scholarly journals A novel time-based surface EMG measure for quantifying hypertonia in paretic arm muscles during daily activities after hemiparetic stroke

2022 ◽  
Author(s):  
M. Hongchul Sohn ◽  
Jasjit Deol ◽  
Julius P. A. Dewald
Keyword(s):  
Muscle Tone ◽  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Quantitative Measure ◽  
Surface Emg ◽  
Daily Basis ◽  
Noise Sources ◽  
Time Segment ◽  
Arm Muscles ◽  
Hemiparetic Stroke

After stroke, paretic arm muscles are constantly exposed to abnormal neural drive from the injured brain. As such, hypertonia, broadly defined as an increase in muscle tone, is prevalent especially in distal muscles, which impairs daily function or in long-term leads to a flexed resting posture in the wrist and fingers. However, there currently is no quantitative measure that can reliably track how hypertonia is expressed on daily basis. In this study, we propose a novel time-based surface electromyography (sEMG) measure that can overcome the limitations of the coarse clinical scales often measured in functionally irrelevant context and the magnitude-based sEMG measures that suffer from signal non-stationarity. We postulated that the key to robust quantification of hypertonia is to capture the true baseline in sEMG for each measurement session, by which we can define the relative duration of activity over a short time segment continuously tracked in a sliding window fashion. We validate that the proposed measure of sEMG active duration is robust across parameter choices (e.g., sampling rate, window length, threshold criteria), robust against typical noise sources present in paretic muscles (e.g., low signal-to-noise ratio, sporadic motor unit action potentials), and reliable across measurements (e.g., sensors, trials, and days), while providing a continuum of scale over the full magnitude range for each session. Furthermore, sEMG active duration could well characterize the clinically observed differences in hypertonia expressed across different muscles and impairment levels. The proposed measure can be used for continuous and quantitative monitoring of hypertonia during activities of daily living while at home, which will allow for the study of the practical effect of pharmacological and/or physical interventions that try to combat its presence.

scholarly journals Surface EMG-based Surgical Instrument Classification for Dynamic Activity Recognition in Surgical Workflows

2019 ◽  
Vol 5 (1) ◽  
pp. 37-40 ◽  
Author(s):  
Richard Bieck ◽  
Reinhard Fuchs ◽  
Thomas Neumuth
Keyword(s):  
Network Architecture ◽  
Muscle Activation ◽  
User Study ◽  
Muscle Tone ◽  
Sampling Rate ◽  
Recognition System ◽  
Surface Emg ◽  
Surgical Instruments ◽  
Data Sampling ◽  
Dynamic Activity

AbstractWe introduce a wearable-based recognition system for the classification of natural hand gestures during dynamic activities with surgical instruments. An armbandbased circular setup of eight EMG-sensors was used to superficially measure the muscle activation signals over the broadest cross-section of the lower arm. Instrument-specific surface EMG (sEMG) data acquisition was performed for 5 distinct instruments. In a first proof-of-concept study, EMG data were analyzed for unique signal courses and features, and in a subsequent classification, both decision tree (DTR) and shallow artificial neural network (ANN) classifiers were trained. For DTR, an ensemble bagging approach reached precision and recall rates of 0.847 and 0.854, respectively. The ANN network architecture was configured to mimic the ensemble-like structure of the DTR and achieved 0.952 and 0.953 precision and recall rates, respectively. In a subsequent multi-user study, classification achieved 70 % precision. Main errors potentially arise for instruments with similar gripping style and performed actions, interindividual variations in the acquisition procedure as well as muscle tone and activation magnitude. Compared to hand-mounted sensor systems, the lower arm setup does not alter the haptic experience or the instrument gripping, which is critical, especially in an intraoperative environment. Currently, drawbacks of the fixed consumer product setup are the limited data sampling rate and the denial of frequency features into the processing pipeline.

scholarly journals Improvement of the Target Sensitivity in DECIGO by Optimizing Its Parameters for Quantum Noise Including the Effect of Diffraction Loss

Galaxies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Tomohiro Ishikawa ◽  
Shoki Iwaguchi ◽  
Yuta Michimura ◽  
Masaki Ando ◽  
Rika Yamada ◽  
...  
Keyword(s):  
Gravitational Wave ◽  
Quantum Noise ◽  
Signal To Noise Ratio ◽  
Outer Space ◽  
Optical Diffraction ◽  
Design Parameters ◽  
Diffraction Loss ◽  
Noise Sources ◽  
Good Target ◽  
Spectrum Density

The DECi-hertz Interferometer Gravitational-wave Observatory (DECIGO) is the future Japanese, outer space gravitational wave detector. We previously set the default design parameters to provide a good target sensitivity to detect the primordial gravitational waves (GWs). However, the updated upper limit of the primordial GWs by the Planck observations motivated us toward further optimization of the target sensitivity. Previously, we had not considered optical diffraction loss due to the very long cavity length. In this paper, we optimize various DECIGO parameters by maximizing the signal-to-noise ratio (SNR) of the primordial GWs to quantum noise, including the effects of diffraction loss. We evaluated the power spectrum density for one cluster in DECIGO utilizing the quantum noise of one differential Fabry–Perot interferometer. Then we calculated the SNR by correlating two clusters in the same position. We performed the optimization for two cases: the constant mirror-thickness case and the constant mirror-mass case. As a result, we obtained the SNR dependence on the mirror radius, which also determines various DECIGO parameters. This result is the first step toward optimizing the DECIGO design by considering the practical constraints on the mirror dimensions and implementing other noise sources.

scholarly journals Information Entropy- and Average-Based High-Resolution Digital Storage Oscilloscope

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Jiang ◽  
Lianping Guo ◽  
Kuojun Yang ◽  
Huiqing Pan
Keyword(s):  
Data Fusion ◽  
Information Entropy ◽  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Storage Oscilloscope ◽  
Whole Process ◽  
Digital Storage ◽  
Gross Error ◽  
Digital Storage Oscilloscope ◽  
Sample Data

Vertical resolution is an essential indicator of digital storage oscilloscope (DSO) and the key to improving resolution is to increase digitalizing bits and lower noise. Averaging is a typical method to improve signal to noise ratio (SNR) and the effective number of bits (ENOB). The existing averaging algorithm is apt to be restricted by the repetitiveness of signal and be influenced by gross error in quantization, and therefore its effect on restricting noise and improving resolution is limited. An information entropy-based data fusion and average-based decimation filtering algorithm, proceeding from improving average algorithm and in combination with relevant theories of information entropy, are proposed in this paper to improve the resolution of oscilloscope. For single acquiring signal, resolution is improved through eliminating gross error in quantization by utilizing the maximum entropy of sample data with further noise filtering via average-based decimation after data fusion of efficient sample data under the premise of oversampling. No subjective assumptions and constraints are added to the signal under test in the whole process without any impact on the analog bandwidth of oscilloscope under actual sampling rate.

Physical AWGN Channel Emulator for Bit Error Rate Test of Digital Baseband Communication

2012 ◽  
Vol 241-244 ◽  
pp. 2491-2495 ◽  
Author(s):  
Antonio Boscolo ◽  
Francesca Vatta ◽  
Francesco Armani ◽  
Emanuele Viviani ◽  
Daniele Salvalaggio
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Error Correcting Codes ◽  
Noise Sources ◽  
Channel Emulator ◽  
Physical Channel ◽  
Rate Test ◽  
Data Signal ◽  
Good Agreement

This paper presents a physical channel emulator solution for applications such as Bit Error Rate Testing of Error Correcting Codes. The solution relies on an analog White Gaussian Noise Generator coupled additively with an analog data signal to emulate the communication channel. This is interfaced to a computer through a USB connection, allowing the use of programs in different environments, such as Matlab and Labview. This solution can allow different types of channels to be emulated and with different noise sources. A software-based method to measure Signal to Noise Ratio and to characterize the channel is also presented. The system has been validated using a Matlab interface implementing multiple error correcting codes and showed good agreement with the theoretical model.

scholarly journals Is the Level of Noise in a School Environment be Harmful to the Hearing of Teachers?

2020 ◽  
Vol 24 (04) ◽  
pp. e503-e507
Author(s):  
Gabriela Guenther Ribeiro Novanta ◽  
Sergio Luiz Garavelli ◽  
Andre Luiz Lopes Sampaio
Keyword(s):  
Classroom Environment ◽  
School Environment ◽  
Otoacoustic Emissions ◽  
Signal To Noise Ratio ◽  
Signal Amplitude ◽  
Daily Basis ◽  
Outer Hair Cells ◽  
Distortion Product ◽  
Teachers And Students ◽  
The Difference

Abstract Introduction The excessive noise observed in the school environment can cause damages or losses to the learning process as well as risks to the health of teachers and students, such as physical, mental and social impairments, including, among them, hearing loss. Objective To assess otoacoustic emissions in teachers and determine whether classroom noise reduces distortion-product otoacoustic emissions (DPOAEs) amplitude and signal-to-noise ratio (SNR). Method Sixty-seven teachers were evaluated using otoacoustic emissions testing in two situations: after hearing rest and after the working day. Results Signal amplitude (p = 0.044 [2 kHz]; p = 0.01 [4 kHz]) and SNR for frequencies of 2 kHz (p = 0.008) and 4 kHz (p = 0.001) decreased significantly between time points. Mean classroom noise was associated with the magnitude of the difference in signal amplitude at 2 kHz (p = 0.017) and 4 kHz (p = 0.015), and SNR at 4 kHz (p = 0.023). Conclusions There was a decrease in the amplitude and in the SNR after exposure to the noise in the classroom environment. The high levels of sound pressure that teachers are exposed to on a daily basis can cause a temporary change in the outer hair cells of the Corti organ, and these changes may become permanent over time.

scholarly journals Lossless Image Compression Techniques: A State-of-the-Art Survey

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1274 ◽  
Author(s):  
Md. Atiqur Rahman ◽  
Mohamed Hamada
Keyword(s):  
Image Compression ◽  
High Speed ◽  
Communication Theory ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Daily Basis ◽  
The State ◽  
Huge Amount ◽  
Lossless Image Compression ◽  
Art Techniques

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.

Least Mean Square Measures of Voice Perturbation

1987 ◽  
Vol 30 (4) ◽  
pp. 529-538 ◽  
Author(s):  
Paul Milenkovic
Keyword(s):  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Processing Technique ◽  
Model Fit ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal Processing Technique ◽  
Normal Voice ◽  
Interpolation Technique ◽  
Low Levels

A signal processing technique is described for measuring the jitter, shimmer, and signal-to-noise ratio of sustained vowels. The measures are derived from the least mean square fit of a waveform model to the digitized speech waveform. The speech waveform is digitized at an 8.3 kHz sampling rate, and an interpolation technique is used to improve the temporal resolution of the model fit. The ability of these procedures to measure low levels of perturbation is evaluated both on synthetic speech waveforms and on the speech recorded from subjects with normal voice characteristics.

Sampling rate effects on surface EMG timing and amplitude measures

2003 ◽  
Vol 18 (6) ◽  
pp. 543-552 ◽  
Author(s):  
Jeffrey C. Ives ◽  
Janet K. Wigglesworth
Keyword(s):  
Sampling Rate ◽  
Surface Emg ◽  
Rate Effects

EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE

2013 ◽  
Vol 12 (03) ◽  
pp. 1350016 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
FRANCK QUAINE ◽  
YANN LAURILLAU ◽  
SIRINEE THONGPANJA ◽  
CHUSAK LIMSAKUL ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Feature Space ◽  
Recognition System ◽  
Surface Emg ◽  
Classification Performance ◽  
Computer Interface ◽  
Computational Time ◽  
Flexor Muscle ◽  
Emg Amplitude ◽  
Emg Signal

To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.

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