Fuzzy Relevance Vector Machines with Application to Surface Electromyographic Signal Classification

2014 ◽  
pp. 161-176
Author(s):  
Hong-Bo Xie ◽  
Hu Huang ◽  
Socrates Dokos
Keyword(s):  
Signal Classification ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Electromyographic Signal ◽  
Surface Electromyographic Signal

Myoelectric Control: an alternative to Mirror Therapy

2021 ◽  
Vol 65 (1) ◽  
pp. 948-950
Author(s):  
Kouadio Niamba ◽  
Frank Schieber ◽  
Megan McCray
Keyword(s):  
Phantom Limb Pain ◽  
Signal Classification ◽  
Phantom Limb ◽  
Mirror Therapy ◽  
Limb Pain ◽  
Immersive Virtual Environment ◽  
Physiological Phenomenon ◽  
Electromyographic Signal ◽  
Surface Electromyographic Signal ◽  
Virtual Therapy

Evidence suggests that fifty to eighty percent (50-80%) of amputees conserve sensation in their missing limb after removal due to the presence of associated nerve endings. Most importantly, a large percentage of amputees experience episodic pain in the missing limb. This physiological phenomenon called phantom limb pain (PLP) has shown resistance to pharmaceutical treatments, but can be treated through mirror therapy. However, mirror therapy only yields temporary results and does not apply to bilateral amputees. Overcoming these challenges are the objectives of the present study. Using a surface electromyographic signal classification approach, this investigation intends to simulate the control of a missing limb within an immersive virtual environment. We predict that replacing mirror therapy with a more immersive “virtual therapy” can serve as a prolonged psychological solution to phantom limb pain.

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