises a wearable central sensor and at least one wearable remote sensor in wireless communication, a portable device readily accessible to the user, and a cloud platform. Each sensor collects batches of data indicative of one or more physiological parameters of the user at a physiological parameter-specific frequency, for a pre-defined time window. The central sensor receives and processes the measured data from each sensor, and stores processed data in a memory within the central sensor. The portable device comprises a receiver wirelessly receiving the processed data and instructions from the central sensor; a processor running a mobile application handling the processed data and instructions; and a transmitter. The cloud platform receives the processed data from the transmitter; analyzes the received processed data; and transmits the results to at least one of the portable device and an authorized healthcare entity.

Abstract: A power-optimized eco-system for tracking a user's health comprises: one or more wearable remote sensors, each wirelessly communicating only with one wearable central sensor; a portable device readily accessible to the user; and a cloud platform. Each sensor is configured to measure data indicative of one or more physiological parameters. The central sensor is configured to receive and subsequently process data measured by each remote sensor, to process data measured by the central sensor, and to generate corresponding instructions. The portable device comprises: a receiver wirelessly receiving the processed data and instructions from the central sensor; a processor running a mobile application handling the processed data and instructions; and a transmitter. The cloud platform is configured to: receive the processed data from the transmitter; analyze the received processed data; and transmit the results of the analysis to at least one of the portable device and an authorized healthcare entity.

Abstract: An eco-system for tracking a user's physiological parameters comprises a central sensor and at least one remote sensor in wireless communication with the central sensor, a portable device readily accessible to the user, and a cloud platform. Each sensor may be worn by the user and measure data indicative of one or more of the physiological parameters. The central sensor may receive and process the measured data from each remote sensor and processes its own measured data. The portable device comprises a receiver wirelessly receiving the processed data and instructions from the central sensor; a processor running a mobile application handling the processed data and instructions; and a transmitter transmitting the processed data. The cloud platform receives the processed data from the transmitter; analyzes the received processed data; and transmits the results of the analysis to at least one of the portable device and an authorized healthcare entity.

Abstract: A personalized adaptive health monitoring system comprises: a wearable central sensor wirelessly communicating with at least one wearable remote sensor, each sensor being configured to measure data indicative of one or more physiological parameters of a user; a portable device, and a cloud platform. The central sensor receives and processes measured data from each remote sensor and processes data measured by the central sensor. The portable device comprises: a receiver wirelessly receiving the processed data from the central sensor; a processor running a mobile application handling the processed data; and a transmitter. The cloud platform receives the processed data from the transmitter; analyzes the received processed data; and transmit results of the analysis to at least one of the portable device and an authorized healthcare entity. Analyzing the received processed data comprises comparing the received processed data with previously received data from the user and/or from other comparable individuals.

Abstract: A cloud-based analytical platform for health data pattern and trend analysis is configured to: receive, from a transmitter of a portable device, processed data derived from physiological data measurements gathered from sensors worn by a user, the sensors being communicatively coupled to the portable device; analyze the received processed data; and transmit the results of the analysis to at least one of the portable device and an authorized healthcare entity. The analyzing of the received processed data comprises using multi-parameter machine learning algorithms to automatically derive the current state of one or more physiological vital parameters and related health conditions for an individual user; automatically deriving deviations from baseline for each of the one or more physiological vital parameters of the user; and automatically deriving a long-term trend for each of the one or more physiological vital parameters of the user.

Abstract: Methods and apparatuses to generate test patterns for detecting faults in an integrated circuit (IC) are described. During operation, the system receives a netlist and a layout for the IC. The system then generates a set of faults associated with the netlist to model a set of defects associated with the IC. Next, the system determines a set of likelihoods of occurrence for the set of faults based at least on a portion of the layout associated with each fault in the set of faults. The system subsequently generates a set of test patterns to target the set of faults, wherein the set of test patterns are generated based at least on the set of likelihoods of occurrence associated with the set of faults.