Journal Articles

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Articles. Typically the realization of research papers reporting original research findings published in a journal issue. (URI: http://purl.org/coar/resource_type/c_6501) Item types include:

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How do authentic, empowering leaders influence new graduate nurses’ burnout development, job satisfaction, and quality of care? Examining the role of short-staffing and work-life interference
(Wiley, 2017) Boamah, Sheila A.; Read, Emily A.; Laschinger, Heather K. Spence
Aim: To test a hypothesized model linking new graduate nurses’ perceptions of their manager’s authentic leadership behaviours to structural empowerment, short-staffing, and work-life interference, and subsequent burnout, job satisfaction, and patient care quality. Background: Authentic leadership and structural empowerment have been shown to reduce early career burnout among nurses. Short-staffing and work-life interference are also linked to burnout and may help explain the impact of positive, empowering leadership on burnout, which in turn influences job satisfaction and patient care quality. Design: A time-lagged study of Canadian new graduate nurses was conducted. Methods: At Time 1, surveys were sent to 3,743 nurses (November 2012 to March 2013) and 1,020 were returned (27.3% response rate). At Time 2 (May to July 2014), 406 nurses who responded at Time 1 completed surveys (39.8% response rate). Descriptive analysis was conducted in SPSS. Structural equation modeling in Mplus was used to test the hypothesized model. Results: The hypothesized model was supported. Authentic leadership had a significant positive effect on structural empowerment, which in turn, decreased both short-staffing and work-life interference. Short-staffing and work-life imbalance subsequently resulted in nurse burnout, lower job satisfaction, and lower patient care quality one year later. Conclusion: The findings suggest that short-staffing and work-life interference are important factors influencing new graduate nurse burnout. Developing nurse managers’ authentic leadership behaviours and working with them to create and sustain empowering work environments may help reduce burnout, increase nurse job satisfaction and improve patient care quality.
The effects of authentic leadership and occupational coping self-efficacy on new graduate nurses’ burnout and mental health: A cross-sectional study
(Elsevier, 2015) Laschinger, Heather K. Spence; Borgogni, Laura; Consiglio, Chiara; Read, Emily
Background – New nurse burnout has personal and organizational costs. The combined effect of authentic leadership, person-job fit within areas of worklife, and occupational coping self-efficacy on new nurses’ burnout and emotional wellbeing has not been investigated. Objectives - This study tested a model linking authentic leadership, areas of worklife, occupational coping self-efficacy, burnout, and mental health among new graduate nurses. We also tested the validity of the concept of interpersonal strain at work as a facet of burnout. Design – A cross-sectional national survey of Canadian new graduate nurses was conducted. Participants – Registered nurses working in direct patient care in acute care settings with less than 3 years of experience were selected from provincial registry databases of 10 Canadian provinces. A total of 1009 of 3743 surveyed new graduate nurses were included in the final sample (useable response rate 27%). Methods - Participants received a mail survey package that included a letter of information, study questionnaire, and a $2 coffee voucher. To optimize response rates non-responders received a reminder letter four weeks after the initial mailing, followed by a second survey package four weeks after that. Ethics approval was obtained from the university ethics board prior to starting the study. Descriptive statistics and scale reliabilities were analyzed. Structural equation modeling with maximum likelihood estimation was used to test the fit between the data and the hypothesized model and to assess the factor structure of the expanded burnout measure. Results - The hypothesized model was an acceptable fit for the data (χ2 (164) = 1221.38; χ2 ratio =7.447; CFI =.921; IFI =.921; RMSEA =.08). All hypothesized paths were significant. Authentic leadership had a positive effect on areas of worklife, which in turn had a positive effect on occupational coping self-efficacy, resulting in lower burnout, which was associated with poor mental health. Conclusions - Authentic leaders may play an important role in creating positive working conditions and strengthening new nurses’ confidence that help them cope with job demands, thereby protecting them from developing burnout and poor mental health. Leadership training to develop supervisors’ authentic leadership skills may promote the development of person-job fit, thereby increasing occupational self-efficacy and new nurses’ wellbeing. Keywords: authentic leadership, areas of worklife, new graduate nurses, occupational coping self-efficacy, burnout, mental health
The influence of authentic leadership and empowerment on nurses’ relational social capital, mental health, and job satisfaction over the first year of practice
(Wiley, 2015) Read, Emily A.; Laschinger, Heather K.S.
Aims: To examine a theoretical model testing the effects of authentic leadership, structural empowerment, and relational social capital on the mental health and job satisfaction of new graduate nurses over the first year of practice. Background: Relational social capital is an important interpersonal organizational resource that may foster new graduate nurses’ workplace wellbeing and promote retention. Evidence shows that authentic leadership and structural empowerment are key aspects of the work environment that support new graduate nurses, however the mediating role of relational social capital has yet to be explored. Design: A longitudinal survey design was used to test the hypothesized model. Methods: One hundred ninety-one new graduate nurses in Ontario with <2 years of experience completed mail surveys in Jan-March 2010 and 1 year later in 2011. Path analysis using structural equation modeling was used to test the theoretical model. Results: Participants were mostly female, working full-time in medicine/surgery or critical care. All measures demonstrated acceptable reliability and validity. Path analysis results supported our hypothesized model; structural empowerment mediated the relationship between authentic leadership and nurses’ relational social capital, which in turn had a negative effect on mental health symptoms and a positive effect on job satisfaction. All indirect paths in the model were significant. Conclusion: By creating structurally empowering work environments, authentic leaders foster relational social capital among new graduate nurses leading to positive health and retention outcomes
Workplace social capital in nursing: an evolutionary concept analysis
(Wiley, 2014) Read, Emily A.
Aim: To report an analysis of the concept of nurses’ workplace social capital. Background: Workplace social capital is an emerging concept in nursing with potential to illuminate the value of social relationships at work. A common definition is needed. Design: Concept analysis Data sources: The Cumulative Index to Nursing and Allied Health Literature, PubMed, PsychINFO, and ProQuest Nursing. Review methods: Databases were systematically searched using the keywords: workplace social capital, employee social capital, work environment, social capital, and nursing published between January 1937 and November 2012 in English that described or studied social capital of nurses at work were included. A total of 668 resources were found. After removing 241 duplicates, literature was screened in two phases: 1) titles and abstracts were reviewed (n = 427), and 2) remaining data sources were retrieved and read (n = 70). Eight sources were included in the final analysis. Results: Attributes of nurses’ workplace social capital included networks of social relationships at work, shared assets, and shared ways of knowing and being. Antecedents were communication, trust, and positive leadership practices. Nurses’ workplace social capital was associated with positive consequences for nurses, their patients, and healthcare organizations. Conclusion: Nurses’ workplace social capital is defined as nurses’ shared assets and ways of being and knowing that are evident in and available through nurses’ networks of social relationships at work. Future studies should examine and test relationships between antecedents and consequences of nurses’ workplace social capital in order to better understand this important aspect of healthy professional practice environments.
Feasibility of the Diabetes and Technology for Increased Activity (DaTA) Study: A Pilot Intervention in High-Risk Rural Adults
(Human Kinetics Journals, 2014) Read, Emily
Background: Rural Canadians are at increased risk of metabolic syndrome. Physical inactivity is a primary target for preventing and reversing metabolic syndrome. Adherence to lifestyle interventions may be enhanced using cell phones and self-monitoring technologies. This study investigated the feasibility of a physical activity and self-monitoring intervention targeting high-risk adults in rural Ontario. Methods: Rural adults (n = 25, M=57.0 ± 8.7 years) with ≥2 criteria for metabolic syndrome participated in an 8-week stage-matched physical activity and self-monitoring intervention. Participants monitored blood glucose, blood pressure, weight, and physical activity using self-monitoring devices and BlackberryTM Smart phones. VO2max, stage of change, waist circumference, weight, blood lipids, and HbA1c were measured at weeks 1, 4, and 8. Results: Adherence to self-monitoring was >94%. Participants’ experiences and perceptions of the technology were positive. Mean stage of change increased 1 stage, physical activity increased 26%, and predicted VO2max increased 17% (p<0.05). Significant changes in weight, waist circumference, diastolic blood pressure, LDL cholesterol, and total cholesterol were found. Conclusions: This stage-matched technology intervention for increased physical activity was feasible and effective. Keywords: Metabolic syndrome, physical activity, cardiovascular health, technology, ruralhealth
Transition experiences of new graduate nurses from accelerated and traditional nursing programs
(Elsevier, 2017) Read, Emily; Laschinger, Heather K.S.
Background: With increasing numbers of new graduate nurses from accelerated nursing programs entering the workforce, it is important to understand their transition experiences, as they may differ from those of traditional graduates. Objectives: The aim of this study was to describe and compare the intrapersonal resources, transition experiences, and retention outcomes of these two groups. Design: A descriptive cross-sectional comparison study was conducted. Participants: A random sample of 3655 registered nurses with < 3 years of nursing experience were invited to participate from across Canada; 1020 responded (27.9%). The final sample included 230 nurses from accelerated programs and 768 from four-year programs (total n = 998). Methods: Following ethics approval, participants were mailed a questionnaire to their home address. One month later non-responders were sent a reminder letter, followed by a second questionnaire one month later (January to March, 2013). Descriptive statistics were conducted using SPSS. Group differences were assessed using independent samples t-tests for continuous variables and χ2 tests for categorical variables. Results: Overall, there were few significant differences between new graduate nurses from accelerated and traditional programs. Nurses in both groups had high levels of intrapersonal resources, positive transition experiences, were satisfied with their jobs and their choice of nursing as a career, and their intentions to leave were low. Conclusions: All new graduate nurses need to have a strong educational preparation and transition support, regardless of their age and previous work and career experiences.
New nurses’ perceptions of professional practice behaviours, quality of care, job satisfaction and career retention
(Wiley, 2016) Laschinger, Heather K. Spence; Zhu, Junhong; Read, Emily
Aim: To test a model examining the effects of structural empowerment and support for professional practice on new graduate nurses’ perceived professional practice behaviours, perceptions of care quality, and subsequent job satisfaction and career turnover intention. Background: The Nursing Worklife Model describes the relationship between environmental factors that support nursing practice and nurse and patient outcomes. The influence of support for professional practice on new nurses’ perceptions of professional nursing behaviours within this model has yet to be tested. Method: Structural equation modeling in Amos software was used to analyze data from a national survey of new graduate nurses across Canada (n = 393). Findings: The model fit the data reasonably well: χ² (124) = 360.054, χ/df=2.904, CFI=. 913, IFI=. 914, RMSEA=.070. The results supported our hypothesized model. The professional practice behaviours, as an individual contributor, mediated the relationship between organizational empowerment, support for professional practice and quality of care, which in turn negatively associated with career turnover intention among new nurses. All paths in the model were significant. Conclusion: The results suggest that job satisfaction and career retention of new nurses are related to their perceptions of work environment factors that support their professional practice behaviours and high quality care. Implications for nursing managers: To diminish nurse job dissatisfaction and intention of career turnover, and to enable them to deliver high quality patient care, nurse managers need to encourage individual professional behaviours, and employ organizational empowerment strategies to support nurses’ professional practice. Keywords: empowerment, nursing, professional practice behaviours, patient care quality, job satisfaction, career turnover
Conservative analytical assessments of localized RF exposure from small magnetic field sources
(Institute of Electrical and Electronics Engineers (IEEE), 2024-04-18) Rouse, Chris D.
A conservative analytical framework for assessing radiofrequency (RF) exposure from small magnetic field sources near the body is presented, with an emphasis on the 3kHz to 10MHz frequency range. Worst-case exposure models are proposed and analyzed for both homogeneous and heterogeneous tissue based on source dimensions, drive current, and separation distance. Electromagnetic analysis of induced field enhancements due to tissue heterogeneity is presented. Maximum drive currents for compliance with the basic restrictions are obtained for both tissue models. In the heterogeneous case, field enhancement in thin regions of low conductivity leads to significantly lower allow- able drive levels for nerve stimulation (NS) compliance. Guidance is provided regarding how to account for such enhancements for various internal E-field calculation methods. The impact of these field enhancements on 10-g specific absorption rate (SAR) is found to be small, i.e., the homogeneous tissue assumption appears to be sufficiently conservative. A small enhancement factor may be appropriate for 1-g SAR. The benefit of assessing against the basic restrictions instead of the reference levels is also explored. This work can be leveraged by regulatory and standardization bodies to develop exemption levels for small magnetic field sources, e.g., inductive chargers, to significantly reduce compliance burdens.
Essential requirements for the governance and management of data trusts, data repositories, and other data collaborations
(Swansea University, 2023-09-20) Paprica, P. Alison; Crichlow, Monique; Curtis Maillet, Donna; Kesselring, Sarah; Pow, Conrad; Scarnecchia, Thomas P.; Schull, Michael J.; Cartagena, Rosario G.; Cumyn, Annabelle; Dostmohammad, Salman; Elliston, Keith O.; Griever, Michelle; Hawn Nelson, Amy; Hill, Sean L.; Isaranuwatcha, Wanrudee; Loukipoudis, Evgueni; McDonald, James Ted; McLaughlin, John R.; Rabinowitz, Alan; Razak, Fahad; Verhulst, Stefaan G.; Verma, Amol A.; Victor, J. Charles; Young, Andrew; Yu, Joanna; McGrail, Kimberlyn
Introduction Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure. Approach and Aims We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs. Results Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty. Conclusions The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.
Double Disparity of Sexual Minority Status and Rurality in Cardiometabolic Hospitalization Risk: A Secondary Analysis Using Linked Population-Based Data
(MDPI, 2023-10-30) Gupta, Neeru; Cookson, Samuel R.
Studies have shown separately that sexual minority populations generally experience poorer chronic health outcomes compared with those who identify as heterosexual, as do rural populations compared with urban dwellers. This Canadian national observational study explored healthcare patterns at the little-understood intersections of lesbian, gay, or bisexual (LGB) identity with residence in rural and remote communities, beyond chronic disease status. The secondary analysis applied logistic regressions on multiple linked datasets from representative health surveys, administrative hospital records, and a geocoded index of community remoteness to examine differences in the risk of potentially avoidable cardiometabolic-related hospitalization among adults of working age. Among those with an underlying cardiometabolic condition and residing in more rural and remote communities, a significantly higher hospitalization risk was found for LGB-identified persons compared with their heterosexual peers (odds ratio: 4.2; 95% confidence interval: 1.5–11.7), adjusting for sociodemographic characteristics, behavioral risk factors, and primary healthcare access. In models stratified by sex, the association remained significant among gay and bisexual men (5.6; CI: 1.3–24.4) but not among lesbian and bisexual women (3.5; CI: 0.9–13.6). More research is needed leveraging linkable datasets to better understand the complex and multiplicative influences of sexual minority status and rurality on cardiometabolic health to inform equity-enhancing preventive healthcare interventions.
Comparison of socio-economic determinants of COVID-19 testing and positivity in Canada: A multi-provincial analysis
(PLOS, 2023-08-23) Antonova, Lilia; Somayaji, Chandy; Cameron, Jillian; Sirski, Monica; Sundaram, Maria E.; McDonald, James Ted; Mishra, Sharmistha; Kwong, Jeffrey C.; Katz, Alan; Baral, Stefan; Caulley, Lisa; Calzavara, Andrew; Corsten, Martin; Johnson-Obaseki, Stephanie
The effects of the COVID-19 pandemic have been more pronounced for socially disadvantaged populations. We sought to determine how access to SARS-CoV-2 testing and the likelihood of testing positive for COVID-19 were associated with demographic factors, socioeconomic status (SES) and social determinants of health (SDH) in three Canadian provinces.
Novel wearable HD-EMG sensor with shift-robust gesture recognition using deep learning
(IEEE, 2023-09-11) Chamberland, Félix; Buteau, Étienne; Tam, Simon; Campbell, Evan; Mortazavi, Ali; Scheme, Erik; Fortier, Paul; Boukadoum, Mounir; Campeau-Lecours, Alexandre; Gosselin, Benoit
In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyography (HD-EMG) sensor called EMaGer. The stretchable, wearable sensor adapts to different forearm sizes while maintaining uniform electrode density around the limb. Leveraging this uniformity, we propose novel array barrel-shifting data augmentation (ABSDA) approach used with a convolutional neural network (CNN), and an anti-aliased CNN (AA-CNN), that provides shift invariance around the limb for improved classification robustness to electrode movement, forearm orientation, and inter-session variability. Signals are sampled from a 4×16 HD-EMG array of electrodes at a frequency of 1 kHz and 16-bit resolution. Using data from 12 non-amputated participants, the approach is tested in response to sensor rotation, forearm rotation, and inter-session scenarios. The proposed ABSDA-CNN method improves inter-session accuracy by 25.67% on average across users for 6 gesture classes compared to conventional CNN classification. A comparison with other devices shows that this benefit is enabled by the unique design of the EMaGer array. The AA-CNN yields improvements of up to 63.05% accuracy over non-augmented methods when tested with electrode displacements ranging from −45 ∘ to +45 ∘ around the limb. Overall, this article demonstrates the benefits of co-designing sensor systems, processing methods, and inference algorithms to leverage synergistic and interdependent properties to solve state-of-the-art problems.
Analyzing the impact of class transitions on the design of pattern recognition-based myoelectric control schemes
(Elsevier, 2022-01) Raghu, Shriram Tallam Puranam; MacIsaac, Dawn T.; Scheme, Erik J.
Despite continued efforts to improve classification accuracy, it has been reported that offline accuracy is a poor indicator of the usability of pattern recognition-based myoelectric control. One potential source of this disparity is the existence of transitions between contraction classes that happen during regular use and are reported to be problematic for pattern recognition systems. Nevertheless, these transitions are often ignored or undefined during both the training and testing processes. In this work, we propose a set of metrics for analyzing the transitions that occur during the voluntary changes between contraction classes during continuous control. These metrics quantify the common types of errors that occur during transitions and compare them to existing metrics that apply only to the steady-state portions of the data. We then use these metrics to analyze transition characteristics of 6 commonly used classifiers on a novel dataset that includes continuous transitions between all combinations of seven different contraction classes. Results show that a linear discriminant classifier consistently outperforms other conventional classifiers during both transitions and steady-state conditions, despite having an almost identical offline performance. Results also show that, although offline training metrics correlate with steady-state performance, they do not correlate with transition performance. These insights suggest that the proposed set of metrics could provide a shift in perspective on the way pattern recognition systems are evaluated and provide a more representative picture of a classifier’s performance, potentially narrowing the gap between offline performance and online usability.
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.
Myoelectric control with fixed convolution-based time-domain feature extraction: exploring the spatio–temporal interaction
(IEEE, 2022-02-24) Khushaba, Rami N.; Al-Timemy, Ali H.; Samuel, Oluwarotimi Williams; Scheme, Erik J.
The role of feature extraction in electromyogram (EMG) based pattern recognition has recently been emphasized with several publications promoting deep learning (DL) solutions that outperform traditional methods. It has been shown that the ability of DL models to extract temporal, spatial, and spatio–temporal information provides significant enhancements to the performance and generalizability of myoelectric control. Despite these advancements, it can be argued that DL models are computationally very expensive, requiring long training times, increased training data, and high computational resources, yielding solutions that may not yet be feasible for clinical translation given the available technology. The aim of this paper is, therefore, to leverage the benefits of spatio–temporal DL concepts into a computationally feasible and accurate traditional feature extraction method. Specifically, the proposed novel method extracts a set of well-known time-domain features into a matrix representation, convolves them with predetermined fixed filters, and temporally evolves the resulting features over a short and long-term basis to extract the EMG temporal dynamics. The proposed method, based on Fixed Spatio–Temporal Convolutions, offers significant reductions in the computational costs, while demonstrating a solution that can compete with, and even outperform, recent DL models. Experimental tests were performed on sparse-and high-density EMG (HD-EMG) signals databases, across a total of 44 subjects performing a maximum of 53 movements. Despite the simplification compared to deep approaches, our results show that the proposed solution significantly reduces the classification error rates by 3% to 10% in comparison to recent DL models, while being efficient for real-time implementations.
Automation of the Timed-Up-and-Go test using a conventional video camera
(IEEE, 2019-08-09) Savoie, Patrick; Cameron, James A. D.; Kaye, Mary E.; Scheme, Erik J.
The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more resolved information about its sub-phases. While some approaches have shown promise, they often require the donning of sensors or the use of specialized hardware, such as the now discontinued Microsoft Kinect, which combines video information with depth sensors (RGBD). In this work, we leverage recent advances in computer vision to automate the TUG test using a regular RGB video camera without the need for custom hardware or additional depth sensors. Thirty healthy participants were recorded using a Kinect V2 and a standard video feed while performing multiple trials of 3 and 1.5 meter versions of the TUG test. A Mask Regional Convolutional Neural Net (R-CNN) algorithm and a Deep Multitask Architecture for Human Sensing (DMHS) were then used together to extract global 3D poses of the participants. The timing of transitions between the six key movement phases of the TUG test were then extracted using heuristic features extracted from the time series of these 3D poses. The proposed video-based vTUG system yielded the same error as the standard Kinect-based system for all six key transitions points, and average errors of less than 0.15 seconds from a multi-observer hand labeled ground truth. This work describes a novel method of video-based automation of the TUG test using a single standard camera, removing the need for specialized equipment and facilitating the extraction of additional meaningful information for clinical use.
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.
A symmetrical component feature extraction method for fault detection in induction machines
(IEEE, 2019-09) St-Onge, Xavier F.; Cameron, James; Saleh, Saleh; Scheme, Erik J.
Induction motors (IMs) are among the fully developed electromechanical technologies that are still in use today. Over the course of the last century, their structure, control, and operation have been undergone through several stages of development. Among stages of development, the automated control and continuous monitoring of IMs has benefited from the emergence of modern artificial intelligence (AI) methods. IM automation schemes have demonstrated the ability to provide machine fault detection and diagnosis (FDD) function. AI-based FDD methods in IMs have employed frequency-domain, time-frequency, and time-domain analyses as the basis of their feature extraction schemes. A promising feature extraction scheme is one that uses symmetrical components (SCs) in time-domain FDD systems. Current SC-based approaches, however, are limited in their generalizability to different fault classes, may require detailed machine models, and can suffer from computational limitations. In this paper, an improved feature extraction method that uses SCs for a pattern recognition based FDD scheme for three-phase (3φ) IMs will be presented. This novel feature extraction method will be implemented and verified experimentally to demonstrate high classification performance, increased generalizability, and low computational cost.
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.
On the usability of intramuscular EMG for prosthetic control: A Fitts’ Law approach
(Elsevier, 2014-10) Kamavuako, Ernest N.; Scheme, Erik J.; Kevin B., Englehart
Previous studies on intramuscular EMG based control used offline data analysis. The current study investigates the usability of intramuscular EMG in two degree-of-freedom using a Fitts’ Law approach by combining classification and proportional control to perform a task, with real time feedback of user performance. Nine able-bodied subjects participated in the study. Intramuscular and surface EMG signals were recorded concurrently from the right forearm. Five performance metrics (Throughput, Path efficiency, Average Speed, Overshoot and Completion Rate) were used for quantification of usability. Intramuscular EMG based control performed significantly better than surface EMG for Path Efficiency (80.5 ± 2.4% vs. 71.5 ± 3.8%, P = 0.004) and Overshoot (22.0 ± 3.0% vs. 45.1 ± 6.6%, P = 0.01). No difference was found between Throughput and Completion Rate. However the Average Speed was significantly higher for surface (51.8 ± 5.5%) than for intramuscular EMG (35.7 ± 2.7%). The results obtained in this study imply that intramuscular EMG has great potential as control source for advanced myoelectric prosthetic devices.

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