This is to certify that the thesis work submitted by Farzana Khanam entitled Determination of Muscle Activity and Work-Done through EMG Analysis during Human Movements in Salat has been approved by the board of examiners for partial fulfillment of the requirements for the degree M. Since surface electromyography (sEMG) is a proven method to observe the muscle activity based on the generated action potential by the muscles, sEMG method is applied to measure the physical activities of different muscles of the different stages of Salat. Salat is the Arabic word for prayers offered by Muslim worshippers, and it is the second pillar of the Islamic faith.
EMG is an electrodiagnostic medical technique for assessing and recording the electrical activity produced by skeletal muscles, allowing the measurement of the change in membrane potential transmitted along the fiber [3]. In addition, EMG is an important guide for investigating the physiological signals of participants. An investigation of upper body EMG activity during Takbirul ihram in salat for the posterior scapula (SC), pectoralis major (PM), biceps brachii (BB) and upper trapezius (UT) was conducted in [8] and concluded in that the effect of the Salat posture and movement is as good as a stretching exercise, because all the upper muscles are activated.
To confirm the conflicting results of MNF and MDF properties, modification of frequency domain properties of the universal indices that can detect both muscle force and muscle fatigue is implemented. To overcome the drawbacks of the above results, it is necessary to develop a mathematical model to estimate the total work done from an EMG signal. To our knowledge, there is no exact technique to estimate the mathematical relationship between work done and EMG value.
A flow diagram is shown in Figure 2.1 showing the physical movement steps with the approximate time of a race.
Analytical Methodology .1 Mathematical Analysis
EMG is an experimental technique that deals with the development, recording and analysis of myoelectric signals. By means of EMG analysis, any form of violence or work performed on the human body can be evaluated. To achieve our goal; we followed a consistent course of action throughout the experiment.
All recorded EMG data were post-processed using the built-in processing tool in the Acqknowledge software, where the RMS values were obtained. In the RMS calculation, the raw signals are rectified and then converted into an amplitude envelope. RMS envelope values of all bursts for each loading period (two raqat Salat) are calculated.
EMG envelope calculation procedure consists of two steps: (i) EMG signal is segmented with W data point samples (1 window) to approximate the raw data with reduced samples and (ii) Square root of the square of the downsampled signal is calculated, which is shown in (3.1). Here, pi, W, Xraw, and XENV represent the averaged EMG data, 15 sampled raw EMG window, raw EMG data, and RMS envelope of the EMG signal, respectively. To get integrated EMG signal, we consider each separate signal as a function of f of given mean EMG data p,.
The average corrected value (ARV) of the EMG is defined as a time window average of the absolute value of the signal. ARV is one of various processing methods used to construct dense signals from raw EMG data [36] that can be useful for further analysis.
- Signal Acquisition
- Experimental Procedure .1 Preprocessing
- Signal Rectification
- Experimental Results and Discussion
- Conclusion
- Proposed Method of Study .1 Mathematical Investigation
- Experimental Procedure and Apparatus
- Data Acquisition
- Experimental Consequences ad Discussions
- Conclusion
- Motivation
- Proposed Methodology
- Physical Procedure of the Method
- Work Done Calculation
- Formulation to Relate Work Done and EMG Potentials
- Procedure of Acquiring EMG Signal
- Experimental Procedure 14 .1 Data Acquisition Method
- Subject Selection and Laboratory Specification
- Apparatus Specification
- Experimental Outcomes and Discussions
- ResuLts and Discussions for Upper Limb
- Results and Discussions for Lower Limb
- Conclusions
For the study, the EMG signals were captured from three different muscles, which are Biceps Brachii (BB), Erector Spine (ES) and Gastrocnemius Medialis (GM) in the body. Integrated values of EMG signals are also acquired during the entire experiment. RMS values of the raw EMG data and the area covering this data are also determined.
The Fourier transform of the autocorrelation function of the EMG signal is used to provide the power spectrum (PS) or power spectral density (PSD). MNF is the mean frequency, which is calculated as the sum of the product of the EMG power spectrum and the frequency divided by the total sum of the power spectrum. Sampled window (b) . Figure 4.8: a) FB-MNF and b) FB-MDF of the Erector Spinae muscle during Salat with a window size of 768 samples for different male subjects.
Sample window (b) . Figure 4.9: a) FB-MNF and b) FB-MDF of the Erector Spinae muscle during Prayer with size 768- window sample for different female subjects. Therefore, it is important to analyze the relationships between human upper and lower limb muscle activities and EMG signals to perform lower limb movement power assistance. The authors in [4] and [50] investigated the EMG activity of lower limb muscles such as medial gastrocnemius, lateral gastrocnemius muscle, etc.
By placing the values of a, b and c in (5.3), we can obtain the equation of the parabola for the best fit, as well as a non-linear relationship between work done and EMG results for the muscles of the upper and lower limbs. For lower limb experiments, electrodes were attached to the right leg over the belly of the Biceps Femoris (BF) and Medial Gastrocnemius (MG). In these tables, the mean and standard deviation of the data are also determined and tabulated.
In order to achieve the greater accuracy of the measurement result, a non-linear curve fitting is performed on the measured data. From (5.7) we have calculated the stress level for the given work done, and their result and error are given in table 5.6. The result of the study is given in table 5.7, where it is found that there is a very low level of error between our proposed mathematical model and the measured result.
As a result, we successfully obtain strong evidence of the accuracy of our proposed hypothesis on the relationship between work done and cumulative action potential (EMG voltage). To describe the mathematical relationship between work done and EMG potential value of BF and MG muscles, we need to achieve more accuracy of the measured results and a non-linear curve fitting is performed on the measured data. According to our proposed mathematical model, we calculated the EMG potential for each of the weight-related work done.
Salahuddin, “Electromyographic activity of lower limb muscles during salat and specific exercises,” Journal of I'hysical Therapy Science, vol.
