Intel Corporation

Apoorv Vyas

Kumar Ranganathan

Jaroslaw Sydir

Venkat Natarajan

Amit S Baxi

Srenivas Varadarajan

Venkataraman Natarajan

Darshan Mehta

Jose Joy

Harshpal Singh

Sanika Phatak

Krishna P Bhat D

Methods, systems, and storage media relating to a circulatory health monitor device are disclosed herein. In an embodiment, such a device may be positioned or disposed on and/or in contact with a subject to measure blood pressure and/or heart rate. The device may include one or more electrocardiographic (ECG) signal sensors, one or more photoplethysmographic (PPG) sensors, and a controller. The controller may activate at a low resolution a PPG measurement of the subject in relation to an ECG signal feature to identify a PPG signal feature location or region in the PPG measurement. The controller may further activate at a high resolution PPG measurement of the subject at the PPG signal feature region to identify the PPG signal feature, and may determine blood pressure and/or heart rate therefrom. Other embodiments may be disclosed and/or claimed.

Optical heart rate sensor with reduced power

Systems and methods may be used to map and collect data from a mesh network at a gateway device connecting a plurality of devices (e.g., edge devices, IoT devices, sensors, or the like) to a network. A method may include determining a shortest path tree (SPT) map of the plurality of devices, the shortest path tree map may define the mesh network for the plurality of devices connected to the gateway. The method may include sampling data throughout the mesh network based on a compressive sensing (CS) sampling schedule. The sampled data may be output, such as to a remote device via the network. The sampled data may be saved at the gateway.

Efficient mesh network data gathering

A semiconductor package apparatus may include technology to provide an image to a low power shallow hash network, generate a hash code from the low power shallow hash network, and identify one or more similar images based on the hash code. Other embodiments are disclosed and claimed.

Low power supervised semantic-preserving shallow hashing

Techniques disclosed for accurately predicting the occurrence of anomalous sensor readings within a sensor network and advantageously using these predictions to limit the amount of power used by relay nodes within the sensor network. Some examples analyze spatial and temporal characteristics of anomalous sensor readings to predict future occurrences. In these examples, the relay nodes operate in a reduced power mode for periods of time in which anomalous sensor readings are not predicted to occur. Also, in these examples, only relay nodes in a path between a sensor predicting an anomalous reading and a gateway of the sensor network operate in full power mode. This feature allows other relay nodes to remain in the reduced power mode even when an anomalous sensor reading is predicted elsewhere in the sensor network.

Scheduling of network relays

Techniques are provided for compressive sensing (CS) in a sensor network, for improved power efficiency. A methodology implementing the techniques according to an embodiment includes determining a state of a sensor network, based on a calculated statistic of sampled data values generated by one or more sensors in the network, and on anomaly indications generated by the one or more sensors. The method further includes calculating a CS sampling schedule based on the determined state and further based on a sparse signal recovery algorithm. The method further includes broadcasting the CS schedule to the one or more sensors. The CS schedule includes a sensor identification, sampling frequency, and sampling time offset for each sensor to be sampled. The method further includes updating the state of the sensor network and the CS schedule, based on updated data values generated by the one or more sensors in accordance with the sampling schedule.

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Bengaluru, India

Apoorv Vyas