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. The validation data demonstrated an accuracy of 83% of the scratch prediction algorithm, while a separate 30 min validation trial had an accuracy of 99% in a controlled environment. In a pilot study with children (n = 6), SIGMA accurately detected 94.4% of scratching when the glove was donned. We believe that this simple device will empower dermatologists to more effectively measure and quantify itching and scratching in AD, and guide personalised treatment decisions.

Effects of Electromyographically-driven Neuromuscular Stimulatio Cycling System on the Lower-Limb of Stroke Survivors.

This paper describes the design of an Electromyographically(EMG)-driven Neuromuscular Electrical Stimulation (NMES) cycling system. It utilises real-time EMG from actively participating stroke survivors as feedback control to drive the cycling system for rehabilitation. The user controls the speed of the cycling system using muscle activities of the side affected recorded by EMG electrodes. Additionally, adaptable NMES stimulations; also EMG based, were provided in cyclic pattern to the respective muscle groups in order to improve muscle coordination. The targeted muscle groups used to control the system were the Hamstring (HS), Tibialis Anterior (TA), Quadriceps (QC), Gastrocnemius Lateralis (GL) of the leg on the affected side. Using the system, 20 30-minutes sessions were conducted with chronic stroke survivors (n=10) at frequency of 2-4 sessions per week. Clinical assessment scores, namely FMA_LE, BBS and 6MWT were calculated before the first session and after the completion of 20 sessions. All the assessment scores showed significant improvement after using the system; FMA_LE(P=0.0244), BBS(P=0.0156), 6MWT(P=0.0112), and SI (P=0.0258), showing that the EMG-driven NMES cycling system provides effective rehabilitation for stroke survivors in terms of muscle strength and balance.