Electromyography in MachineLearning

Incorporating Prior Information in MachineLearning by Creating Virtual

One of the key problems in supervised learning is the insuucient size of the training set. The natural way for an intelligent learner to counter this problem and successfully generalize is to exploit prior information that may be available about the domain or that can be learned from prototypical examples. We discuss the notion of using prior knowledge by creating virtual examples and thereby e...

Beyond the knowledge level : Behavior descriptions of machinelearning

For the last fteen years the eld of machine learning has ourished and a number of complex and powerful learning systems have been developed. Knowledge level descriptions of such systems may be most useful for keeping an overview of the practical applicability of these systems. However, Dietterich's (1986) knowledge level analysis yielded very disappointing results: Learning could be characteriz...

Electromyography in Myofascial Syndrome

Myofascial syndrome is a muscular pain syndrome with regional symptoms, often in limb girdle or neck and back area. It is common and causes much disability and inability to work. Myofascial pain may be activated by precision work, repetitive strain or recent injury. Typical findings in symptomatic muscles are taut bands and painful trigger points (TrPs), where pressure elicites a typical spread...

Electromyography Fundamentals

Needle electromyography.

Physiologic assessment of diseases of the motor unit from the anterior horn cells to the muscles relies on a combination of needle electromyography (EMG) and nerve conduction studies (NCS). Both require a unique combination of knowledge of peripheral nervous system anatomy, physiology, pathophysiology, diseases, techniques, and electricity is necessary. Successful, high-quality, reproducible EM...