Feasibility of Data-driven EMG Signal Generation using a Deep Generative Model.

Despite recent advancements in the field of pattern recognition-based myoelectric control, the collection of a high quality training set remains a challenge limiting its adoption. This paper proposes a framework for a possible solution by augmenting short training protocols with subject-specific synthetic electromyography (EMG) data generated using a deep generative network, known as SinGAN. The aim of this work is to produce high quality synthetic data that could improve classification accuracy when combined with a limited training protocol.

Automation of the Timed-Up-and-Go Test Using a Conventional Video Camera.

The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more resolved information about its sub-phases. While some approaches have shown promise, they often require the donning of sensors or the use of specialized hardware, such as the now discontinued Microsoft Kinect, which combines video information with depth sensors (RGBD).