Isometric Finger Pose Recognition with Sparse Channel SpatioTemporal EMG Imaging

© 2018 IEEE. High fidelity myoelectric control of prostheses and orthoses isparamount to restoring lost function to amputees and neuro-muscular disease sufferers. In this study we prove that patio-temporal imaging can be used to allow convolutional neural networks to classify sparse channel EMG samples from a consumer-grade device with over 94% accuracy. 10,572 images are generated from 960 samples of simple and complex isometric finger poses recorded from 4 fully intact subjects. Real-time classification of 12 poses is achieved with a 250ms continuous overlapping window

A pilot study of sound levels in an Australian adult general intensive care unit

High technology and activity levels in the intensive care unit (ICU) lead to elevated and disturbing sound levels. As noise has been shown to affect the ability of patients to rest and sleep, continuous sound levels are required during sleep investigations. The aim of this pilot study was to develop a robust protocol to measure continuous sound levels for a larger more substantive future study to improve sleep for the ICU patient. A review of published studies of sound levels in intensive care settings revealed sufficient information to develop a study protocol. The study protocol resulted in 10 usable recordings out of 11 attempts to collect pilot data. The mean recording time was 17.49 ± 4.5 h. Sound levels exceeded recommendations made by the World Health Organization (WHO) for hospitals. The mean equivalent sound level (LAeq) was 56.22 ± 1.65 dB and LA90 was 46.8 ± 2.46 dB. The data reveal the requirement for a noise reduction program within this ICU