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Novak A.C.,Toronto Rehabilitation Institute UHN | Mayich D.J.,A+ Network | Perry S.D.,Toronto Rehabilitation Institute UHN | Perry S.D.,Wilfrid Laurier University | And 2 more authors.
Foot and Ankle International

Motion of the foot and ankle has been traditionally investigated by modeling the foot as a single rigid segment that interacts with the lower leg. For example, the modified Helen Hayes foot model is commonly used in the literature to assess ankle motion during gait11 (Figure 1). Such an analysis permits identification of movement (kinematics), forces/ moments, and powers (kinetics) generated about the ankle in normal62,87 and pathological populations.11,63 Although relatively easy to perform and analyze, the basic information from single segment foot modeling does not provide observation of the complex motions that occur within the foot during human gait. Multisegment foot modeling addresses this problem by analyzing motion within the foot itself.7,18,40 Several multisegment gait models of the foot have been developed to date and are being used in clinical gait analysis. 19,22,33,40,40,52,73,79 Each model that has been developed has strengths and weaknesses. With multisegment foot modeling being reported with increasing frequency in the literature in the past few years, it is important for clinicians and researchers involved in assessment of foot and ankle dysfunction to gain a basic understanding of the benefits and limitations of multisegment foot modeling. © 2013 The Author(s). Source

Colantonio A.,University of Toronto | Colantonio A.,Toronto Rehabilitation Institute UHN | Kim H.,Daegu University | Allen S.,Dalhousie University | And 3 more authors.
Journal of Correctional Health Care

This study examined the proportion of men and women reporting previous traumatic brain injury (TBI) in an Ontario (Canada) prison sample by demographic characteristics; adverse life experiences; and criminal, drug, and alcohol use history. Using data from The Cost of Substance Abuse in Canada study based on a random sample from four Ontario prisons, this study found 50.4% of males and 38% of females reporting previous TBI. More TBIs occurred before the first crime for women than for men. Women with TBI experienced more early physical and sexual abuse than those without TBI. Additionally, this study shows high prevalence of early life experiences among persons, particularly women, with a history of TBI. Prisoners and prison staff should be educated on TBI and best practice for rehabilitation of TBI. © The Author(s) 2014. Source

Cammer A.,University of Saskatchewan | Morgan D.,University of Saskatchewan | Stewart N.,University of Saskatchewan | McGilton K.,Toronto Rehabilitation Institute UHN | And 3 more authors.

Purpose: Context is increasingly recognized as a key factor to be considered when addressing healthcare practice. This study describes features of context as they pertain to knowledge use in long-term care (LTC). Design and Methods: As one component of the research program Translating Research in Elder Care, an in-depth qualitative case study was conducted to examine the research question "How does organizational context mediate the use of knowledge in practice in long-term care facilities?" A representative facility was chosen from the province of Saskatchewan, Canada. Data included document review, direct observation of daily care practices, and interviews with direct care, allied provider, and administrative staff. Results: The Hidden Complexity of Long-Term Care model consists of 8 categories that enmesh to create a context within which knowledge exchange and best practice are executed. These categories range from the most easily identifiable to the least observable: physical environment, resources, ambiguity, flux, relationships, and philosophies. Two categories (experience and confidence, leadership and mentoring) mediate the impact of other contextual factors. Inappropriate physical environments, inadequate resources, ambiguous situations, continual change, multiple relationships, and contradictory philosophies make for a complicated context that impacts care provision. Implications: A hidden complexity underlays healthcare practices in LTC and each care provider must negotiate this complexity when providing care. Attending to this complexity in which care decisions are made will lead to improvements in knowledge exchange mechanisms and best practice uptake in LTC settings. © 2013 The Author. Source

Zhao S.,University of Toronto | Rudzicz F.,University of Toronto | Rudzicz F.,Toronto Rehabilitation Institute UHN
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

This paper presents a new dataset combining 3 modalities (EEG, facial, and audio) during imagined and vocalized phonemic and single-word prompts. We pre-process the EEG data, compute features for all 3 modalities, and perform binary classification of phonological categories using a combination of these modalities. For example, a deep-belief network obtains accuracies over 90% on identifying consonants, which is significantly more accurate than two baseline support vector machines. We also classify between the different states (resting, stimuli, active thinking) of the recording, achieving accuracies of 95%. These data may be used to learn multimodal relationships, and to develop silent-speech and brain-computer interfaces. © 2015 IEEE. Source

Frydenlund A.,University of Toronto | Rudzicz F.,University of Toronto | Rudzicz F.,Toronto Rehabilitation Institute UHN
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

We present a multimodel system for independent affect recognition using deep neural networks. Using the DEAP data set, features are extracted from EEG and other physiological signals, as well as videos of participant faces. We introduce both a novel way of extracting video features using sum-product networks, and a unique method of creating extra training examples from data that would have otherwise been lost in downsampling. Deep neural networks are used for estimating the emotional dimensions of arousal, valence, and dominance, along with favourability and familiarity. This work lays the foundation for future work in estimating emotional responses from physiological measurements. © Springer International Publishing Switzerland 2015. Source

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