A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis.

In this work, a lightweight compliant glove that detects scratching using data from microtubular stretchable sensors on each finger and an inertial measurement unit (IMU) on the palm through a machine learning model is presented: the SensorIsed Glove for Monitoring Atopic Dermatitis (SIGMA). SIGMA provides the user and clinicians with a quantifiable way of assaying scratch as a proxy to itch. With the quantitative information detailing scratching frequency and duration, the clinicians would be able to better classify the severity of itch and scratching caused by atopic dermatitis (AD) more objectively to optimise treatment for the patients, as opposed to the current subjective methods of assessments that are currently in use in hospitals and research settings.

A practical comparison of temporal artery thermometry and axillary thermometry in neonates under different environments.

Aim: Thermoregulatory stability and monitoring are crucial in neonatal care. However, the current standard of temperature measurement using Axillary Thermometry (AT) poses multiple limitations. Temporal Artery Thermometry (TT) is a promising new method, which thus begs the question: Can TT replace AT in neonates? Previous studies reveal conflicting results, with none involving a Southeast-Asian multi-ethnic neonatal population under different environments.