Computer vision-inspired contrastive learning for self-supervised anomaly detection in sensor-based remote healthcare monitoring.

Sensor-based remote healthcare monitoring is a promising approach for timely detection of adverse health events such as falls or infections in people living with dementia (PLwD) in the home, and reducing preventable hospital admissions. Current anomaly detection approaches in the home setting are hindered by challenges such as noisy, multivariate data, unreliability of event annotations, and heterogeneity across home settings. Inspired by the simplicity and recent applications of contrastive learning in the field of computer vision, we propose a lightweight, self-supervised, negative sample-free approach to detect anomalous events using home activity changes in PLwD.

Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.

Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.

OrthoTV: Broadcasting Orthopedics around the World.

OrthoTV has emerged as a pioneering platform in the field of orthopedic education, leveraging technology to create a comprehensive and accessible knowledge repository. Originating from the vision of the Indian Orthopedic Research Group in 2013, OrthoTV has evolved into a global educational hub, streaming thousands of hours of surgical videos, webinars, and podcasts. It provides a dynamic learning environment through live interactive sessions, fostering real-time engagement with experts and facilitating a global exchange of knowledge.