scholarly journals Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study

2020 ◽  
Vol 65 (4) ◽  
pp. 461-468
Author(s):  
Jannatul Naeem ◽  
Nur Azah Hamzaid ◽  
Amelia Wong Azman ◽  
Manfred Bijak
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Isometric Force ◽  
Mean Square ◽  
Clear Indication ◽  
Initial Value ◽  
Cord Injury ◽  
The Mean ◽  
Electrical Stimulator ◽  
Fatigue Detection

AbstractFunctional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.

NOTE ON THE VLASOV EQUATION FOR PLASMAS

1963 ◽  
Vol 41 (12) ◽  
pp. 1960-1966 ◽  
Author(s):  
Ta-You Wu ◽  
M. K. Sundaresan
Keyword(s):  
Distribution Function ◽  
Fourier Transform ◽  
Vlasov Equation ◽  
Hermite Polynomials ◽  
Infinite Matrix ◽  
Mean Square ◽  
Initial Value ◽  
Positive Ions ◽  
The Mean ◽  
The Fourier Transform

The linearized Vlasov equation is solved as an initial value problem by expanding (the Fourier components of) the distribution function in a series of Hermite polynomials in the momentum, with coefficients which are functions of time. The spectrum of frequencies is given by the eigenvalues of an infinite matrix. All the frequencies ω are real, extending from small values of order ω2 = k2(u22), where (u22) is the mean square velocity of the positive ions (of mass M), to [Formula: see text], where ω1, (u12) are the plasma frequency and mean square velocity of the electrons (of mass m). The classic work of Landau solves the Vlasov equation for (the Fourier transform of) the potential for which he obtains the "damping", whereas Van Kampen and the present writers solve the equation for (the Fourier transform of) the distribution function itself. While the present work gives results equivalent to those of Van Kampen, the method is simpler and in fact elementary.

scholarly journals Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

2019 ◽  
Vol 57 (6) ◽  
pp. 1199-1211 ◽  
Author(s):  
Jannatul Naeem ◽  
Nur Azah Hamzaid ◽  
Md. Anamul Islam ◽  
Amelia Wong Azman ◽  
Manfred Bijak
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Muscle Fatigue ◽  
Cord Injury ◽  
Fatigue Detection

scholarly journals Muscle fatigue detection and treatment system driven by internet of things

2019 ◽  
Vol 19 (S7) ◽  
Author(s):  
Bin Ma ◽  
Chunxiao Li ◽  
Zhaolong Wu ◽  
Yulong Huang ◽  
Ada Chaeli van der Zijp-Tan ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Internet Of Things ◽  
Response Time ◽  
Muscle Fatigue ◽  
Real Time ◽  
Pulse Width Modulation ◽  
Health Related ◽  
Consistent Change ◽  
High Utility ◽  
Fatigue Detection

Abstract Background Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. Methods To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. Results With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. Discussion Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. Conclusions Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

scholarly journals An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3108 ◽  
Author(s):  
Shing-Hong Liu ◽  
Chuan-Bi Lin ◽  
Ying Chen ◽  
Wenxi Chen ◽  
Tai-Shen Huang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Root Mean Square ◽  
The Body ◽  
Median Frequency ◽  
Mean Square ◽  
Sport Injuries ◽  
Physiological Measurement ◽  
The Real ◽  
Emg Signal

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

Quadriceps mechanomyography reflects muscle fatigue during electrical stimulus-sustained standing in adults with spinal cord injury – a proof of concept

2020 ◽  
Vol 65 (2) ◽  
pp. 165-174
Author(s):  
Morufu Olusola Ibitoye ◽  
Nur Azah Hamzaid ◽  
Ahmad Khairi Abdul Wahab ◽  
Nazirah Hasnan ◽  
Glen M. Davis
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Muscle Fatigue ◽  
Root Mean Square ◽  
Low Frequency ◽  
Motor Units ◽  
Electrical Stimulus ◽  
Mechanical Activity ◽  
Mean Square ◽  
Cord Injury

AbstractThis study investigates whether mechanomyography (MMG) produced from contracting muscles as a measure of their performance could be a proxy of muscle fatigue during a sustained functional electrical stimulation (FES)-supported standing-to-failure task. Bilateral FES-evoked contractions of quadriceps and glutei muscles, of four adults with motor-complete spinal cord injury (SCI), were used to maintain upright stance using two different FES frequencies: high frequency (HF – 35 Hz) and low frequency (LF – 20 Hz). The time at 30° knee angle reduction was taken as the point of critical “fatigue failure”, while the generated MMG characteristics were used to track the pattern of force development during stance. Quadriceps fatigue, which was primarily responsible for the knee buckle, was characterized using MMG-root mean square (RMS) amplitude. A double exponential decay model fitted the MMG fatigue data with good accuracy [R2 = 0.85–0.99; root mean square error (RMSE) = 2.12–8.10] implying changes in the mechanical activity performance of the muscle’s motor units. Although the standing duration was generally longer for the LF strategy (31–246 s), except in one participant, when compared to the HF strategy, such differences were not significant (p > 0.05) but suggested a faster muscle fatigue onset during HF stimulation. As MMG could discriminate between different stimulation frequencies, we speculate that this signal can quantify muscle fatigue characteristics during prolonged FES applications.

scholarly journals State Vector Identification of Hybrid Model of a Gas Turbine by Real-Time Kalman Filter

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 659
Author(s):  
Gustavo Delgado-Reyes ◽  
Pedro Guevara-Lopez ◽  
Igor Loboda ◽  
Leobardo Hernandez-Gonzalez ◽  
Jazmin Ramirez-Hernandez ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Gas Turbine ◽  
State Vector ◽  
Convergence Time ◽  
Mean Square ◽  
Real Time Simulation ◽  
Time Simulation ◽  
The Mean ◽  
Mean Square Errors

A model and real-time simulation of a gas turbine engine (GTE) by real-time tasks (RTT) is presented. A Kalman filter is applied to perform the state vector identification of the GTE model. The obtained algorithms are recursive and multivariable; for this reason, ANSI C libraries have been developed for (a) use of matrices and vectors, (b) dynamic memory management, (c) simulation of state-space systems, (d) approximation of systems using equations in matrix finite difference, (e) computing the mean square errors vector, and (f) state vector identification of dynamic systems through digital Kalman filter. Simulations were performed in a Single Board Computer (SBC) Raspberry Pi 2® with a real-time operating system. Execution times have been measured to justify the real-time simulation. To validate the results, multiple time plots are analyzed to verify the quality and convergence time of the mean square error obtained.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

The mean-square generalized delayed decision feedback sequence detector

2003 ◽  
Vol 14 (3) ◽  
pp. 265-268 ◽  
Author(s):  
Maurizio Magarini ◽  
Arnaldo Spalvieri ◽  
Guido Tartara
Keyword(s):  
Decision Feedback ◽  
Mean Square ◽  
The Mean

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

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