Name of the Technology :     Prosthetic Hand  
Disability Sector :                  Locomotor Disability
Function :                               Mobility/ Holding aids
Category of research :           Upgradation of existing technology/product
Brief description of the product/ technology:

Prototype for writing and gripping of objects controlled by EMG pattern recognition techniques. The existing type of prosthesis is body powered or myoelectric. The pattern recognition technique will improve the time of training an amputee to adapt with the usage with less effort for activation. The surface EMG signal is obtained from electrode site for finger flexion and wrist up action from health subjects. The entire system was a PC based one. Sufficient data sets were collected, pre processed using signal processing techniques. Feature extraction procedure is adopted to create database to train artificial neural network to recognize the patterns. The output of the neural network was fed as input to embedded processor to control/drive prosthetics to perform action like griping and drawing vertical and horizontal segments. Presently the work is focused towards deployment of pattern recognition algorithm into DSP boards.
With modernized technology and industrial partner, the product will take good shape.

Readiness level of the product/technology: Proof of Concept (alpha level)
Anticipated time to reach the next readiness level:                                                More than 12 months 
Anticipated time for the technology/product to come in the market:                    More than two years 
Any Hindrance(s) being faced w.r.t. the development of product/technology:      Lack of Funds

Anticipated cost:          
Contact Details:           Dr.C.Lakshmi Deepika-Associate Professor & Mr.E.Sreedhar Kumar, Senior Manager,
                                     PSG TIFAC - CORE in Product Design, PSG College of Technology
                                     Department of Bio- Medical Engineering, PSG College of Technology
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