Choice Of Mechanomyography Sensors For Diverse Types Of Muscle Activities

Skeletal muscles contribute to the movement produced in the human body.They are therefore of vital importance for the study of muscles in various applications of movement including exercise,sports,prosthesis,rehabilitation,etc. The movement produced by skeletal muscles can be analyzed through various techniques like mechanomyography (MMG) and electromyography (EMG).MMG is a novel technique to assess skeletal muscle function through the oscillations produced during muscle contractions.MMG advocates well for its reliability,performance,and ease in application to other presently used techniques.MMG employs several types of sensors to observe vibrations in skeletal muscles.These sensors vary widely from application to type of movement and muscle.This review provides a comprehensive chunk of information on MMG sensor selection according to its placement,muscle function and condition,and limb movement and application. Recommendations for the choice of MMG sensor are given through extensive literature search over here

Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severit

Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informativ

Virtual Phacoemulsification Surgical Simulation Using Visual Guidance And Performance Parameters As A Feasible Proficiency Assessment Tool

Background: Computer based surgical training is believed to be capable of providing a controlled virtual environment for medical professionals to conduct standardized training or new experimental procedures on virtual human body parts,which are generated and visualised three-dimensionally on a digital display unit. The main objective of this study was to conduct virtual phacoemulsification cataract surgery to compare performance by users with different proficiency on a virtual reality platform equipped with a visual guidance system and a set of performance parameters.Methods: Ten experienced ophthalmologists and six medical residents were invited to perform the virtual surgery of the four main phacoemulsification cataract surgery procedures – 1) corneal incision (CI),2) capsulorhexis (C),3) phacoemulsification (P), and 4) intraocular lens implantation (IOL).Each participant was required to perform the complete phacoemulsification cataract surgery using the simulator for three consecutive trials (a standardized 30-min session).The performance of the participants during the three trials was supported using a visual guidance system and evaluated by referring to a set of parameters that was implemented in the performance evaluation system of the simulator.Results: Subjects with greater experience obtained significantly higher scores in all four main procedures – CI1 (ρ = 0.038),CI2 (ρ = 0.041),C1 (ρ = 0.032), P2 (ρ = 0.035) and IOL1 (ρ = 0.011).It was also found that experience improved the completion times in all modules – CI4 (ρ = 0.026),C4 (ρ = 0.018),P6 (ρ = 0.028) and IOL4 (ρ = 0.029).Positive correlation was observed between experience and anti-tremor – C2 (ρ = 0.026) P3 (ρ = 0.015),P4 (ρ = 0.042) and IOL2 (ρ = 0.048) and similarly with anti-rupture – CI3 (ρ = 0.013),C3 (ρ = 0.027),P5 (ρ = 0.021) and IOL3 (ρ = 0.041).No significant difference was observed between the groups with regards to P1 (ρ = 0.077). Conclusions: Statistical analysis of the results obtained from repetitive trials between two groups of users reveal that augmented virtual reality (VR) simulators have the potential and capability to be used as a feasible proficiency assessment tool for the complete four main procedures of phacoemulsification cataract surgery (ρ < 0.05),indicating the construct validity of the modules simulated with augmented visual guidance and assessed through performance parameters

Virtual Phacoemulsification Surgical Simulation Using Visual Guidance And Performance Parameters As A Feasible Proficiency Assessment Tool

Background: Computer based surgical training is believed to be capable of providing a controlled virtual environment for medical professionals to conduct standardized training or new experimental procedures on virtual human body parts, which are generated and visualised three-dimensionally on a digital display unit. The main objective of this study was to conduct virtual phacoemulsification cataract surgery to compare performance by users with different proficiency on a virtual reality platform equipped with a visual guidance system and a set of performance parameters. Methods: Ten experienced ophthalmologists and six medical residents were invited to perform the virtual surgery of the four main phacoemulsification cataract surgery procedures – 1) corneal incision (CI), 2) capsulorhexis (C), 3) phacoemulsification (P), and 4) intraocular lens implantation (IOL). Each participant was required to perform the complete phacoemulsification cataract surgery using the simulator for three consecutive trials (a standardized 30-min session). The performance of the participants during the three trials was supported using a visual guidance system and evaluated by referring to a set of parameters that was implemented in the performance evaluation system of the simulator. Results: Subjects with greater experience obtained significantly higher scores in all four main procedures – CI1 (ρ = 0.038), CI2 (ρ = 0.041), C1 (ρ = 0.032), P2 (ρ = 0.035) and IOL1 (ρ = 0.011). It was also found that experience improved the completion times in all modules – CI4 (ρ = 0.026), C4 (ρ = 0.018), P6 (ρ = 0.028) and IOL4 (ρ = 0.029). Positive correlation was observed between experience and anti-tremor – C2 (ρ = 0.026), P3 (ρ = 0.015), P4 (ρ = 0.042) and IOL2 (ρ = 0.048) and similarly with anti-rupture – CI3 (ρ = 0.013), C3 (ρ = 0.027), P5 (ρ = 0.021) and IOL3 (ρ = 0.041). No significant difference was observed between the groups with regards to P1 (ρ = 0.077). Conclusions: Statistical analysis of the results obtained from repetitive trials between two groups of users reveal that augmented virtual reality (VR) simulators have the potential and capability to be used as a feasible proficiency assessment tool for the complete four main procedures of phacoemulsification cataract surgery (ρ < 0.05), indicating the construct validity of the modules simulated with augmented visual guidance and assessed through performance parameters

Wheeze Sound Analysis Using Computer-Based Techniques: A Systematic Review

Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstructio

Recommendations Related To Wheeze Sound Data Acquisition

In the field of computerized respiratory sounds,a reliable data set with a sufficient number of subjects is required for the development of wheeze detection algorithm or for further analysis.Validated and accurate data is a critical issue in the field of research.In this study,the protocol related to wheeze sound data acquisition is discussed.Previously,most articles focused on wheeze detection or its parametric analysis,but no consideration was given to data acquisition.Second major purpose of this study is to exhibit particulars of our dataset which was attained for future analysis.We compile a database with a sufficient and reliable number of cases with all essential details,in contrast to commercially available wheeze sound data used for research,freely available online data on websites and data used to train medical students for auscultation

Design And Development Of Path Planning Techniques For A Tennis Ball Retriever Robot

During a tennis solo training,players usually train using an automatic ball launcher machine.After some time, they are required to collect all the balls scattered all around the court themselves to refill the launcher machine. This is a physically challenging procedure, which is generally loathed by keen tennis players and may cause unwelcome injuries. This study aims to design of an autonomous tennis ball retriever that will discard all the unnecessary energy and time wasting in traditional ball picking up method. This robot will sweep all the balls using a suitable path planning technique. After this, a few path planning methods such as Coverage Path Planning (CPP) U-Turn,CPP ISS,and Probabilistic Roadmap Method (PRM) were integrated into the tennis ball retriever robot for comparison. After comparison between all the experiment done,CPP U-Turn is proven the best path planning method among the three tested algorithms to be integrated into a tennis ball retriever robot

Hand Motion Pattern Recognition Analysis Of Forearm Muscle Using MMG Signals

Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifie

Analysis And Classification Of Multiple Hand Gestures Using MMG Signals

This research aimed to find out whether the MMG signal is useful in recognition of multiple hand gesture.The following hand gestures are Hand closing, wrist flexion, wrist extension,opening,pointing.MMG is reflects the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction.However, external mechanical noise sources such as movement artifact are known to cause considerable interference to MMG compromising the classification accuracy.First aim to develop various feature extraction algorithms software that can identify multiple hand gesture using MMG signal. The main purpose of this work is to identify the hand gestures that are predefined using the artificial neural network,which is particularly useful for classification purpose.The MMG patterns are extracted from the signals for each movement,the features extracted from the signals are given to the neural network for training and classification since it is the good technique for classifying the bio signals.The features like mean absolute value,root mean square,variance,standard deviation and root mean square are chosen to train the neural network