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Erik Scheme
University of New Brunswick
Pattern recognitionProsthesisComputer visionComputer scienceFeature extraction
56Publications
10H-index
276Citations
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Publications 89
Newest
#1Angkoon Phinyomark (UNB: University of New Brunswick)H-Index: 20
#2Robyn Larracy (UNB: University of New Brunswick)
Last. Erik Scheme (UNB: University of New Brunswick)H-Index: 10
view all 3 authors...
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 affect the outcome: the series length and the parameters used in the algorithm. The Hurst exponent, scaling exponent, and/or fractal dimension are comput...
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#1Evan CampbellH-Index: 1
#2Angkoon Phinyomark (UNB: University of New Brunswick)H-Index: 20
Last. Erik SchemeH-Index: 10
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This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.The in...
3 CitationsSource
#1Evan Campbell (UNB: University of New Brunswick)H-Index: 1
#2Jason Chang (UNB: University of New Brunswick)
Last. Erik Scheme (UNB: University of New Brunswick)H-Index: 10
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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 myoelectric control systems by leveraging existing data to shorten training. However, due to the difficulty of obtaining large sets of data from ampu...
#1Evan Campbell (UNB: University of New Brunswick)H-Index: 1
#2Angkoon Phinyomark (UNB: University of New Brunswick)H-Index: 20
Last. Erik Scheme (UNB: University of New Brunswick)H-Index: 10
view all 3 authors...
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 providing gesture specific information, exceeding the performance of IMUs alone. This study, therefore, presents a preliminary investigation of these confli...
1 Citations
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 shifts the focus from feature engineering to feature learning. Nevertheless, the black-box nature of deep learning makes it hard to understand the type of ...
2 CitationsSource
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, a novel supervised adaptation approach based on transfer learning (TL) with convolutional neural networks (CNNs) is proposed which requires only a short training session (a few seconds for each class) to recalibrate the system. TL is proposed as a so...
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#1Satinder GillH-Index: 2
#2Nitin Seth (UNB: University of New Brunswick)
Last. Erik SchemeH-Index: 10
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Due to the increasing rates of chronic diseases and an aging population, the use of assistive devices for ambulation is expected to grow rapidly over the next several years. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor changes in gait due to the presence of pain or a condition in outdoor and indoor environments. In this paper, we evaluated the effectiveness of a multi-sensor cane in detecting changes in gait due to the presence of simulated gait abno...
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#1Angkoon Phinyomark (UNB: University of New Brunswick)H-Index: 4
#1Angkoon Phinyomark (UNB: University of New Brunswick)H-Index: 20
Last. Erik Scheme (UNB: University of New Brunswick)H-Index: 10
view all 3 authors...
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, and classification. The real challenge for prostheses and gesture recognition interfaces are the dynamic factors that invoke changes in EMG signal chara...
2 CitationsSource
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-calibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this re-calibration before every...
#1Ulysse Cote-AllardH-Index: 3
Last. Benoit GosselinH-Index: 18
view all 7 authors...
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 within an offline dataset as each of them exponentially augment the number of segments to be recorded. On the other hand, online datasets are biased ...
1 Citations
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