Electromyogram

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors

10p panasonic01 12-12-2011 34 2   Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors

10p panasonic08 05-01-2012 26 2   Download

Mechatronic Experiments Course Design: A Myoelectric Controlled Partial-Hand Prosthesis Project Ton-Tai Pan, Ping-Lin Fan, Huihua Kenny Chiang, Member, IEEE, Rong-Seng Chang, and Joe-Air Jiang, Member, IEEE Abstract—This paper describes a proposed laboratory project involving the design of a myoelectric-controlled partial-hand prosthesis to reinforce mechatronic education. The proposal focuses mainly on extract electromyogram (EMG) signals generated during contraction of the biceps.

8p longmontran 14-01-2010 109 13   Download

The process of collecting and analyzing the data is critical in healthcare as it constitutes the basis for categorization of patient health problems. Data collected in medical practice ranges from free form text to structured text, numerical measurements, recorded signals, and imaging data.

352p hyperion75 22-01-2013 55 6   Download

Neuro-Fuzzy Control of a Robotic Exoskeleton With EMG Signals Kazuo Kiguchi, Member, IEEE, Takakazu Tanaka, and Toshio Fukuda, Fellow, IEEE Abstract—We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move.

10p khangoc2393 13-08-2012 66 5   Download

In numerous signal processing applications, non-stationary signals should be segmented to piece-wise stationary epochs before being further analyzed. In this article, an enhanced segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals, such as electroencephalogram (EEG), magnetoencephalogram (MEG) and electromyogram (EMG), is proposed. In the proposed approach, discrete wavelet transform (DWT) decomposes the signal into orthonormal time series with different frequency bands.

12p trinhthamhodang1 16-11-2019 10 0   Download