Domain adaptation for EEG-based, cross-subject epileptic seizure prediction.

The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common patient-specific approaches, which apply to each patient independently, often perform poorly for other patients due to the data variability. The aim of this study is to propose deep learning models which can handle this variability and generalize across various patients. This study addresses this challenge by introducing a novel cross-subject and multi-subject prediction models. Multiple-subject modeling broadens the scope of patient-specific modeling to account for the data from a dedicated ensemble of patients, thereby providing some useful, though relatively modest, level of generalization. The basic neural network architecture of this model is then adapted to cross-subject prediction, thereby providing a broader, more realistic, context of application. For accrued performance, and generalization ability, cross-subject modeling is enhanced by domain adaptation. Experimental evaluation using the publicly available CHB-MIT and SIENA data datasets shows that our multiple-subject model achieved better performance compared to existing works. However, the cross-subject faces challenges when applied to different patients. Finally, through investigating three domain adaptation methods, the model accuracy has been notably improved by 10.30% and 7.4% for the CHB-MIT and SIENA datasets, respectively.

A Method for Quantifying Back Flexion/Extension from Three Inertial Measurement Units Mounted on a Horse's Withers, Thoracolumbar Region, and Pelvis.

Back mobility is a criterion of well-being in a horse. Veterinarians visually assess the mobility of a horse's back during a locomotor examination. Quantifying it with on-board technology could be a major breakthrough to help them. The aim of this study was to evaluate the accuracy of a method of quantifying the back mobility of horses from inertial measurement units (IMUs) compared to motion capture (MOCAP) as a gold standard. Reflective markers and IMUs were positioned on the withers, eighteenth thoracic vertebra, and pelvis of four sound horses. The horses performed a walk and trot in straight lines and performed a gallop in circles on a soft surface. The developed method, based on the three IMUs, consists of calculating the flexion/extension angle of the thoracolumbar region. The IMU method showed a mean bias of 0.8° (±1.5°) (mean (±SD)) and 0.8° (±1.4°), respectively, for the flexion and extension movements, all gaits combined, compared to the MOCAP method. The results of this study suggest that the developed method has a similar accuracy to that of MOCAP, opening up possibilities for easy measurements under field conditions. Future studies will need to examine the correlations between these biomechanical measures and clinicians' visual assessment of back mobility defects.

Targeted exercises can improve biomechanical markers in individuals with knee osteoarthritis: A secondary analysis from a cluster randomized controlled trial.

BACKGROUND: It is not clear whether exercise therapy significantly improves knee biomechanics during gait in osteoarthritis (OA) patients. This study aimed to determine whether targeted exercises based on a knee kinesiography exam improve biomechanical markers (BMs) compared with conventional primary care (CPC) management. METHODS: This was a secondary analysis of a cluster randomized controlled trial in which patients were assigned to one of three groups: (1) Control (CPC), (2) Exercise, and (3) Exercise&Education. Fourteen known BMs in knee OA patients were assessed. The primary outcome was the global evolution ratio (GER), which was calculated as the sum of improved BMs over the sum of deteriorated BMs 6 months after baseline assessment. GER scores were categorized with three different sets of cut-off values into clinical levels: (a) Deteriorated, (b) Stabilized, and (c) Improved. Ordinal logistic regressions were performed on the per-protocol population to determine whether there was a relationship between group assignment and GER levels. RESULTS: Of the 221 eligible participants, 163 were included. Two different regression models showed that patients from Group 3 (Exercise&Education) were 2.5-times more likely to be in an upper GER level (i.e., Stabilized or Improved) than patients from the control group (both odds ratio (OR) > 2.46, Wald Χ2(1) ≥ 7.268, P ≤ 0.01). They also reported significantly more improvement in pain and function (Knee Injury and Osteoarthritis Outcome Score, both P ≤ 0.01). CONCLUSIONS: Results suggest that targeted exercises can improve biomechanical markers in knee OA patients compared with CPC treatment. Further studies are needed to confirm these findings and refine the biomechanical markers to address to maximize patients' clinical outcomes.

Biomechanical markers associations with pain, symptoms, and disability compared to radiographic severity in knee osteoarthritis patients: a secondary analysis from a cluster randomized controlled trial.

BACKGROUND: Conventional radiography is commonly used to diagnose knee osteoarthritis (OA), but also to guide clinical decision-making, despite a well-established discordance between radiographic severity and patient symptoms. The incidence and progression of OA is driven, in part, by biomechanical markers. Therefore, these dynamic markers may be a good metric of functional status and actionable targets for clinicians when developing conservative treatment plans. The aim of this study was to assess the associations between biomechanical markers and self-reported knee function compared to radiographic severity. METHODS: This was a secondary analysis of data from a randomized controlled trial (RCT) conducted in primary care clinics with knee OA participants. Correlation coefficients (canonical (ρ) and structural (Corr)) were assessed between the Knee Injury and Osteoarthritis Outcome Score (KOOS) and both, radiographic OA severity using the Kellgren-Lawrence grade, and three-dimensional biomechanical markers quantified by a knee kinesiography exam. Significant differences between coefficients were assessed using Fischer's z-transformation method to compare correlations from dependent samples. RESULTS: KOOS and biomechanical data were significantly more associated than KOOS and X-ray grading (ρ: 0.41 vs 0.20; p < 0.001). Structural correlation (Corr) between KOOS and X-ray grade was 0.202 (4% of variance explained), while individual biomechanical markers, such as the flexion during loading, explained up to 14% of KOOS variance (i.e., Corr2). Biomechanical markers showed the strongest associations with Pain and Activity of Daily Living KOOS subscales (both > 36% variance explained), while X-ray grading was most associated with Symptoms subscale (21% explained; all p ≤ 0.001). CONCLUSIONS: Knee biomechanical markers are associated with patient-reported knee function to a greater extent than X-ray grading, but both provide complementary information in the assessment of OA patients. Understanding how dynamic markers relate to function compared to radiographic severity is a valuable step towards precision medicine, allowing clinicians to refine and tailor therapeutic measures by prioritizing and targeting modifiable biomechanical markers linked to pain and function. TRIAL REGISTRATION: Original RCT was approved by the Research Ethics Boards of École de technologie supérieure (H20150505) and Centre hospitalier de l'Université de Montréal (CHUM-CE.14.339), first registered at https://www.isrctn.com/ (ID-ISRCTN16152290) on May 27, 2015.

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers.

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using an efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yielded an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down.

A Spectral-Based Approach for BCG Signal Content Classification.

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals-in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

Graphical-based multivariate analysis for knee joint clinical and kinematic data correlation assessment.

A large amount of data including joint kinematics, joint kinetics, clinical and functional measurements constitutes the clinical gait analysis basis which is a process whereby quantitative gait information are collected to aid in clinical decision-making. Therefore, better understanding the relationship between the biomechanical and clinical data for the knee osteoarthritis (OA) patient is for a relevant importance. It's the purpose of this paper, which aims to analyze and visualize the correlation structure between biomechanical characteristics and clinical symptoms, and thus to provide an additional knowledge from the coupling of these parameters that will be useful for the pathology assessment of knee-joint disease in the end-staged knee OA patients. We perform two multivariate statistical approaches, first, a Canonical Correlation Analysis (CCA) to assess the multivariate association and, second, a graphical- based representation of the multivariate correlation to better understand the association between these multivariate data. Results show the usefulness of using such multivariate approaches to highlight association and specific correlation structure between the features and to extract meaningful information.

A multi-arm cluster randomized clinical trial of the use of knee kinesiography in the management of osteoarthritis patients in a primary care setting.

Objective: An important clinical gap reported by primary care physicians (PCPs) in managing knee osteoarthritis patients is the lack of validated tools to help them guide conservative treatment decision-making. This study aimed at evaluating the clinical utility of adding to current medical management (CMM) by PCPs, a dynamic knee kinesiography (KneeKG) exam assessing biomechanical risk factors linked to osteoarthritis progression.Design: In this 6-month cluster randomized controlled trial, primary care clinics were randomized into three groups: 1-CMM by PCPs, 2-CMM+KneeKG, and 3-CMM+KneeKG+Education (a self-management education session and two follow-up group meetings). Primary outcomes were scores on the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscales and overall score.Results: Of the 894 patients referred from 87 clinics, 515 participated, 449 (87.2%) completed the study. At 6-month follow-up, patients in both KneeKG groups reported statistically significant improvement on the KOOS overall score (Group2: +5.5; Group3: +5.0), and on the symptoms, pain, and activities of daily living subscales compared to control group (all p < 0.05). They also reported significantly higher satisfaction levels with global care (both p < 0.01). Group 3-CMM+KneeKG+Education showed statistically significant improvements in objective functional tests as well as greater global impression of change in pain, function, quality of life, and global condition (all p < 0.05).Conclusions: Results demonstrated significant improvements in terms of pain, function, and satisfaction in KneeKG groups relative to the CMM. Adding education and supervision further improves clinical outcomes. These findings may support the added value of a KneeKG exam in assisting PCPs in the management of knee osteoarthritis patients.

Hip-Knee-Ankle (HKA) angle modification during gait in healthy subjects.

BACKGROUND: Achieving a neutral static Hip-Knee-Ankle angle (sHKA) measured on radiographs has been considered a factor of success for total knee arthroplasty (TKA). However, recent studies have shown that sHKA seems to have no effect on TKA survivorship. sHKA is not representative of the dynamic loading occurring during gait, unlike the dynamic HKA (dHKA). RESEARCH QUESTION: The primary objective was to see if the sHKA is predictive of the dynamic HKA (dHKA). A secondary objective was to document to what degree the dHKA changes during gait. METHODS: We analysed 3D knee kinematics during gait of a cohort of 90 healthy individuals with the KneeKG™ system. dHKA was calculated and compared with sHKA. Knees were considered "Stable" if the dHKA remained in valgus or varus for greater than 95% of the corresponding phase, and "Changer" otherwise. Patient characteristics of the Stable and Changer knees were compared to find associated factors. RESULTS: Absolute variation of dHKA during gait was 10.9 ± 5.3° for the whole cohort. The variation was less for the varus knees (10.3 ± 4.8°), than for the valgus knees (12.8 ± 6.1°, p = 0.008). We found low to moderate correlations (r = 0.266 to 0.553, p < 0.001) between sHKA and dHKA values for varus knees and no significant correlation for valgus knees. Twenty two percent (36/165) of the knees were considered Changers. The proportion of knees that were Changers was 15% of the varus versus 39% of the valgus (p < 0.001). SIGNIFICANCE: Lower limb radiographic measures of coronal alignment have limited value for predicting dynamic measures of alignment during gait.

Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review.

BACKGROUND: The purpose of this study is to review the current literature on knee joint biomechanical gait data analysis for knee pathology classification. The review is prefaced by a presentation of the prerequisite knee joint biomechanics background and a description of biomechanical gait pattern recognition as a diagnostic tool. It is postfaced by discussions that highlight the current research findings and future directions. METHODS: The review is based on a literature search in PubMed, IEEE Xplore, Science Direct, and Google Scholar on April 2019. Inclusion criteria admitted articles, written in either English or French, on knee joint biomechanical gait data classification in general. We recorded the relevant information pertaining to the investigated knee joint pathologies, the participants' attributes, data acquisition, feature extraction, and selection used to represent the data, as well as the classification algorithms and validation of the results. RESULTS: Thirty-one studies met the inclusion criteria for review. CONCLUSIONS: The review reveals that the importance of medical applications of knee joint biomechanical gait data classification and recent progress in data acquisition technology are fostering intense interest in the subject and giving a strong impetus to research. The review also reveals that biomechanical data during locomotion carry essential information on knee joint conditions to infer an early diagnosis. This survey paper can serve as a useful informative reference for research on the subject.

A multivariate relationship between the kinematic and clinical parameters of knee osteoarthritis population.

BACKGROUND: Biomechanical and clinical parameters contribute very closely to functional evaluations of the knee joint. To better understand knee osteoarthritis joint function, the association between a set of knee biomechanical data and a set of clinical parameters of an osteoarthritis population (OA) is investigated in this study. METHODS: The biomechanical data used here are a set of characteristics derived from 3D knee kinematic patterns: flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, all determined during gait recording. The clinical parameters include a KOOS questionnaire and the patient's demographic characteristics. Canonical correlation analysis (CCA) is used (1) to evaluate the multivariate relationship between biomechanical data and clinical parameter sets, and (2) to cluster the most correlated parameters. Multivariate models were created within the identified clusters to determine the effect of each parameter's subset on the other. The analyses were performed on a large database containing 166 OA patients. RESULTS: The CCA results showed meaningful correlations that gave rise to three different clusters. Multivariate linear models were found explaining the subjective clinical parameters by evaluating the biomechanical data contained within each cluster. CONCLUSION: The results showed that a multivariate analysis of the clinical symptoms and the biomechanical characteristics of knee joint function allowed a better understanding of their relationships.

An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls.

Three-dimensional (3D) knee kinematic data, measuring flexion/extension, abduction/adduction, and internal/external rotation angle variations during locomotion, provide essential information to diagnose, classify, and treat musculoskeletal knee pathologies. However, and so across genders, the curse of dimensionality, intra-class high variability, and inter-class proximity make this data usually difficult to interpret, particularly in tasks such as knee pathology classification. The purpose of this study is to use data complexity analysis to get some insight into this difficulty. Using 3D knee kinematic measurements recorded from osteoarthritis and asymptomatic subjects, we evaluated both single feature complexity, where each feature is taken individually, and global feature complexity, where features are considered simultaneously. These evaluations afford a characterization of data complexity independent of the used classifier and, therefore, provide information as to the level of classification performance one can expect. Comparative results, using reference databases, reveal that knee kinematic data are highly complex, and thus foretell the difficulty of knee pathology classification.

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems.

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes.

Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase.

OBJECTIVE: To investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers, as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) (Altman et al., 1986). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording. METHODS: 100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity. RESULTS: Diagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes. CONCLUSION: The results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial compartment knee OA.

Data correction to determine a representative pattern of a set of 3D knee kinematic measurements.

The purpose of this study is to determine a representative pattern of a set of three dimensional (3D) knee kinematic measurement curves recorded throughout several trials with a patient walking on a treadmill. The measurements are knee angles, (namely joint angles) with respect to the sagittal, frontal, and transverse planes, as a function of time during a gait cycle. Two serious difficulties met while extracting a representative pattern from the trials are that the curves possess phase variability and there are outliers. We propose a scheme which first removes outliers using the modified band depth index method, and follows with phase variability reduction by curve registration. This scheme leads to retaining the mean curve of the corrected set of curves, as the most representative.

A rule-based algorithm can output valid surgical strategies in the treatment of AIS.

BACKGROUND: Variability in surgical strategies for the treatment of adolescent idiopathic scoliosis (AIS) has been demonstrated despite the existence of classifications to guide selection of AIS curves to include in fusion. Decision trees and rule-based algorithms have demonstrated their potential to improve reliability of AIS classification because of their systematic approach and they have also been proposed in algorithms for selection of instrumentation levels in scoliosis. Our working hypothesis is that a rule-based algorithm with a knowledge base extracted from the literature can efficiently output surgical strategies alternatives for a given AIS case. Our objective is to develop a rule-based algorithm based on peer-reviewed literature to output alternative surgical strategies for approach and levels of fusion. METHODS: A literature search of all English Manuscripts published between 2000 and December 2009 with Pubmed and Google scholar electronic search using the following keywords: "adolescent idiopathic scoliosis" and "surgery" alternatively with "levels of fusion" or "approach". All returned abstracts were screened for contents that could contain rules to include in the knowledge base. A dataset of 1,556 AIS cases treated surgically was used to test the surgical strategy rule-based algorithm (SSRBA) and evaluate how many surgical treatments are covered by the algorithm. The SSRBA was programmed using Matlab. Descriptive statistic was used to evaluate the ability of the rule-based algorithm to cover all treatment alternatives. RESULTS: A SSRBA was successfully developed following Lenke classification's concept that the spine is divided into three curve segments [proximal thoracic (PT), main thoracic (MT) and thoracolumbar/lumbar (TL)]. Each of the 1,556 AIS patients in the dataset was ran through the SSRBA. It proposed an average of 3.78 (±2.06) surgical strategies per case. Overall, the SSRBA is able to match the treatment offered by the surgeon in approach and level of fusion 70 % of the time (with one vertebral level leeway). CONCLUSION: This study is to the author's knowledge the first attempt at proposing an algorithm to output all surgical alternatives for a given AIS case. It uses a rule-based algorithm with a knowledge base extracted from peer-reviewed literature in an area with great variability. When tested against a database of AIS patients treated surgically, the SSRBA developed has the ability to propose a surgical plan with respect to approach and levels of fusion that match the surgeon's plan in a great majority of cases. Since this SSRBA seems to output multiple valid surgical strategies, it could allow the comparisons of various strategies and the outcomes achieved in similar cases in large databases for a given case and guide surgical treatment.

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification.

BACKGROUND CONTEXT: Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns. PURPOSE: To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM. STUDY DESIGN: This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion. PATIENT SAMPLE: A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification. OUTCOME MEASURES: For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded. METHODS: A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map. RESULTS: The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types. CONCLUSIONS: An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.

Relationship between knee walking kinematics and muscle flexibility in runners.

CONTEXT: Decreased flexibility in muscles and joints of lower extremities is commonly observed in runners. Understanding the effect of decreased flexibility on knee walking kinematics in runners is important because, over time, altered gait patterns can make runners vulnerable to overuse injuries or degenerative pathologies. OBJECTIVES: To compare hamstring and iliotibial-band (ITB) flexibility and knee kinematics in runners and nonrunners. DESIGN: A descriptive, comparative laboratory study. SETTING: Hamstring and ITB flexibility were measured with the active knee-extension test and the modified Ober test, respectively, in both groups of participants. Three-dimensional (3D) walking kinematic data were then recorded at the knee using a motiontracking system. PARTICIPANTS: 18 runners and 16 nonrunners. MAIN OUTCOME MEASURES: Knee-extension angle (hamstring flexibility) and hip-adduction angle (ITB flexibility). Knee kinematic parameters of interest included knee angle at initial contact, peak knee angles, and knee-angle range in all planes of movement. RESULTS: The runners had a significantly less flexible ITB than the nonrunners (hip adduction [-] and adduction [+] angles, 3.1° ± 5.6° vs -.4° ± 4.5°; P < .001). The runners demonstrated a greater mean tibial external-rotation angle at initial contact (7.3° ± 5.8° vs 2.0° ± 4.0°; P = .01) and a smaller mean peak tibial internal-rotation angle (-1.6° ± 3.0° vs -4.2° ± 3.2°; P = .04) than the nonrunners. CONCLUSION: This study provides new insight into the relationship between muscle flexibility and 3D knee kinematics in runners. This supports the premise that there is an association between muscle flexibility and transverse-plane knee kinematics in this population.

Computer algorithms and applications used to assist the evaluation and treatment of adolescent idiopathic scoliosis: a review of published articles 2000-2009.

Adolescent idiopathic scoliosis (AIS) is a complex spinal deformity whose assessment and treatment present many challenges. Computer applications have been developed to assist clinicians. A literature review on computer applications used in AIS evaluation and treatment has been undertaken. The algorithms used, their accuracy and clinical usability were analyzed. Computer applications have been used to create new classifications for AIS based on 2D and 3D features, assess scoliosis severity or risk of progression and assist bracing and surgical treatment. It was found that classification accuracy could be improved using computer algorithms that AIS patient follow-up and screening could be done using surface topography thereby limiting radiation and that bracing and surgical treatment could be optimized using simulations. Yet few computer applications are routinely used in clinics. With the development of 3D imaging and databases, huge amounts of clinical and geometrical data need to be taken into consideration when researching and managing AIS. Computer applications based on advanced algorithms will be able to handle tasks that could otherwise not be done which can possibly improve AIS patients' management. Clinically oriented applications and evidence that they can improve current care will be required for their integration in the clinical setting.

Effects of physiotherapy treatment on knee osteoarthritis gait data using principal component analysis.

BACKGROUND: Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteoarthritis (OA) patients' gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. METHODS: Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treatment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment's effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. FINDINGS: Increased quadriceps and hamstring strength was observed following treatment (P<0.05). Except for the knee flexion/extension angle, two different groups (G(1) and G(2)) were extracted from the angle and moment data. When pre- and post-treatment analyses were performed considering the groups, participants exhibiting a G(2) knee moment pattern demonstrated a greater first peak flexion moment, lower adduction moment impulse and smaller rotation angle range post-treatment (P<0.05). When pre- and post-treatment comparisons were performed without grouping, the data showed no treatment effect. INTERPRETATION: The results of the present study suggest that the effect of physiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre- and post-treatment comparisons.