Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 11

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
Wyszukiwano:
w słowach kluczowych:  sygnał EMG
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
PL
W artykule opisane zostały wybrane zagadnienia związane z identyfikacją artefaktów EKG zarejestrowanych wraz z sygnałem bioelektrycznym z mięśni człowieka. Napisany został program do przetwarzania wyników pomiarów. Program wykorzystuje dane zapisane w pliku tekstowym. Algorytm aplikacji realizuje identyfikację składowych zespołu QRS. Wykonywane są operacje uśredniania sygnału w odpowiednio dobranych oknach czasowych. Na przykładzie wybranych plików z wynikami pomiarów przeprowadzone zostały testy działania algorytmu identyfikacji artefaktów i napisanej w środowisku Matlab aplikacji.
EN
In the paper, the selected problems that concern the identification of ECG artifacts recorded in a bioelectrical signal of human muscles. A special program has been written to process the measurement results. The program uses the data that have been saved in a text file. The presented algorithm detects interferences related to the identification of the QRS component. Identification of the QRS components was performed by the algorithm. Signal averaging operations are realized in properly selected time windows. Using the selected files concerning the results, operation tests on the algorithm and Matlab application were realized.
PL
Jednym z czynników mających istotny wpływ na niezawodność sterowania wielofunkcyjną protezą dłoni jest proces akwizycji biosygnałów. W pracy omówiono naturę sygnałów EMG i MMG oraz zakłócenia towarzyszące ich rejestracji. Opisano opracowany na podstawie tych przesłanek system pomiarowy, a także przebieg procedury pomiaru.
EN
The process of biosignal acquisition has a significant impact on the reliability of the control of the multi-functional hand prosthesis. The paper discusses the nature of EMG and MMG signals and the noise associated with their registration. The measuring system developed on the basis of these premises, as well as the measurement procedure were described.
EN
The analysis of electromyographic signals can be very time consuming. In designing a program for EMG signal analysis, there are two competing factors: the accuracy of the final result and its speed. In scientific work, accuracy is the most important factor. All of the existing decomposition programs used in neurophysiology require a final phase of manual corrections, if reliable results are to be obtained. This phase is considerably longer than the phase of automatic recognition. The solutions presented below, used in our new MUR program, allow for the accurate decomposition of complex EMG signals in a reasonable amount of time. The decomposition is performed interactively with optimal time division between automatic and manual tasks. All of this is achieved through a simple method of automatic recognition with the use of the modified coefficient of determination and the method of multiple subtractions of potentials.
EN
Background: In recent years, as a result of the usage of electronic gadgets in vehicles, driver inattention has become one of the major causes of road accidents that lead to severe physical injuries, deaths and significant economic losses. Statistics ensure the need of a reliable driver inattention detection system that can alert the driver before a mishap happens. Methods: In this work, we aimed to develop a system that can detect inattention using electrocardiogram (ECG) and surface electromyogram (sEMG) signals. Cognitive and visual inattention was manipulated by asking the driver to respond to phone calls and short messaging services, respectively. A total of 15 male subjects participated in the data collection process. The subjects were asked to drive for two hours in a simulated environment at three different times of the day. ECG, sEMG and video were obtained throughout the experiment. The gathered physiological signals were preprocessed to remove noises and artefacts. The inattention features were extracted from the preprocessed signals using conventional statistical, higher-order statistical and higher-order spectral features. The features were classified using k-nearest neighbour analysis, linear discriminant analysis and quadratic discriminant analysis. Results: The bispectral features gave overall maximum accuracies of 98.12% and 90.97% for the ECG and EMG signals, respectively. Conclusion: We conclude that ECG and EMG signals can be explored further to develop a robust and reliable inattention detection system.
EN
Power-line interference is always a problem when biopotential signals are recorded. This paper presents a technique for time-efficient power-line interference suppression from EMG signals using digital IIR (Infinite Impulse Response) notch filters with reduced transient response. The reduction of the transient response is obtained by finding optimal non-zero initial conditions for the considered notch filters. Simulations verifying the effectiveness of the proposed technique are presented and compared with the performance of the traditional notch filters with zero initial conditions using EMG signal with unwanted sinusoidal interferences as a study case.
6
Content available remote Stanowisko do oceny zmęczenia mięśnia
PL
W pracy przedstawiono stanowisko do badań dynamicznych aktywności mięśnia. Zaproponowano rozwiązanie, w którym istnieje możliwość pomiaru wybranych grup mięśniowych w czasie ruchu. Ze względu na fakt, że ból mięśni może być spowodowany poprzez niewłaściwe ułożenie ciała, jak również niekontrolowane napięcie mięśni, zaproponowano rozwiązanie, w którym istnieje możliwość kontroli i automatycznej reakcji systemu na podobne zdarzenia. Przedstawiono system pomiarowy składający się ze wzmacniacza sygnału, układu filtracji oraz układu mikroprocesorowego, umożliwiającego przesyłanie danych do jednostki nadrzędnej, np. komputera. Stanowisko pozwala na niezależny pomiar dla trzech mięśni, a dane przekazywane do komputera są archiwizowane i analizowane.
EN
The article describes a technical concept of device to research dynamic activity of a muscle. The proposed solution anticipated the possibility of taking measurements of selected muscle groups in motion. Muscle pain can be caused by improper position of body, as well as uncontrolled muscle tension. However, there is a solution allowing for control and automatic reaction to similar occurrences. The system of measurement, which is presented, consists of signal amplifier, active filter and microprocessor with AD converter which enables to send data to recording. The device enables the independent measurement of three muscles and data transmitted to the computer is archived and analyzed simultaneously.
7
PL
Celem przeprowadzonych badań było wykazanie użyteczności sygnału EMG (elektromiograficznego) w teorii sterowania. Przeprowadzono badanie stwierdzające powiązanie pomiędzy aktywnością elektryczną mięśni (biceps i triceps brachii) a siłą mięśniową w warunkach statycznych. Opracowano algorytm obróbki danych elektromiograficznych. Wykazano liniową zależność pomiędzy omawianymi wielkościami fizycznymi jednocześnie potwierdzając użyteczność sygnału EMG jako sygnału sterującego. Jednakże zaleca się poszerzenie przeprowadzonych badań o badania dynamiczne skurczu mięśnia.
EN
The aim of the study was to perform sEMG (surfacial electromyography) signal analysis focusing on its applicability for control purposes. Research that aimed at determining the relation between electromuscular activity and static muscular force was conducted. Algorithm for processing of the obtained data was prepared. Linear dependence of the aforementioned quantities was established, therefore the convenience of usage of sEMG as a control signal was confirmed. However, further research on the dynamics of muscular contraction is necessary.
EN
In this paper the amplifier to measure electromyographic (EMG) signals was developed. The device to recognize EMG signals was built with the use precision instrumentation amplifier INA122 made by BURR-BROWN Corporation. First, the solution was identified and the gain characteristics prepared. Next, the device was tested by measuring of EMG signals on biceps brachii muscle. Finally, the method of flex muscle identification was proposed.
PL
Praca przedstawia problem sterowania decyzyjnego bioprotezą dłoni, traktowany jako rozpoznawanie intencji ruchowych człowieka na drodze analizy miosygnałów. Ze względu na dużą liczbę klas ruchu oraz wymaganą, wysoką niezawodność rozpoznawania tych klas prezentowane podejście polega na łącznym wykorzystaniu takich metod jak: drzewa decyzyjne, sieci neuronowe oraz algorytmy genetyczne dla uzyskania poprawy niezawodności rozpoznawania.
EN
The paper discusses the problem of human intention recognition by means of the electromyography (EMG) signals analysis. The signal characteristics and the large number of movement classis of a dexterous hand together with the high reliability of their recognition thatis demanded make all this problem all the more difficult. The presented approach consist in combining such technics as Decision Tree, Neural Networks and Genetic Algorithms to obtain the reliable recognition.
10
Content available remote Finger curvature movement recognition interface technique using SEMG signals
EN
Purpose: Until recently, keyboard has been used as the primary input method for machinery operation system. But in recent years, numerous methods related to direct input interface have been developed. One of them is to measure the surface electric potential that generates on the skin surface during muscle contraction. Based of this fact, hand finger operation can also be recognized with the help of the surface muscle electric potential. The purpose of this study is to identify the hand finger operation using surface electromyogram (SEMG) during crookedness state of the finger. Design/methodology/approach: Two electrodes (Ag-AgCl electrode) were sticked randomly on the forearm muscles and the intensity of EMG signals at different muscles were measured for each crooked finger. Then depending on the intensity of the obtained electric potentials, a position was located and considered to have participated most actively during the crookedness state of that finger. Thus five locations on the forearm muscles were identified for five different fingers. Moreover, four different types of crookedness states were considered for each finger. Findings: In this experimental study, the electric current that generates on the skin during muscle activity was measured for different hand finger operations. As a result, it is found that there is a specified position related the maximum intensity of EMG signals for each finger. Practical implications: This paper cleared that the amount of crookedness of each finger can also be recognized with the help of surface EMG. It could be used as a machine interface technology in the field of welfare equipments, robot hand operation, virtual reality, etc. Originality/value: The objective of this research project was to develop the method of recognizing the hand finger operation and their crookedness states from surface electromyogram (SEMG).
11
Content available remote Time-frequency analysis of EMG signals
EN
The results obtained by means of well known short-time Fourier transform is compared with the new approach, the wavelet transform. The influence of muscle fatiguing contractions on the myoelectric signal is examined. The results of experimental investigations are shown on figures.
PL
W artykule porównano wyniki uzyskane przy pomocy tradycyjnej krótkookresowej transformaty Fouriera z rezultatami analizy otrzymanymi dzięki transformacie falkowej. Badano wpływ zmęczenia mięśni w wyniku skurczów na elektromiogram (sygnał EMG). Otrzymane wyniki badań eksperymentalnych zamieszczono na rysunkach.
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.