https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 Development of a deep neural network for automated electromyographic pattern classification https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35130 98%) compared with supervised ANNs. AlexNet demonstrated the highest accuracy (99.55%) with negligible false classifications. The results indicate that sEMG quality evaluation can be automated via an ANN without compromising human-like classification accuracy. This classifier will be publicly available and will be a valuable tool for researchers and clinicians using electromyography.]]> Thu 02 Apr 2020 15:47:43 AEDT ]]> Reliability of telemetric electromyography and near-infrared spectroscopy during high-intensity resistance exercise https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:20916 Sat 24 Mar 2018 08:06:12 AEDT ]]> Experimental pain in the groin may refer into the lower abdomen: implications to clinical assessments https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:30606 Sat 24 Mar 2018 07:28:28 AEDT ]]>