UNB Scholar Research Repository :: Browsing by Author "Scheme, Erik"

Browsing by Author "Scheme, Erik"

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A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control
(Institute of Electrical and Electronics Engineers, 2020) Ameri, Ali; Akhaee, Mohammad Ali; Scheme, Erik; Englehart, Kevin
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 solution to the problem of insufficient calibration data due to short training times for both classification and regression-based control schemes. This approach was validated for electrode shift of roughly 2.5cm with 13 able-bodied subjects to estimate individual and combined wrist motions. With this method, the original CNN (trained before the shift) was fine-tuned with the calibration data from after shifting. The results show that the proposed technique outperforms training a CNN from scratch (random initialization of weights) or a support vector machine (SVM) using the minimal calibration data. Moreover, it demonstrates superior performance than previous LDA and QDA-based adaptation approaches. As the outcomes confirm, the proposed CNN TL method provides a practical solution for adaptation to external factors, improving the robustness of electromyogram (EMG) pattern recognition systems.
A dynamic approach to balance assessment using self-induced perturbations
(University of New Brunswick, 2020) Chambers, Neil Cameron; Scheme, Erik; Sensinger, Jonathon
A novel balance assessment method for older adults was developed using a balance platform that induces dynamic self-perturbations and which could use synchronization as an adjustable level of assistance. Rather than measure the subject’s performance, the proposed balance assessment measures how much assistance they need to reach a standard level of performance. A swaying balance platform was constructed and instrumented to conduct the balance assessment with different levels of assistance. Nineteen healthy young adults were tested with self-perturbations introduced by following swaying visual instructions that changed frequency instantaneously. Experimental results were unable to confirm if synchronization was capable of providing assistance, nor whether the assessment outcomes could benefit from applying different levels of assistance. Future studies should focus on understanding how to ensure that synchronization occurs in the combined subject/instructions system.
A linearly extendible multi-artifact removal approach for improved upper extremity EEG-based motor imagery decoding
(IOP Publishing Ltd, 2021) Asogbon, Mojisola Grace; Samuel, Oluwarotimi Williams; Li, Xiangxin; Nsugbe, Ejay; Scheme, Erik; Li, Guanglin
Background and Objective: Non-invasive multichannel Electroencephalography (EEG) recordings provide an alternative source of neural information from which motor imagery (MI) patterns associated with limb movement intent can be decoded for use as control inputs for rehabilitation robots. The presence of multiple inherent dynamic artifacts in EEG signals, however, poses processing challenges for brain-computer interface (BCI) systems. A large proportion of the existing EEG signal preprocessing methods focus on isolating single artifact per time from an ensemble of EEG trials and require calibration and/or reference electrodes, resulting in increased complexity of their application to MI-EEG controlled rehabilitation devices in practical settings. Also, a few existing multi-artifacts removal methods though explored in other domains, they have rarely been investigated in the space of MI-EEG signals for multiple artifacts cancellation in a simultaneous manner. Approach: Building on the premise of previous works, this study propose a semi-automatic EEG preprocessing method that combines Generalized Eigenvalue Decomposition driven by low-rank approximation and a Multi-channel Wiener Filter (GEVD-MWF) that employs a learning technique for simultaneous elimination of multiple artifacts from MI-EEG signals. The proposed method is applied to remove multiple artifacts from 64-channel EEG signals recorded from transhumeral amputees while they performed distinct classes of upper limb MI tasks before decoding their movement intent using a selection of features and machine learning algorithms. Main Results: Experimental results show that the proposed GEVD-MWF method yields significant improvements in MI decoding accuracies, in the range of 13.23%-41.21% compared to four existing popular artifact removal algorithms. Further investigation revealed that the GEVD-MWF approach enabled accuracies in the range of 90.44% - 99.67% using "single trial" EEG recordings, which could eliminate the need to record and process large ensembles of EEG trials as commonly required in some existing approaches. Additionally, using a variant of the sequential forward floating selection algorithm, a subset of 9 channels was used to obtain a decoding accuracy of 93.73%±1.58%. Significance: Given its improved performance, reduced data requirements, and feasibility with few channels, the proposed GEVD-MWF could potentially spur the development of effective real-time control strategies for multi-degree of freedom EEG-based miniaturized rehabilitation robotic interfaces.
A multi-variate approach to predicting myoelectric control usability
(University of New Brunswick, 2020) Nawfel, Jena; Scheme, Erik; Englehart, Kevin
Pattern recognition techniques leveraging the use of electromyography (EMG) signals have become a popular approach to provide intuitive control of myoelectric devices. Performance of these control interfaces is commonly quantified using offline classification accuracy, despite researchers having shown that this metric is a poor indicator of usability. Several attempts have been made to find alternative training metrics that better correlate with online performance. Moderate correlations have been identified in some cases; however, the relationship between offline training and online usability has yet to be fully defined in the literature. The following work attempts to bridge this information divide by exploring combinations of offline training metrics capable of predicting myoelectric control usability. The results indicate that linear combinations of three offline training metrics provide superior predictive power of future online performance. Additionally, the role of feedback presented to the user during training is explored to determine its effect on performance and predictability. The results of this study suggest that properly designed feedback mechanisms can influence both the quality of the training metrics and the predictive ability of the developed linear models.
A semi-automated security assessment framework for wearable health monitoring devices
(University of New Brunswick, 2020) Amel Zendehdel, Ghazale; Stakhanova, Natalia; Scheme, Erik
A survey on neuromarketing using EEG signals
(IEEE, 2021-03-12) Khurana, Vaishali; Gahalawat, Monika; Kumar, Pradeep; Roy, Partha Pratim; Dogra, Debi Prosad; Scheme, Erik; Soleymani, Mohammad
Neuromarketing is the application of neuroscience to the understanding of consumer preferences toward products and services. As such, it studies the neural activity associated with preference and purchase intent. Neuromarketing is considered an emerging area of research, driven in part by the approximately 400 billion dollars spent annually on advertisement and promotion. Given the size of this market, even a slight improvement in performance can have an immense impact. Traditional approaches to marketing consider a posteriori user feedback in the form of questionnaires, product ratings, or review comments, but these approaches do not fully capture or explain the real-time decision-making process of consumers. Various physiological measurement techniques have been proposed to facilitate the recording of this crucial aspect of the decision-making process, including brain imaging techniques [functional magnetic resonance imaging (fMRI), electroencephalography (EEG), steady state topography (SST)], and various biometric sensors. The use of EEG in neuromarketing is especially promising. EEG detects the sequential changes of brain activity, without appreciable time delay, needed to assess both the unconscious reaction and sensory reaction of the customer. Several types of EEG devices are now available in the market, each with its own advantages and disadvantages. Researchers have conducted experiments using many of these devices, across different age groups and different categories of products. Because of the deep insights that can be gained, the field of neuromarketing research is carefully monitored by consumer and research protection groups to ensure that subjects are properly protected. This article surveys a range of considerations for EEG-based neuromarketing strategies, including the types of information that can be gathered, how marketing stimuli are presented to consumers, how such strategies may affect the consumer in terms of appeal and memory, machine learning techniques applied in the field, and the variety of challenges faced, including ethics, in this emerging field.
An exploration of EEG-based, non-stationary emotion classification for affective computing
(University of New Brunswick, 2020) Bendrich, Nicole; Scheme, Erik
The monitoring of emotional state is important in the prevention and management of mental health problems and is increasingly being used to support affective computing. Researchers are exploring various modalities from which emotion can be inferred, such as through facial images or via electroencephalography (EEG) signals. Current research commonly investigates the performance of machine-learning-based emotion recognition systems by exposing users to short films that are assumed to elicit a single known emotional response. Assuming static emotions, even for these brief periods, however, does not consider that emotions evolve. Moreover, in order to demonstrate better results, many existing models are not tested in ways that reflect realistic real-world implementations. In this thesis, the dynamic evolution of emotions induced using longer and variable stimuli is explored using EEG signals from the publicly available dataset, AMIGOS. A variety of feature engineering and selection techniques are applied and evaluated across four different cross-validation frameworks. The role of imperfect labelling of ground truth emotions and both data and gender-imbalances in the dataset are also investigated. Improved feature design and selection lead to up to 13% absolute improvement relative to comparable previously reported studies using this dataset. Alternative training configurations and a selective confidence-based classification scheme are proposed, leading to further possible improvements.
An investigation of transition-informed classifier adaptation for myoelectric control
(University of New Brunswick, 2023-12) Meneley, Julia; MacIsaac, Dawn; Scheme, Erik
Myoelectric prostheses use pattern recognition of surface electromyography (SEMG) to interpret a user’s intent. Over time, changes in the SEMG worsen the usability of these prostheses, requiring cumbersome retraining. Adaptive learning, although able to update the classifier, suffers from mislabelling errors during unsupervised use. This study aimed to overcome this by investigating the impact of transitions between classes, often associated with elevated misclassification, on the adaptation process. Several adaptation techniques, some based on explicitly avoiding transitions and others based on leveraging awareness of transitions to improve decision stream labelling, were explored. Finally, these transition-informed adaptation techniques were tested on two datasets that included sequences of transitions between known classes. Results suggest that an awareness of transience in the SEMG can inform the data selection process and improve the labelling of unsupervised data for adaptation. A resulting LC-SSL technique yielded significant (p¡0.05) improvement to several offline classifier performance metrics.
Augmented biofeedback for partial weight-bearing learning
(University of New Brunswick, 2019) Smith, Ian; Bateman, Scott; Scheme, Erik
Assistive devices, including canes and crutches, are used in partial weight-bearing (PWB)–offloading weight from limbs weakened by disease or injury to promote recovery and prevent reinjury. While it is important to accurately offload weight to target loads prescribed by healthcare providers, current training methods result in poor compliance. It is currently unknown how to most effectively provide feedback during training to allow users accurate execution of the skill later on. In this work, three studies were conducted to investigate the effects of feedback modality, delay, and resolution on both regulation and learning of PWB while stationary and during gait. Results indicate that concurrent feedback is best suited for continuous skill regulation whereas retrospective feedback is preferable for training PWB, and that task-specific training is critical for compliance. This work presents design guidelines for improved clinical PWB training methods and highlights the importance of researching retrospective motor learning methods.
Automation of the Timed Up and Go test using an instrumented walking cane
(University of New Brunswick, 2021-10) Valsangkar, Ameya; Scheme, Erik
The Timed Up and Go (TUG) test is used to test a person’s mobility and static and dynamic balance. It measures the time a person takes to stand up from a chair, walk three meters, turn around, walk back to the chair, and sit down. Typically, the TUG test is assessed by a physiotherapist with a stopwatch, limiting its effectiveness and making it prone to user error. This has motivated research into automated approaches capable of assessing the various segments of the TUG test using a range of sensing modalities. This study extends upon this body of work by evaluating the feasibility of segmenting the TUG test using an instrumented walking cane. More general contributions are made by introducing the use of error in transition time, as opposed to accuracy, as the cost function during the design of the machine learning framework, and a time-series inspired binary segmentation approach that facilitates the comparison of only two segments at a time. Data was collected using an instrumented cane that measures loading and movement information from 16 participants with musculoskeletal injuries. As a group, the participants yielded TUG times ranging from 11.12s to 28.57s, and a mean of 17.8s. Results of segmenting the TUG test into six segments - Sitting to standing, Walking, Turning, Walking back, Turning back, Standing to sitting - were validated using a leave-one-trial-out and a leave-one-person-out approach, to test both within- and across-participant performance. Various approaches were explored, including conventional classifiers Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), and extended time series and deep learning methods such as Hidden Markov Models (HMM), CNN LSTM (CLSTM) and Encoder-Decoder Temporal Convolutional Networks (EDTCN). A binary segmentation approach leveraging the temporal nature of the TUG test was adopted with a Dynamic Time Warping (DTW)-based postprocessing alignment. The calculated segmentation error for every case was recorded as both the performance measurement and the optimization parameter as opposed to the traditional use of accuracy of prediction. The results promisingly suggest that the segments or subtasks of a TUG test can be extracted using data collected from a smart cane, laying the groundwork for its automation.
Compression of EMG Signals Using Deep Convolutional Autoencoders
(Institute of Electrical and Electronics Engineers, 2022) Dinashi, Kimia; Ameri, Ali; Akhaee, Mohammad Ali; Englehart, Kevin; Scheme, Erik
Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However, due to limitations in internet speed and hardware resources, transmission and storage of EMG data are challenging. As a solution, this work proposes a new method for EMG data compression using deep convolutional autoencoders (CAE). Eight-channel EMG data from 10 subjects, and high-density EMG data from 18 subjects, were investigated for compression. The CAE architecture was designed to extract an abstract data representation that is heavily compressed, but from which the salient information for classification can be effectively reconstructed. The proposed method attained efficient compression; for CR = 1600, the average PRDN (percentage RMS difference normalized) was 31.5% and the wrist motions classification accuracy (CA) reduced roughly 5%. The CAE substantially outperformed the state-of-the-art high-efficiency video coding and a well-known wavelet-thresholding compression technique. Moreover, by reducing the bit-resolution of the CAE's compressed data from 24 bits to 6 bits, an additional 4-fold compression was achieved without significant degradation of the reconstruction performance. Furthermore, the CAE's inter-subject performance was promising; e.g., for CR = 1600, the PRDN for the inter-subject case was only 2.6% less than that of the within-subject performance. The powerful EMG compression performance with remarkable reconstruction results reflects the CAEs potential as an automatic end-to-end approach with the ability to learn the complete encoding and decoding process. Furthermore, the excellent inter-subject performance demonstrates the generalizability and usability of the proposed approach.
Data-driven approaches to reducing the training burden in pattern recognition based myoelectric control
(University of New Brunswick, 2020) Campbell, Evan David; Scheme, Erik
Advancements in EMG pattern recognition have enabled intuitive gesture recognition of upper limb motions for use in human computer interfaces. Whether it be to control a prosthesis, a drone, or a virtual reality environment, all current implementations rely on the acquisition of representative training data from the end-user before use. This requirement has been reported as frustrating for users and a limitation to the broader adoption of EMG-based controllers. Consequently, this thesis aims to address the burden of the training protocol for EMG pattern recognition using data-driven approaches and cross-user models. Two exploratory studies were conducted to assess the impact and degree of inter-subject variability and difference in EMG information content between user groups. Based on observed subject differences, an adaptive domain adversarial neural network (ADANN) was subsequently developed to adapt a previously trained model to a new user using minimal training data. The proposed ADANN cross-subject model significantly outperformed the current state-of-the-art canonical correlation analysis (CCA) cross-subject model for both intact-limb and amputee populations (with 9.4% and 22.4% absolute improvement, respectively). Finally, a generative adversarial network architecture, SinGAN, was adopted as a novel alternative for reducing the amount of EMG data needed for training. SinGAN was able to generate synthetic EMG signals based on a single subject-supplied motion repetition, significantly improving accuracy compared to training with the single motion alone.
Designing breathing exercise technologies for health and wellness
(University of New Brunswick, 2024-08) Tabor, Aaron; Bateman, Scott; Scheme, Erik
This doctoral research identifies design guidelines that can improve breathing exercise technologies – guidance and feedback systems that support breathing exercises. Specifically, the research demonstrates that two commonly employed Human Computer Interaction (HCI) design approaches for increasing user engagement (i.e., serious games) and decreasing attentional demand (i.e., peripheral information systems) can be used to promote breathing exercise technologies in a way that preserves exercise integrity and benefit. This is important because breathing exercises have a wide range of health and wellness benefits, and our designs may allow these benefits to be attained more fully and by a wider audience. Further, the research also contributes novel design artifacts and insights that will support the ongoing exploration of breathing exercise technologies. The findings may generalize to other design-focused research applications such as interventions for health and wellness, serious games for rehabilitation, and peripheral and ambient information systems.
Dynamic visual data prioritization in automated object detection systems for multi-camera surveillance
(University of New Brunswick, 2019) Cameron, James A. D.; Kaye, Mary; Scheme, Erik
Modern automated object detection systems are key tools in surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data created by surveillance cameras. Due to the nature of surveillance systems, this visual data must be processed accurately and in real-time. However, many of the frames that are created by the surveillance cameras may be of low importance, providing no useful information to the object detection system. Sub-sampling the surveillance data by prioritizing important camera frames can greatly reduce unnecessary computation. Several works have been conducted on dynamic visual data sub-sampling using various modalities of information (ie. spatial or temporal information) for prioritization. However, few works have used different modalities of information together for visual data prioritization. Furthermore, given the fast pace of the research space, only a small subset of works have implemented visual data prioritization with modern computer vision algorithms in mind. This work evaluates several individual prioritization metrics, that use different modalities of information to prioritize visual data, for use with modern object detection algorithms. This thesis presents an ensemble method that uses a KNN regressor to combine the best of the previously evaluated metrics. This dynamic approach was shown to increase the detection rate in an indoor surveillance scenario by over 60% compared to a static sub-sampling baseline.
Evaluation of myoelectric control learning using multi-session game-based training
(IEEE, 2018-07-12) Tabor, Aaron; Bateman, Scott; Scheme, Erik
While training is critical for ensuring initial success as well as continued adoption of a myoelectric powered prosthesis, relatively little is known about the amount of training that is necessary. In previous studies, participants have completed only a small number of sessions, leaving doubt about whether the findings necessarily generalize to a longer-term clinical training program. Furthermore, a heavy emphasis has been placed on a functional prosthesis use when assessing the effectiveness of myoelectric training. Although well-motivated, this all-inclusive approach may obscure more subtle improvements made in underlying muscle control that could lead to tangible benefits. In this paper, a deeper exploration of the effects of myoelectric training was performed by following the progress of 30 participants as they completed a series of ten 30-min training sessions over multiple days. The progress was assessed using a newly developed set of metrics that was specifically designed to quantify the aspects of muscle control that are foundational to the strong myoelectric prosthesis use. It was determined that, while myoelectric training can lead to improvements in muscle control, these improvements may take longer than previously considered, even occurring after improvements in the training game itself. These results suggest the need to reconsider how and when transfer from training activities is assessed.
Evaluation of the real-time usability of force myography as a human-computer interface
(University of New Brunswick, 2018) Belyea, Alexander; Scheme, Erik; Englehart, Kevin
Force myography (FMG) is an alternative to electromyography (EMG) for the control of powered upper limb prostheses. FMG signals originate from deformations of muscles and surrounding tissue applying pressure to a force sensor. FMG-based pattern recognition classifiers have been shown to yield high classification rates. High classification accuracy, however, does not ensure great device usability. Instead, these control systems for prostheses should be evaluated based on real-time usability metrics. In the first section of this work a proportional control algorithm, critical to the completion of the second phase of work, was derived and compared to a mean signal amplitude-based approach. In the second, the real-time usability of high-density force myography (HDFMG) was compared to that of EMG in a Fitts’ Law virtual target acquisition task. FMG was found to significantly outperform EMG in throughput for both classification (0.901±0.357 bits/s versus 0.751±0.309 bits/s) and regression (0.871±0.325 bits/s versus 0.689±0.269 bits/s) control types. The evaluated regression-based proportional control algorithm also performed significantly better (ρ<0.001) than a standard mean signal amplitude-based approach. Subsequent data collection from an amputee subject achieved comparable classification accuracy to the able-bodied participants, but an R2 correlation coefficient of only 0.375 for regression based proportional control, significantly (ρ<0.001) lower than the able-bodied results. This work provides a comparison between the real-time usability of HD-FMG and EMG-based control in both a traditional classification-based pattern recognition scheme, with an additional proportional controller dictating device velocity and a regression-based control scheme. HD-FMG was shown to outperform EMG in both control schemes in both throughput and efficiency.
Exploring performance limits for pressure-based gait biometrics
(University of New Brunswick, 2023-08) Kazemi, Saeed; Scheme, Erik
This thesis investigates pressure-based gait biometrics as a potential multi-factor authentication technique for building access control and border patrol security. The research aims to explore the performance limits of pressure-based gait authentication systems by considering two major confounding factors: participant count and measurement count. The study uses a publicly available dataset to implement and compare state-of-the-art pressure-based gait recognition algorithms. The results demonstrate that pressure-based gait biometrics have great potential as a reliable and robust authentication technique, especially in scenarios where other biometric identification techniques may not be feasible or practical. The findings of this research can help improve the design and implementation of pressure-based gait authentication systems for enhanced security applications.
FMG Versus EMG: A Comparison of Usability for Real-Time Pattern Recognition Based Control
(Institute of Electrical and Electronics Engineers, 2019) Belyea, Alex; Englehart, Kevin; Scheme, Erik
Objective: Force myography (FMG), which measures the surface pressure profile exerted by contracting muscles, has been proposed as an alternative to electromyography (EMG) for human-machine interfaces. Although FMG pattern recognition-based control systems have yielded higher offline classification accuracy, comparatively few works have examined the usability of FMG for real-time control. In this work, we conduct a comprehensive comparison of EMG- and FMG-based schemes using both classification and regression controllers. Methods: A total of 20 participants performed a two-degree-of-freedom Fitts' Law-style virtual target acquisition task using both FMG- and EMG-based classification and regression control schemes. Performance was evaluated based on the standard Fitts' law testing metrics throughput, path efficiency, average speed, number of timeouts, overshoot, stopping distance, and simultaneity. Results: The FMG-based classification system significantly outperformed the EMG-based classification system in both throughput (0.902 ± 0.270) versus (0.751 ± 0.309), (ρ <; 0.001) and path efficiency (87.2 ± 8.7) versus (83.2 ± 7.8), (ρ <; 0.001). Similarly, FMG-based regression significantly outperformed EMG-based regression in throughput (0.871 ± 0.2) versus (0.69 ± 0.3), (ρ <; 0.001) and path efficiency (64.8 ± 5.3) versus (58.8 ± 7.1), (ρ <; 0.001). Conclusions: The FMG-based schemes outperformed the EMG-based schemes regardless of which controller was used. This provides further evidence for FMG as a viable alternative to EMG for human-machine interfaces. Significance: This work describes a comprehensive evaluation of the online usability of FMG- and EMG-based control using both sequential classification and simultaneous regression control.
Game-based myoelectric muscle training
(University of New Brunswick, 2017) Tabor, Aaron; Bateman, Scott; Scheme, Erik
For new myoelectric prosthesis users, muscle training is a critical step that promotes effective use and long-term adoption of the prosthesis. Training, however, currently has several problems: 1) existing approaches require expensive tools and clinical expertise, restricting their use to the clinical environment, 2) exercises are boring, repetitive, and uninformative, making it difficult for patients to stay motivated, 3) assessment tools focus exclusively on improvements in functional, real-world prosthesis tasks, which conflicts with other therapeutic goals in early training, and 4) little is known about the effects of longer-term training because existing studies have subjected participants to a very short series of training sessions. While myoelectric training games have been proposed to create a more motivating training environment, commercially available games still exhibit many of these issues. Furthermore, current research presents inconsistent findings and conflicting results, making it unclear whether games hold therapeutic value. This research demonstrates that training games can be designed to address these issues by developing a low-cost, easy-to-use training game that targets the therapeutic goals of myoelectric training. Guidelines for promoting a fun, engaging, and informative training experience were identified by engaging prosthesis users and clinical experts throughout the design of a myoelectric training game. Furthermore, a newly developed set of metrics was used to demonstrate improvement in participants’ underlying muscle control throughout a series of game-based training sessions, further suggesting that games can be designed to provide therapeutic value. This work introduces an open-source training game, demonstrates the therapeutic value of games for myoelectric training, and presents insight that will be applicable to both future research on myoelectric training as well as aspects of training in clinical practice.
High-density force myography: A possible alternative for upper-limb prosthetic control
(Rehabilitation Research and Development Service, 2016) Radmand, Ashkan; Scheme, Erik; Englehart, Kevin
Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using high-density force myography (HD-FMG) for prosthetic control. HD-FMG uses a high-density array of pressure sensors to detect changes in the pressure patterns between the residual limb and socket caused by the contraction of the forearm muscles. In this work, HD-FMG outperforms the standard electromyography (EMG)-based system in detecting different wrist and hand gestures. With the arm in a fixed, static position, eight hand and wrist motions were classified with 0.33% error using the HD-FMG technique. Comparatively, classification errors in the range of 2.2%–11.3% have been reported in the literature for multichannel EMG-based approaches. As with EMG, position variation in HD-FMG can introduce classification error, but incorporating position variation into the training protocol reduces this effect. Channel reduction was also applied to the HD-FMG technique to decrease the dimensionality of the problem as well as the size of the sensorized area. We found that with informed, symmetric channel reduction, classification error could be decreased to 0.02%.

Browsing by Author "Scheme, Erik"

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