City University of Hong Kong

Leanne Lai Hang Chan

Hong Yan

Hung Chim

Ray Chak Chung Cheung

Hong Yan Ching

Stephen Kugbere Agadagba

Mehdi Hasan Chowdhury

Chi Kuen Choi

Abdelrahman Bakr Mohammed Abdelnaby Eldaly

The present invention provides a system for implementing a logistic regression classification mechanism to measure and assess a depth of anesthesia of an animal subject based on electroencephalography (EEG), which includes a signal pre-processor, an epoch generator, a feature extractor, a classifier, and a predictor. Related method of how to pre-process the raw data of EEG signal, epoch generation thereof, feature extraction from each epoch, classification based on extracted features, and prediction of different states of the animal subject based on a prediction decision mechanism is also provided. Classification accuracy of the present invention for 1-second and 10% overlapping epochs is about 94% with an average total system delay of about 12 μs and low on-chip power consumption. The present system is entirely optimized, which leads to a 100% accurate channel prediction after a 7-second run-time on average.

System and method for measurement and assessment of depth of anesthesia in an animal subject based on electroencephalography

Facilitation of monitoring and analysis of user motion is performed to facilitate detecting when a user has fallen. A device can comprise a housing configured to attach to a body part of a human, a sensor module configured to capture motion data corresponding to motion of an object to which the device is attached, and a communication module configured to communicatively couple the sensor module to another device and transmit the motion data to the other device, wherein the motion data is configured to be analyzed by the other device to determine a type of the motion of the object, and in response to a determination that the type of motion is a falling motion, the other device is configured to activate a notification mechanism to notify an entity that the human has fallen.

Monitoring user activity using wearable motion sensing device

Growing community of inventors

Hong Kong, China

Leanne Lai Hang Chan