A comparison of lower limb EMG and ground reaction forces between barefoot and shod gait in participants with diabetic neuropathic and healthy controls

Background: It is known that when barefoot, gait biomechanics of diabetic neuropathic patients differ from nondiabetic individuals. However, it is still unknown whether these biomechanical changes are also present during shod gait which is clinically advised for these patients. This study investigated the effect of the participants own shoes on gait biomechanics in diabetic neuropathic individuals compared to barefoot gait patterns and healthy controls. Methods: Ground reaction forces and lower limb EMG activities were analyzed in 21 non-diabetic adults (50.9 +/- 7.3 yr, 24.3 +/- 2.6 kg/m(2)) and 24 diabetic neuropathic participants (55.2 +/- 7.9 yr, 27.0 +/- 4.4 kg/m(2)). EMG patterns of vastus lateralis, lateral gastrocnemius and tibialis anterior, along with the vertical and antero-posterior ground reaction forces were studied during shod and barefoot gait. Results: Regardless of the disease, walking with shoes promoted an increase in the first peak vertical force and the peak horizontal propulsive force. Diabetic individuals had a delay in the lateral gastrocnemius EMG activity with no delay in the vastus lateralis. They also demonstrated a higher peak horizontal braking force walking with shoes compared to barefoot. Diabetic participants also had a smaller second peak vertical force in shod gait and a delay in the vastus lateralis EMG activity in barefoot gait compared to controls. Conclusions: The change in plantar sensory information that occurs when wearing shoes revealed a different motor strategy in diabetic individuals. Walking with shoes did not attenuate vertical forces in either group. Though changes in motor strategy were apparent, the biomechanical did not support the argument that the use of shoes contributes to altered motor responses during gait.

Plantar Pressures During Shod Gait in Diabetic Neuropathic Patients with and without a History of Plantar Ulceration

Background: Diabetic neuropathy leads to progressive loss of sensation, lower-limb distal muscle atrophy, autonomic impairment, and gait alterations that overload feet. This overload has been associated with plantar ulcers even with consistent daily use of shoes. We sought to investigate and compare the influence of diabetic neuropathy and plantar ulcers in the clinical history of diabetic neuropathic patients on plantar sensitivity, symptoms, and plantar pressure distribution during gait while patients wore their everyday shoes. Methods: Patients were categorized into three groups: a control group (CG; n = 15), diabetic patients with a history of neuropathic ulceration (DUG; n = 8), and diabetic patients without a history of ulceration (DG; n = 10). Plantar pressure variables were measured by Pedar System shoe insoles in five plantar regions during gait while patients wore their own shoes. Results: No statistical difference between neuropathic patients with and without a history of plantar ulcers was found in relation to symptoms, tactile sensitivity, and duration of diabetes. Diabetic patients without ulceration presented the lowest pressure-time integral under the heel (72.1 +/- 16.1 kPa x sec; P=.0456). Diabetic patients with a history of ulceration presented a higher pressure-time integral at the midfoot compared to patients in the control group (59.6 +/- 23.6 kPa x sec x 45.8 +/- 10.4 kPa x sec; P = .099), and at the lateral forefoot compared to diabetic patients without ulceration (70.9 +/- 17.7 kPa sec x 113.2 +/- 61.1 kPa x sec, P = .0193). Diabetic patients with ulceration also presented the lowest weight load under the hallux (0.06 +/- 0.02%, P = .0042). Conclusions: Although presenting a larger midfoot area, diabetic neuropathic patients presented greater pressure-time integrals and relative loads over this region. Diabetic patients with ulceration presented an altered dynamic plantar pressure pattern characterized by overload even when wearing daily shoes. Overload associated with a clinical history of plantar ulcers indicates future appearance of plantar ulcers. (J Am Podiatr Med Assoc 99(4): 285-294, 2009)

Automated supervised classification of variable stars in the CoRoT programme Method and application to the first four exoplanet fields

Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.

TESTING STATISTICAL HYPOTHESIS ON RANDOM TREES AND APPLICATIONS TO THE PROTEIN CLASSIFICATION PROBLEM

Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statistical approach to test if two sets of protein domain sequences coming from two families of the Pfam database are significantly different. We model protein sequences as realizations of Variable Length Markov Chains (VLMC) and we use the context trees as a signature of each protein family. Our approach is based on a Kolmogorov-Smirnov-type goodness-of-fit test proposed by Balding et at. [Limit theorems for sequences of random trees (2008), DOI: 10.1007/s11749-008-0092-z]. The test statistic is a supremum over the space of trees of a function of the two samples; its computation grows, in principle, exponentially fast with the maximal number of nodes of the potential trees. We show how to transform this problem into a max-flow over a related graph which can be solved using a Ford-Fulkerson algorithm in polynomial time on that number. We apply the test to 10 randomly chosen protein domain families from the seed of Pfam-A database (high quality, manually curated families). The test shows that the distributions of context trees coming from different families are significantly different. We emphasize that this is a novel mathematical approach to validate the automatic clustering of sequences in any context. We also study the performance of the test via simulations on Galton-Watson related processes.

LIPSCHITZ CLASSIFICATION OF FUNCTIONS ON A HOLDER TRIANGLE

The problem of semialgebraic Lipschitz classification of quasihomogeneous polynomials on a Holder triangle is studied. For this problem, the ""moduli"" are described completely in certain combinatorial terms.

Laser-induced breakdown spectroscopy and chemometrics for classification of toys relying on toxic elements

Quality control of toys for avoiding children exposure to potentially toxic elements is of utmost relevance and it is a common requirement in national and/or international norms for health and safety reasons. Laser-induced breakdown spectroscopy (LIBS) was recently evaluated at authors` laboratory for direct analysis of plastic toys and one of the main difficulties for the determination of Cd. Cr and Pb was the variety of mixtures and types of polymers. As most norms rely on migration (lixiviation) protocols, chemometric classification models from LIBS spectra were tested for sampling toys that present potential risk of Cd, Cr and Pb contamination. The classification models were generated from the emission spectra of 51 polymeric toys and by using Partial Least Squares - Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA) and K-Nearest Neighbor (KNN). The classification models and validations were carried out with 40 and 11 test samples, respectively. Best results were obtained when KNN was used, with corrected predictions varying from 95% for Cd to 100% for Cr and Pb. (C) 2011 Elsevier B.V. All rights reserved.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

DOES THE CHRONOTYPE CLASSIFICATION NEED TO BE UPDATED? PRELIMINARY FINDINGS

Traditionally, chronotype classification is based on the Morningness-Eveningness Questionnaire (MEQ). It is implicit in the classification that intermediate individuals get intermediate scores to most of the MEQ questions. However, a small group of individuals has a different pattern of answers. In some questions, they answer as ""morning-types"" and in some others they answer as ""evening-types,"" resulting in an intermediate total score. ""Evening-type"" and ""Morning-type"" answers were set as A(1) and A(4), respectively. Intermediate answers were set as A(2) and A(3). The following algorithm was applied: Bimodality Index = (Sigma A(1) x Sigma A(4))(2) - (Sigma A(2) x Sigma A(3))(2). Neither-types that had positive bimodality scores were classified as bimodal. If our hypothesis is validated by objective data, an update of chronotype classification will be required. (Author correspondence: brunojm@ymail.com)

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Strength training improves fall-related gait kinematics in the elderly: a randomized controlled trial

Background: Falls are one of the greatest concerns among the elderly. Among a number of strategies proposed to reduce the risk of falls, improving muscle strength has been applied as a successful preventive strategy. Although it has been suggested as a relevant strategy, no studies have analyzed how muscle strength improvements affect the gait pattern. The aim of this study was to determine the effects of a lower limb strength training program on gait kinematics parameters associated with the risk of falls in elderly women. Methods: Twenty seven elderly women were assigned in a balance and randomized order into an experimental (n = 14: age = 61.1 (4.3) years, BMI = 26.4 (2.8) kg m(-2)) and a control (n = 13; age = 61.6 (6.6) years; BMI = 25.9 (3.0) kg m(-2)) group. The EG performed lower limb strength training during 12 weeks (3 days per week), being training load increased weekly. Findings: Primary outcomes were gait kinematics parameters and maximum voluntary isometric contractions at pre- and post-training period. Secondary outcomes were training load improvement weekly and one repetition maximum every two weeks. The I maximal repetition increment ranged from 32% to 97% and was the best predictor of changes in gait parameters (spatial, temporal and angular variables) after training for the experimental group. Z-score analysis revealed that the strength training was effective in reversing age-related changes in gait speed, stride length, cadence and toe clearance, approaching the elderly to reference values for healthy young women. Interpretation: Lower limb strength training improves fall-related gait kinematic parameters. Thus, strength training programs should be recommended to the elderly women in order to change their gait pattern towards young adults. (C) 2009 Elsevier Ltd. All rights reserved.

Transient effects of stretching exercises on gait parameters of elderly women

This study aimed to analyse the effects of a single stretching exercise session on a number of gait parameters ill elderly participants in all attempt to determine whether these exercises can influence the risk of fall. Fifteen healthy women living in the community Volunteered to participate in the study. A kinematic gait analysis was performed immediately before and after a session of static stretching exercises applied oil hip flexor/extensor muscles. Results showed a significant influence of stretching exercises on a number of gait parameters, which have previously been proposed as fall predictors. Participants showed increased gait velocity, greater step length and reduced double Support time during stance after performing stretching exercises, suggesting improved stability and mobility. Changes around the pelvis (increased anterior-posterior tilt and rotation range of motion) resulting from the stretching exercises were suggested to influence the gait parameters (velocity, step length and double support time). Therefore, stretching exercises were shown to be a promising strategy to facilitate changes in gait parameters related to the risk of fall. Some other gait variables related to the risk of fall remained Unaltered (e.g., toe clearance). The stable pattern of segmental angular velocities was proposed to explain the stability of these unchanged gait variables. The results indicate that stretching exercises, performed oil a regular (daily) basis, result in gait adaptations which can be considered as indicative of reduced fall risk. Other Studies to determine whether regular stretching routines are an effective strategy to reduce the risk of fall are required. (C) 2008 Elsevier Ltd. All rights reserved.

Classification of normal swallowing and oropharyngeal dysphagia using wavelet

Oropharyngeal dysphagia is characterized by any alteration in swallowing dynamics which may lead to malnutrition and aspiration pneumonia. Early diagnosis is crucial for the prognosis of patients with dysphagia, and the best method for swallowing dynamics assessment is swallowing videofluoroscopy, an exam performed with X-rays. Because it exposes patients to radiation, videofluoroscopy should not be performed frequently nor should it be prolonged. This study presents a non-invasive method for the pre-diagnosis of dysphagia based on the analysis of the swallowing acoustics, where the discrete wavelet transform plays an important role to increase sensitivity and specificity in the identification of dysphagic patients. (C) 2008 Elsevier Inc. All rights reserved.

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.

Electrical stimulation and treadmill gait in tetraplegic patients: assessment of its effects on the knee with magnetic resonance imaging

Study design: Evaluation of knees of tetraplegic patients who have been walking for several months with the aid of a system that involves neuromuscular stimulation, treadmill and a harness support device. Objectives: To investigate if the training program could cause knee injury to tetraplegic patients. Setting: Hospital das Clinicas - UNICAMP. Campinas-SP, Brazil. Methods: Nine patients were evaluated. Clinical exam and magnetic resonance images (MRIs) were used for evaluation. MRIs were taken before and after the training program, in a 6-month interval for each patient. There were two sessions of training every week. Each session lasted 20 min. Results: No severe clinical abnormality was observed in any patient. Mild knee injury was observed in four of nine patients studied. Conclusions: Tetraplegic patients undergoing treadmill gait training deserve a close follow-up to prevent knee injury.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011