Involuntary ankle oscillations from normal subjects

1977 ◽  
Vol 233 (1) ◽  
pp. R8-R14
Author(s):  
R. N. Stiles ◽  
R. R. Rietz
Keyword(s):  
Spectral Analysis ◽  
Root Mean Square ◽  
Power Function ◽  
Normal Subjects ◽  
Displacement Amplitude ◽  
Mean Square ◽  
Hand Tremor ◽  
Negative Relation ◽  
Tremor Frequency

Spectral analysis of ankle tremor records obtained from normal seated subjects during continuous elevation of the heel for 10-45 min revealed that the root-mean-square (rms) displacement amplitude of the tremor increased from minimum values of about 4 micronm to values as large as 4,000 micronm. Associated with this increase in the displacement amplitude was a systematic decrease in the tremor frequency from values of 7-8 Hz to values of 5-6 Hz. Spectral analysis of demodulated soleus EMG records indicated that the rms value of this EMG (calculated at the tremor frequency) and the rms displacement of the tremor are related by a power function, with the rms value of the EMG increasing over a range of about 4-40 micronV as the tremor displacement increased from about 4 to 4,000 micronm. The negative relation between frequency and rms displacement amplitude values for postural ankle tremor was similar to that found previously for postural hand tremor.

Frequency and displacement amplitude relations for normal hand tremor

1976 ◽  
Vol 40 (1) ◽  
pp. 44-54 ◽  
Author(s):  
R. N. Stiles
Keyword(s):  
Spectral Analysis ◽  
Small Amplitude ◽  
Continuous Extension ◽  
Normal Subjects ◽  
Displacement Amplitude ◽  
Modulation Amplitude ◽  
Mean Square ◽  
Hand Tremor ◽  
Normal Hand ◽  
Tremor Frequency

Spectral analysis of hand tremor records obtained from normal subjects during continuous extension of the hand for 15–45 min revealed that the root-mean-square (rms) displacement amplitude of the tremor increased from control levels of about 30 mum to levels on the order of 100–1,000 times control. Associated with this increase in the displacement was a systematic decrease in the hand tremor frequency from control values of 8–9 Hz to values of 4–6 Hz. Spectral analysis of demodulated extensor EMG records indicated a consistent relation between EMG modulation amplitude at the tremor frequency and the tremor displacement amplitude for tremor records with rms displacement above about 100 mum. No consistent relation was found between these two variables for tremor records with displacements below 100 mum. Consideration of both mechanical and neural reflex effects indicated that a viscoelastic-mass mechanism primarily determined the small-amplitude (less than 100 mum) tremors, while the large displacement tremors may have involved both mechanical and neural feed back factors.

A mechanical-reflex oscillator hypothesis for parkinsonian hand tremor

1976 ◽  
Vol 40 (6) ◽  
pp. 990-998 ◽  
Author(s):  
R. N. Stiles ◽  
R. S. Pozos
Keyword(s):  
Spectral Analysis ◽  
Steady State ◽  
Root Mean Square ◽  
Fold Increase ◽  
Normal Subjects ◽  
Single Frequency ◽  
Mean Square ◽  
Hand Tremor ◽  
S Period ◽  
Rms Amplitude

Spectral analysis was performed on postural hand tremor records obtained from 22 parkinsonian subjects. Of these 22 subjects, 18 had postural hand tremor that occurred primarily at a single frequency during any one 16-s period. In general, this tremor occurred at different steady-state frequencies (each calculated over 16 s) between about 4 Hz and 8–9 Hz. This frequency decreased approximately 1 Hz for each 10-fold increase in displacement amplitude (root-mean-square, rms, amplitude determined at 16 cm from the wrist), decreasing from 8–9 Hz at about 30 mum to 3.75–4.0 Hz at about 30,000 mum. The major finding was that the frequency of parkinsonian hand tremor was nearly the same as that for hand tremor from normal subjects when these frequenceis were compared at similar rms displacement levels. This comparison, plus a comparison between other aspects of these two kinds of tremor, indicate that the mechanism for parkinsonian hand tremor is similar to that for large-displacement (greater than 100 mum) hand tremor of normal subjects, i.e., a mechanical-reflex oscillator mechanism.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

Some further studies about generalised spectral norms

2021 ◽  
Vol 63 (6) ◽  
pp. 362-369
Author(s):  
K Karioja ◽  
E Juuso ◽  
J Nissilä
Keyword(s):  
Spectral Analysis ◽  
Root Mean Square ◽  
Automatic Monitoring ◽  
Vibration Monitoring ◽  
Spectral Information ◽  
Measured Signal ◽  
Mean Square ◽  
Automatic Monitoring System ◽  
Special Cases ◽  
Peak Value

Spectral analysis is a very common tool in vibration monitoring. While useful in machine diagnostics, spectral analysis can be rather time consuming. Tasks that require an extensive amount of time are often considered too expensive, especially in modern industry. To achieve an automatic monitoring system of some kind, different features, such as the root mean square (RMS) or the peak value of signals, are often monitored. These are special cases of generalised norms, which can be effective tools to determine whether a signal has shown some kind of change and how notable the change is when compared to a previously measured signal, for example. However, no spectral information is obtained in this way and the question regarding the frequency in the signal at which the change has occurred remains unanswered. Generalised spectral norms are a frequency-domain application of these norms and, as presented in this paper, provide a suitable way to perform automatic monitoring regarding the spectral information, for example.

Quantification of Silymarin in Silybi mariani fructus: Challenging the Analytical Performance of Benchtop vs. Handheld NIR Spectrometers on Whole Seeds

Planta Medica ◽  
2021 ◽  
Author(s):  
Sophia Mayr ◽  
Simon Strasser ◽  
Christian G. Kirchler ◽  
Florian Meischl ◽  
Stefan Stuppner ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Performance ◽  
Milk Thistle ◽  
Mean Square

AbstractThe content of the flavonolignan mixture silymarin and its individual components (silichristin, silidianin, silibinin A, silibinin B, isosilibinin A, and isosilibinin B) in whole and milled milk thistle seeds (Silybi mariani fructus) was analyzed with near-infrared spectroscopy. The analytical performance of one benchtop and two handheld near-infrared spectrometers was compared. Reference analysis was performed with HPLC following a Soxhlet extraction (European Pharmacopoeia) and a more resource-efficient ultrasonic extraction. The reliability of near-infrared spectral analysis determined through partial least squares regression models constructed independently for the spectral datasets obtained by the three spectrometers was as follows. The benchtop device NIRFlex N-500 performed the best both for milled and whole seeds with a root mean square error of CV between 0.01 and 0.17%. The handheld spectrometer MicroNIR 2200 as well as the microPHAZIR provided a similar performance (root mean square error of CV between 0.01 and 0.18% and between 0.01 and 0.23%, respectively). We carried out quantum chemical simulation of near-infrared spectra of silichristin, silidianin, silibinin, and isosilibinin for interpretation of the results of spectral analysis. This provided understanding of the absorption regions meaningful for the calibration. Further, it helped to better separate how the chemical and physical properties of the samples affect the analysis. While the study demonstrated that milling of samples slightly improves the performance, it was deemed to be critical only for the analysis carried out with the microPHAZIR. This study evidenced that rapid and nondestructive quantification of silymarin and individual flavonolignans is possible with miniaturized near-infrared spectroscopy in whole milk thistle seeds.

scholarly journals Experimental Analysis of Vertical Vibrations of a Railway Bogie

2021 ◽  
Vol 23 (4) ◽  
pp. B299-B307
Author(s):  
Mădălina Dumitriu ◽  
Ioan Cristian Cruceanu
Keyword(s):  
Spectral Analysis ◽  
Root Mean Square ◽  
Experimental Analysis ◽  
Constant Velocity ◽  
Mean Square ◽  
Passenger Vehicle ◽  
Railway Bogie ◽  
Vertical Vibrations

The paper presents a study of the vertical vibrations of a bogie of a passenger vehicle, based on the acceleration of the axles and the bogie frames, measured during the running at a constant velocity. To this purpose, the root mean square (RMS) acceleration is calculated for more measurement sequences at different velocities. In principle, the RMS acceleration increases along with the velocity and influence of the variability of the amplitude in the track defects upon the dispersion of the values in the RMS acceleration. Based on the spectral analysis of the measured acceleration, wheel defects and undulatory wear of the rolling surfaces of wheels and rail are highlighted.

scholarly journals Approach to COVID-19 time series data using deep learning and spectral analysis methods

2021 ◽  
Vol 9 (1) ◽  
pp. 1-21
Author(s):  
Kayode Oshinubi ◽  
◽  
Augustina Amakor ◽  
Olumuyiwa James Peter ◽  
Mustapha Rachdi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Spectral Analysis ◽  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Time Series Data ◽  
Series Data ◽  
Mean Square

<abstract> <p>This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.</p> </abstract>

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