Identify differences in EMG signals between two functionally distinct muscles. 5. Explain how the EMG signals can be analysed (offset-correction, rectification, smoothing, and normalization of the signal across different movements and different individuals). 6. Be able to make appropriate interpretations of the EMG signals collected in the lab ...
May 25, 2021 · •Data collection for normalization of dynamometer (use ‘default’ in channel setup) from minimum contraction (nothing) to maximum (100% of effort) • Find 0 effort in mV • Find 100% effort in mV • Scale the signal from the ‘0’ mV to the ‘100’ mV Test to make sure this works - do a quick test • Calibrate the Rectified and Smoothed EMG (will do ...
Because the absolute amplitude of the signal is meaningless, one cannot evaluate the level of activity in the muscle, but only that it is more or less active in one intervention/movement compared to the other. Therefore, comparison of muscle activity levels between muscles or individuals is not valid. 3.
Electromyography (EMG) signal is an electrical signal, acquired from different organs, is usually used clinically for the diagnosis of neuromuscular problems and also in many types of research ...
Electromyography (EMG) has been around since the 1600s [1]. It is a tool used to measure the action potentials of motor units in muscles [2]. The EMG electrodes are like little microphones which “listen” for muscle action potentials so having these microphones in different locations relative to the muscle or motor units affects the nature of the recording [3]. The amplitude and frequency characteristics of the raw electromyogram signal have been shown to be highly variable and sensitive to many factors. De Luca [4] provided a detailed account of these characteristics which have a “basic” or “elemental” effect on the signal dividing them into extrinsic and intrinsic sub-factors. Extrinsic factors are those which can be influenced by the experimenter, and include: electrode configuration (distance between electrodes as well as area and shape of the electrodes); electrode placement with respect to the motor points in the muscle and lateral edge of the muscle as well as the orientation to the muscle fibres; skin preparation and impedance [5, 6]; and perspiration and temperature [7]. Intrinsic factors include: physiological, anatomical and biochemical characteristics of the muscles such as the number of active motor units; fiber type composition of the muscles; blood flow in the muscle; muscle fiber diameter; the distance between the active fibers within the muscle with respect to the electrode; and the amount of tissue between the surface of the muscle and the electrode. These factors vary between individuals, between days within an individual and within a day in an individual if the electrode set up has been altered. Given that there are many factors that influence the EMG signal, voltage recorded from a muscle is difficult to describe in terms of level if there is no reference value to which it can be compared. Therefore, interpretation of the amplitude of the raw EMG signal is problematic unless some kind of normalization procedure is performed. Normalization refers to the conversion of the signal to a scale relative to a known and repeatable value. It has been reported [8] that normalized EMG signals were first presented by Eberhart, Inman & Bresler in 1954 [9]. Since then, there have been a number of methods used to normalize EMG signals with no consensus as to which method is most
Studies use EMG to identify differences in the activation levels and patterns between normal subjects and those with neuro-musculo-skeletal dysfunction with the aim of understanding the cause of the dysfunction and developing improved rehabilitation programs to treat the dysfunction. Since the use of MVICs is the most valid method to normalize EMG data allowing comparison of activity levels between muscles in different individuals, it should be the normalization method of choice when evaluating muscle function in clinical populations provided symptomatic individuals can produce MVICs. Indeed recent studies have shown that individuals from some clinical populations (moderate knee osteoarthritis [58], following knee surgery [103], back pain [104, 105], cerebral palsy [106], stroke [45, 107]), are able to produce maximum activation levels using the same MVIC tests as healthy individuals [8]. If symptomatic individuals are unable to elicit maximal contractions, e.g. as a result of pain due to illness or injury, then comparisons between these clinical populations and normal subjects can only be made using normalization to peak or mean activation levels obtained during the task under investigation. Under these circumstances comparisons of activity levels between muscles, between tasks and between individuals are not valid. Only comparison of muscle activation patterns between normal and symptomatic individuals can be made.
An electromyogram (EMG ) signal detects the electrical potential activities generated by muscle cells.
Electromyography (EMG) signal is an electrical signal, acquired from different organs, is usually used clinically for the diagnosis of neuromuscular problems and also in many types of research laboratories, including biomechanics, motor control, neuromuscular physiology, movement disorders, postural control and physical therapy [1].
EMG is a type of pathology, location, and etiology which can be investigated using characteristicsof EMG waveforms. These techniques assist medical doctors in their diagnosis. For complicatedcases, invasive methods such as muscle biopsies or more sophisticated imaging techniques such asultrasound are preferred. Muscle contraction is the activation of tension-generating sites within themuscle fibers. A muscle fiber is excited via a motor nerve which generates an action potential thatspreads along the surface membrane (sarcolemma) and the transverse tubular system into the deeperparts of the muscle fiber.
In signal processing analysis, feature extraction plays a critical role to achieve a better performanceof classification for motion pattern recognition. This process involves the transformation of raw EMGsignals into a feature vector . Generally, features in the analysis of EMG signals can be divided into threecategories, including time domain (TD) features, frequency domain (FD) features and time-frequencydomain (TFD) features [29,43,44]. For TD features, the features are evaluated based on signal amplitudethat varies with time. The amplitude of the signal depends on muscle conditions and types during theobservation process. To keep the computational complexity low, most previous studies had focusedon TD features. In addition, these features do not require additional signal transformation. UnlikeTD features, FD features contain the power spectrum density (PSD) of the signals and are computedby parametric methods or a periodogram. On the other hand, a combination information of time andfrequency are defined as TFD features. TFD features can characterize varying frequency information atdifferent time locations, providing plentiful non-stationary information of the analyzed signals. Oskeiand Hu had illustrated the key parameters in each domain of signal analysis [45].In 1993, five TD features were proposed by Hudgins et al. [46]: mean absolute value (MAV),mean absolute value slope, slope sign changes (SSC), zero crossing (ZC) and waveform length (WL).According to Tsai et al., the time taken to extract the features set is approximately 10 ms for 200 msof sampled data collected for normal and amputee subjects during dynamic and static contractionsof the arm [29]. The ZC and SSC features in TD represent rough FD information but do not involveconverting EMG signals to FD. In the detection of hand motions, Ahsan et al. extracted EMG signalsusing MAV, ZC, SSC, root mean square (RMS), variance (VAR) and standard deviation (SD) [47].Extended work was conducted in 2013 by adding one more feature, WL, and the feature is fed asan input to the classifier. In the same year, another TD feature, namely maximum amplitude (MAX)is used along with SD and RMS to interpret EMG signals within hand-lifting three different loads.SD had the best overall performance compared to MAX and RMS [7]. Furthermore, RMS and MAXfeatures are the better ones that can be used with SD for a useful feature vector.In 2014 [48], the complexity of EMG signals of patients after stroke during 20 sessions ofrobot-aided rehabilitation training was investigated using two indexes: Fuzzy approximate entropy(fApEn) features and maximum voluntary contraction (MVC). Other TD features such as skewness(Skew) [45], Kurtosis (Kurt) and moving approximate entropy (moving ApEn) were initially employedby Ahmad and Chappel in 2009 for prosthetic hand applications. Moving ApEn effectively recognizesthe stages of contraction (e.g., start, middle, end) based on surface EMG signals of flexor carpi ulnarisand extensor carpi radials muscles [35]. The research clarified that using moving ApEn to extractfeatures in clinical processes is promising.
There is a direct relationship between EMG and many biomechanical variables. With respect to isometric contractions, there is a positive relationship between the increase of tension within the muscle and the amplitude of the EMG signal recorded. There is a lag time, however, as the EMG amplitude does not directly match the build-up of isometric tension. As a result, it is difficult to estimate force production from the EMG signal, as there is questionable validity of the relationship of force to amplitude when many muscles are crossing the same joint, or when muscles cross multiple joints.
The amplitude of EMG signals derived during gait may be interpreted as a measure of relative muscle tension. The EMG processed through a linear envelope has been widely used to compare the EMG-tension relationship, especially if the tension is changing with time. For constant tension experiments, it has been reported that the average value of the rectified EMG is a measure of tension. This can be derived from a long time constant linear envelope circuit. Both linear and non-linear relationships between EMG amplitude and tension have been reported.
The sampling rate is the frequency at which the EMG data is sampled or measured. Thus a sampling rate of 1000 Hz means that the EMG signal is measured 1000 times every second. This would mean that the theoretical maximum rate at which the EMG signal can change, and still be accurately reproduced, is 500 Hz. In practice, it is recommended that the EMG signal be sampled at least 4 to 5 times faster than the highest frequency component that is expected to be present in the signal if any signal analysis is to be performed. At a minimum, the EMG signal must be sampled at least twice as fast as the highest frequency component within the signal.
Fatigue has been found to not only reduce the muscle force, but also to alter the shape of the motor action potentials. An auto-correlation has shown that there is an increase in the average duration of the recruited MUAP. The EMG spectrum is also shown to have shifted to reflect these changes. It has been found that higher frequency components decreased with fatigue.
Mechanical artifacts are common and occur when the EMG signal cables move as the subject is in motion, as well as from any movement of the EMG sensor electrode on the skin surface. Cable artifact can generate low frequency signals as the cables shift during the subjects’ motion. This is a particular problem with passive surface electrodes if the cables to the electrode are long and are often not secured to prevent undue motion.
It is imperative that the raw EMG signal can be monitored in real-time, as it is recorded and as the electrodes are placed on the subject. Ideally, this monitoring must be performed as the EMG signal is recorded as it is often difficult to differentiate between signal and noise if any processing has been done to the EMG signal. One disadvantage of using some computerized collection systems is that many do not afford one the ability to see a raw EMG signal in real time. In this case we recommend using a separate EMG display system.
Aliasing is a sampling problem in any data acquisition system. It can cause erroneous results and occurs whenever the incoming EMG signal contains frequency components that are at, or higher, than half the analog sampling rate. If the incoming EMG is not filtered to remove these frequencies, they will show up as aliases or false lower frequency components is the recorded EMG signal that cannot be distinguished from valid sampled data. The alias signals are actually at a higher frequency, but are “folded back” by the sampling process to create false low frequency signals below half the sampling rate. This new, and completely false signal, is completely indistinguishable from a signal in the source EMG signal.
High quality EMG signals are essential for the successful execution of research applications. A number of noise sources may contaminate the recording of EMG signals and may not be easily recognized by visual inspection. These sources can distort the signal and they can lead to errors in the interpretation of the EMG signal for investigating muscle ...
Sensor Placement place the EMG sensor in the in the middle of the muscle belly, away from innervation zones and tendon origins and as far as possible from sources of physiological noise, with the electrodes aligned parallel to the muscles fibres.