EXPERIMENT 10
This was research based experiment wherein we studied an application of dsp processor.We formed a group of 5 students. The members were Nikita Pagar, Golappagouda Patil, Anushree Mhatre, Apoorva Raut and Sahil Rai. The topic we selected was “Detection and Processing of Electromyography signals”.
the paper name
EMG Pattern Recognition Based on
Artificial Intelligence Techniques
Artificial Intelligence Techniques
the patent
RECOGNIZING FINGER GESTURES FROM
FOREARM EMG SIGNALS
FOREARM EMG SIGNALS
Pub. No.: US 2013/0232095 A1
Applicant: MICROSOFT CORPORATION
Inventors: Desney Tan, Kirkland,
Morris, Bellevue, T. Scott
Saponas, Seattle, Ravin
Inventors: Desney Tan, Kirkland,
Morris, Bellevue, T. Scott
Saponas, Seattle, Ravin
This paper presents an electromyographic (EMG)
pattern recognition method to identify motion commands for
the control of a prosthetic arm by evidence accumulation based
on artificial intelligence with multiple parameters. The integral
absolute value, variance, autoregressive (AR) model coefficients,
linear cepstrum coefficients, and adaptive cepstrum vector are
extracted as feature parameters from several time segments of
EMG signals.
the control of a prosthetic arm by evidence accumulation based
on artificial intelligence with multiple parameters. The integral
absolute value, variance, autoregressive (AR) model coefficients,
linear cepstrum coefficients, and adaptive cepstrum vector are
extracted as feature parameters from several time segments of
EMG signals.
A machine learning model is trained by instructing a user to
perform Various prede?ned gestures, sampling signals from
EMG sensors arranged arbitrarily on the user’s forearm With
respect to locations of muscles in the forearm, extracting
(Us) feature samples from the sampled signals, labeling the feature
samples according to the corresponding gestures instructed to
be performed, and training the machine learning model With
the labeled feature samples
perform Various prede?ned gestures, sampling signals from
EMG sensors arranged arbitrarily on the user’s forearm With
respect to locations of muscles in the forearm, extracting
(Us) feature samples from the sampled signals, labeling the feature
samples according to the corresponding gestures instructed to
be performed, and training the machine learning model With
the labeled feature samples
Interesting would like to know more about it
ReplyDeleteYour topic is interesting. :)
ReplyDeleteIt is really intresenting as using this we can apply it in many medical applications like neuromuscular diseases, to detect medical abnormalities,etc.
ReplyDeleteOne of the applications of electromyography is designing prosthetics and wearable devices
ReplyDelete