Angkoon Phinyomark

University of New Brunswick · Institute of Biomedical Engineering

This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devic...

Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shift...

In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interp...

The functional equivalence (FE) between imagery and perception or motion has been proposed on the basis of neuroimaging evidence of large spatially overlapping activations between real and imagined sensori-motor conditions. However, similar local activation patterns do not imply the same mesoscopic integration of brain regions, which can be describ...

The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introdu...

Myoelectric control has been used predominantly in the field of prosthetics, but is an increasingly promising hands-free input modality for emerging consumer markets such as mixed reality. Developing robust machine learning-enabled EMG control systems, however, has historically required substantial domain expertise. This has presented a significant...

Wrist electromyography (EMG) signals have been explored for incorporation into subtle wrist-worn wearable devices for decoding hand gestures. Previous studies have now shown that wrist EMG can even outperform the more commonly used forearm EMG, depending on the application. However, the performance and robustness of wrist EMG-based pattern recognit...

Background: Population-level screening programs aimed at early detection and treatment of breast cancer saves lives. Analyzing breath using infrared spectroscopy offers a highly sensitive, non-invasive, and cost-effective mechanism for identifying exhaled volatile organic chemicals, and it is hypothesized that it may identify differences in the “br...

Early diagnosis of lung cancer greatly improves the likelihood of survival and remission, but limitations in existing technologies like low-dose computed tomography have prevented the implementation of widespread screening programs. Breath-based solutions that seek disease biomarkers in exhaled volatile organic compound (VOC) profiles show promise...

Surface electromyography (sEMG) signals are now commonly used in continuous myoelectric control of prostheses. More recently, researchers have considered EMG-based gesture recognition systems for human computer interaction research. These systems instead focus on recognizing discrete gestures (like a finger snap). The majority of works, however, ha...

In early stage biomedical studies, small datasets are common due to the high cost and difficulty of sample collection with human subjects. This complicates the validation of machine learning models, which are best suited for large datasets. In this work, we examined feature selection techniques, validation frameworks, and learning curve fitting for...

Though breath analysis shows promise as a noninvasive and cost-effective approach to lung cancer screening, biomarkers in exhaled breath samples can be overwhelmed by irrelevant internal and environmental volatile organic compounds (VOCs). These extraneous VOCs can obscure the disease signature in a spectral breathprint, hindering the performance o...

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hema...

The effort, focus, and time to collect data and train EMG pattern recognition systems is one of the largest barriers to their widespread adoption in commercial applications. In addition to multiple repetitions of motions, including exemplars of confounding factors during the training protocol has been shown to be critical for robust machine learnin...

Current state-of-the-art myoelectric interfaces employ traditional pattern recognition (PR) algorithms to decode the Electromyogram (EMG) signals into hand movements for controlling artificial limbs. Recently, deep learning (DL) models have also been exploited for EMG feature learning/extraction. Models like Convolutional Neural Networks (CNN), whi...

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including th...

The design of pattern recognition-based myoelectric interfaces has been heavily explored and contested in the research literature. A considerable proportion of the performance of these interfaces has been linked to the quality of the feature extraction (FE) stage used to describe the underlying myoelectric signal. In this paper, we address two impo...

Despite a historical focus on prosthetics, the incorporation of electromyography (EMG) sensors into less obtrusive wearable designs has recently gained attention as a potential human-computer interaction scheme for general consumer use. Because consumers are more used to wrist-worn devices, this study presents a comprehensive and systematic investi...

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time...

In recent years, many electromyography (EMG) benchmark databases have been made publicly available to the myoelectric control research community. Many small laboratories that lack the instrumentation, access, and experience needed to collect quality EMG data have used these benchmark datasets to explore and propose new signal processing and pattern...

Recent advancements in wearable technologies have increased the potential for practical gesture recognition systems using electromyogram (EMG) signals. However, despite the high classification accuracies reported in many studies (> 90%), there is a gap between academic results and industrial success. This is in part because state-of-the-art EMG-bas...

e21037 Background: Currently low-dose computed tomography is used for lung cancer (LC) screening, but is limited by radiation exposure, cost, and a high false detection rate (1,2). An accurate, accessible and affordable screening technology is needed to improve detection of LC in high risk individuals. Methods: We conducted an unblinded, prospectiv...

Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as a marker for gait adaptability, gait disorder, and fall risk among patients with movement disorders. This study is designed to systematically and comprehensively investigate two practical aspects of fractal analysis which significantly aff...

Despite decades of research and development of pattern recognition approaches, the clinical usability of myoelectriccontrolled prostheses is still limited. One of the main issues is the high inter-subject variability that necessitates long and frequent user-specific training. Cross-user models present an opportunity to improve clinical viability of...

Recent human computer-interaction (HCI) studies using electromyography (EMG) and inertial measurement units (IMUs) for upper-limb gesture recognition have claimed that inertial measurements alone result in higher classification accuracy than EMG. In biomedical research such as in prosthesis control, however, EMG remains the gold standard for provid...

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability o...

Electromyography (EMG) is the process of measuring the electrical activity produced by muscles throughout the body using electrodes on the surface of the skin or inserted in the muscle. EMG pattern recognition based myoelectric control systems typically contain data pre-processing, data segmentation, feature extraction, dimensionality reduction, an...

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic re...

Within sEMG-based gesture recognition, a chasm exists in the literature between offline accuracy and real-time usability of a classifier. This gap mainly stems from the four main dynamic factors in sEMG-based gesture recognition: gesture intensity, limb position, electrode shift and transient changes in the signal. These factors are hard to include...

The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus...

The linear discriminant analysis (LDA) classifier remains a standard in myoelectric control due to its simplicity, ease of implementation, and robustness. Despite this, challenges associated with the temporal evolution of the myoelectric signal may require flexibility beyond the capabilities of standard LDA. Recently proposed approaches have levera...

Difficulties accessing amputee populations has resulted in the widespread adoption of able-bodied subjects in virtual environments for the development of myoelectric prostheses. Factors such as scar tissue, different physiologies or surgical outcomes, and reduced visual and proprioceptive feedback, however, may contribute to differences in electrom...

Technological advances are providing researchers with larger and larger amounts of data, creating a rich landscape for data scientists to explore and search for meaningful patterns. Novel biomedical imaging techniques over the next 5 to 10 years, for instance, will develop their resolution to the point at which single subject scans might provide te...

This paper presents a novel set of temporally inspiredtime domain features for electromyographic (EMG) pattern recognition. The proposed methods employ simple time series measures derived from peak detection, and could better reflect EMG activity over time. Multiple EMG datasets consisting of 68 able-bodied and transradial amputee subjects performi...

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands ty...

Abstract Background Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data...

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle “big data”. Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors ass...

With recent advancements in wearable sensors, wireless communication and embedded computing technologies, wearable EMG armbands are now commercially available and accessible to most laboratories. Due to the embedded system constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g. 200 Hz for the Myo armband) than p...

The functional equivalence between mental images and perception or motion has been proposed on the basis of neuroimaging evidence of large spatially overlapping activations between real and imagined sensori-motor conditions. However, similar local activation patterns do not imply the same mesoscopic integration of brain regions active during imager...

The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variabili...

Resting state functional magnetic resonance imaging (rfMRI) can be used to measure functional connectivity and then identify brain networks and related brain disorders and diseases. To explore these complex networks, however, huge amounts of data are necessary. Recent advances in neuroimaging technologies, and the unique methodological approach of...

Background Running-related overuse injuries can result from the combination of extrinsic (e.g., running mileage) and intrinsic risk factors (e.g., biomechanics and gender), but the relationship between these factors is not fully understood. Therefore, the first purpose of this study was to determine whether we could classify higher- and lower-milea...

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using...

The purpose of this study is to design effective sparse feature sets from meaningful subgroups of the 58 state-of-the-art EMG features, via a topological data analysis method called Mapper. The resulting topological network has a main structure shaped like a letter Y composed of three arms: (1) amplitude estimation features, (2) nonlinear complexit...

The aim of this study was to determine the test-retest reliability of linear acceleration waveforms collected at the low back, thigh, shank, and foot during walking, in a cohort of knee osteoarthritis patients, with applying two separate sensor attitude correction methods (static attitude correction and dynamic attitude correction). Linear accelera...

Background Not all patients with patellofemoral pain exhibit successful outcomes following exercise therapy. Thus, the ability to identify patellofemoral pain subgroups related to treatment response is important for the development of optimal therapeutic strategies to improve rehabilitation outcomes. The purpose of this study was to use baseline ru...

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to male runners. It has also been reported that male and female marathon runners are at increased risk of different injuries. Furthermore, the incidence rate for running injuries depends on the specificity of the group of runners concerned (such as recrea...

Muscle strengthening exercises can improve pain and function in adults with knee osteoarthritis (OA), but individual responses can vary widely. Recent research has demonstated the ability to predict which individuals will respond favourably to a muscle strengthening intervention using 3-dimensional (3D) gait kinematic data. While this is a promisin...

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to male runners. It has also been reported that male and female marathon runners are at increased risk of different injuries. Furthermore, the incidence rate for running injuries depends on the specificity of the group of runners concerned (such as recrea...

This study proposes and evaluates the application of two classifiers: decision tree (DT) and neural network (NN) to discriminate three region types: cancer (CC), lymphocyte (LC), and stromal (SC) in the breast cancer cell images. The feature extraction from area based texture information of BCCI is studied to compare results from the segmented cell...

Much of the biomechanical research over the past 20 years has investigated the influence of potential injury risk factors in isolation. More likely, multiple biomechanical and clinical variables interact with one another and operate as combined risk factors to the point that traditional biomechanical analysis techniques (that is, using discrete var...

Background Females have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting...

The identification of between-group differences and changes in gait mechanics are useful for injury prevention. Previous studies suggest the differences in gait biomechanical variables may interact in a complex non-linear fashion rather than a simple linear fashion. A non-linear multivariate analysis technique is therefore necessary to unravel the...

The features associated with temporal gait biomechanical data are complex and multivariate and it is therefore necessary to identify methods that reduce the difficulty underlying the interpretation and identification of differences between groups of interest. Discrete variables and principal component analysis (PCA) are feature extraction methods t...

The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminat...

Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification t...

Atypical running gait biomechanics are considered a primary factor in the aetiology of iliotibial band syndrome (ITBS). However, a general consensus on the underpinning kinematic differences between runners with and without ITBS is yet to be reached. This lack of consensus may be due in part to three issues: gender differences in gait mechanics, th...

Recently, a principal component analysis (PCA) approach has been used to provide insight into running pathomechanics. However, researchers often account for nearly all of the variance from the original data using only the first few, or lower-order principal components (PCs), which are often associated with the most dominant movement patterns. In co...

Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study...

Mean frequency (MNF) and median frequency (MDF) are often used to assess muscular fatigue from surface electromyography (sEMG) signals. To determine muscle fatigue at different levels of isometric and dynamic contractions, it is necessary to know the relationship between the force of contraction and the MNF/MDF methods. Despite numerous studies, no...

Exercise interventions are widely used for treatment of patellofemoral pain syndrome (PFPS). However, controversy remains on the effects of exercise therapy in pain reduction. It has been postulated that the inconsistent responses to an exercise therapy may be due to different gait profiles and kinematic patterns within this PFPS group. The aim of...

Previous studies have demonstrated distinct clusters of walking gait patterns in both healthy and pathological groups, suggesting that within samples of walkers, different movement strategies may be represented, and comparative outcomes may be influenced by these strategies. However, no studies have investigated whether distinct clusters exist for...

A linear relationship between the probability density function (PDF) of surface electromyography (EMG) signal and the level of contraction force has been found in many previous investigations. However, only few muscles have been studies while differences between muscles in anatomical and physiological properties are ackowledged. Therefore, the purp...

Surface electromyography (EMG) is a non-invasive technique for measuring the electrical activities from the muscles. To accurately classify movement patterns from EMG signals for the control of multifunctional devices, previous investigations suggest that the selection of the feature extraction techniques is more important than the selection of the...

A statistical measure is needed to estimate a probability density function (PDF) of EMG signals to choose the suitable feature extraction methods for EMG pattern recognition system. The utility of L-kurtosis was investigated in estimating the PDFs of three different dynamic EMG involving transient and steady-state signals during four hand motions m...