scholarly journals A Review of Force Myography Research and Development

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4557 ◽  
Author(s):  
Zhen Gang Xiao ◽  
Carlo Menon
Keyword(s):  
Research And Development ◽  
Muscle Stiffness ◽  
Machine Learning Algorithms ◽  
Research Progress ◽  
Force Myography ◽  
Machine Interface ◽  
Art Research ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Insight Into

Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.

scholarly journals An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2432 ◽  
Author(s):  
Zhen Gang Xiao ◽  
Carlo Menon
Keyword(s):  
Upper Limb ◽  
Sampling Frequency ◽  
Isometric Actions ◽  
Monitoring Applications ◽  
Minimum Requirements ◽  
Data Content ◽  
Acquisition Device ◽  
Force Myography ◽  
Machine Interface ◽  
Insight Into

Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised.

Cyclostationary Approach for Instabilities Detection and Condition Monitoring of Centrifugal Compressor

2020 ◽  
Author(s):  
Mateusz Stajuda ◽  
David Garcia Cava ◽  
Grzegorz Liśkiewicz
Keyword(s):  
Condition Monitoring ◽  
Centrifugal Compressor ◽  
Operating Conditions ◽  
Periodic Signal ◽  
Centrifugal Compressors ◽  
First Order ◽  
Standard Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Insight Into

Abstract This study intends to explore the capabilities of the cyclostationary approach for instabilities detection and operating conditions monitoring of centrifugal compressors. Cyclostationary approach offers powerful signal analysis methods, applicable to different processes. It was proven useful for analysis of vibration, acoustic and pressure data for systems exhibiting periodicity. Cyclostationarity has been used for extracting subtle changes between cycles of the periodic signal which could be used for condition monitoring. Recent research focuses on employing this method for fault indication. Cyclostationary approach has not been extensively used in the field of turbomachinery, except for a few cases when it was proven to give a better insight into flow structure than standard signal processing techniques and allow for the detection of instabilities in flow systems. Thus, the cyclostationary approach may be suitable for instabilities detection and condition monitoring in centrifugal compressors. This paper exploits various techniques employing a cyclostationary framework for instabilities detection and operating conditions monitoring with the use of pressure signals from the low-speed centrifugal compressor. The most prospective cyclostationarity-based indicators are applied for the detection of instabilities. Due to a lack of second-order cyclostationarity, the study confines to the analysis of first-order cyclostationarity strongly exhibited in the compressor pressure signal. First-order cyclostationarity analysis provides an indication of instabilities and working conditions differentiation, but due to time-domain sampling, it is not fully robust and reliable. The highest potential is perceived in the cyclostationary approach use to extract changes between cycles. Different measures of change in variability could serve as a valuable indicator of instabilities.

scholarly journals An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ashwin Belle ◽  
Rosalyn Hobson Hargraves ◽  
Kayvan Najarian
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Discrete Wavelet ◽  
Physiological Signal ◽  
Cognitive Attention ◽  
Primary Focus ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Signal Processing Techniques

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

scholarly journals A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3037
Author(s):  
Miguel Luján ◽  
María Jimeno ◽  
Jorge Mateo Sotos ◽  
Jorge Ricarte ◽  
Alejandro Borja
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Autobiographical Memory ◽  
Memory Deficits ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Time Frequency ◽  
Machine Learning Classification ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms

2020 ◽  
pp. 1-9
Author(s):  
P. Ajitha ◽  
A. Sivasangari ◽  
R. Immanuel Rajkumar ◽  
S. Poonguzhali
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Sentiment Mining ◽  
Processing Techniques ◽  
Text Sentiment Analysis ◽  
Insight Into

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.

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