Angkoon Phinyomark

Gender and Age-Related Differences in Bilateral Lower Extremity Mechanics during Treadmill Running

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to their male counterparts. Thus, a comprehensive understanding of the sex-related differences in running kinematics is necessary. However, previous studies have either used discrete time point variables and inferential statistics and/or relatively small subject numbers. Therefore, the first purpose of this study was to use a principal component analysis (PCA) method along with a support vector machine (SVM) classifier to examine the differences in running gait kinematics between female and male runners across a large sample of the running population as well as between two age-specific sub-groups. Bilateral 3-dimensional lower extremity gait kinematic data were collected during treadmill running. Data were analysed on the complete sample (nu200a=u200a483: female 263, male 220), a younger subject group (nu200a=u200a56), and an older subject group (nu200a=u200a51). The PC scores were first sorted by the percentage of variance explained and we also employed a novel approach wherein PCs were sorted based on between-gender statistical effect sizes. An SVM was used to determine if the sex and age conditions were separable and classifiable based on the PCA. Forty PCs explained 84.74% of the variance in the data and an SVM classification accuracy of 86.34% was found between female and male runners. Classification accuracies between genders for younger subjects were higher than a subgroup of older runners. The observed interactions between age and gender suggest these factors must be considered together when trying to create homogenous sub-groups for research purposes.

A Review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition

Abstract Electric Power Wheelchairs (EPWs) are becoming increasingly important in assistive technology and rehabilitation devices. Normally EPWs are controlled by a joystick. However, this may not be suitable for disabled people who lack full control of their upper-limbs. Recent advances in the control of EPWs based on electromyography (EMG) signals are able to meet the needs of users with restricted limb movement and provide high performance control. Hence, EPWs controlled by EMG signals are highly appropriate for elderly and disabled users. The purpose of this article is to review the state-of-the-art of EMG controlled EPWs and to present the achievements so far in this technology. A study of a variety of methods for EMG-based control in literature was studied here. Two types of control methods for EPWs, pattern recognition and hybrid recognition systems are discussed. Four major criteria are applied to compare the quality of control resulting from the use of these control methods: Accuracy of control, response time or real-time operation, robustness, and intuitiveness of control. Based on these four criteria, the use of the support vector machine classifier using features based on the time domain such as mean absolute value, waveform length, and zero crossing are suggested for the pattern recognition method. Furthermore, a combination of the pattern recognition and non-pattern recognition methods is recommended in order to increase the control commands by use of a small number of muscle positions.

Gender differences in gait kinematics for patients with knee osteoarthritis

BackgroundFemales have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting results have been reported. Therefore, this study was designed to examine the differences in gait kinematics (1) between male and female subjects with and without knee OA and (2) between healthy gender-matched subjects as compared with their OA counterparts.MethodsOne hundred subjects with knee OA (45 males and 55 females) and 43 healthy subjects (18 males and 25 females) participated in this study. Three-dimensional kinematic data were collected during treadmill-walking and analysed using (1) a traditional approach based on discrete variables and (2) a machine learning approach based on principal component analysis (PCA) and support vector machine (SVM) using waveform data.ResultsOA and healthy females exhibited significantly greater knee abduction and hip adduction angles compared to their male counterparts. No significant differences were found in any discrete gait kinematic variable between OA and healthy subjects in either the male or female group. Using PCA and SVM approaches, classification accuracies of 98–100xa0% were found between gender groups as well as between OA groups.ConclusionsThese results suggest that care should be taken to account for gender when investigating the biomechanical aetiology of knee OA and that gender-specific analysis and rehabilitation protocols should be developed.

Gender differences in gait kinematics in runners with iliotibial band syndrome

Atypical running gait biomechanics are considered a primary factor in the etiology of iliotibial band syndrome (ITBS). However, a general consensus on the underpinning kinematic differences between runners with and without ITBS is yet to be reached. This lack of consensus may be due in part to three issues: gender differences in gait mechanics, the preselection of discrete biomechanical variables, and/or relatively small sample sizes. Therefore, this study was designed to address two purposes: (a) examining differences in gait kinematics for male and female runners experiencing ITBS at the time of testing and (b) assessing differences in gait kinematics between healthy gender‐ and age‐matched runners as compared with their ITBS counterparts using waveform analysis. Ninety‐six runners participated in this study: 48 ITBS and 48 healthy runners. The results show that female ITBS runners exhibited significantly greater hip external rotation compared with male ITBS and female healthy runners. On the contrary, male ITBS runners exhibited significantly greater ankle internal rotation compared with healthy males. These results suggest that care should be taken to account for gender when investigating the biomechanical etiology of ITBS.

Do intermediate- and higher-order principal components contain useful information to detect subtle changes in lower extremity biomechanics during running?

Recently, a principal component analysis (PCA) approach has been used to provide insight into running pathomechanics. However, researchers often account for nearly all of the variance from the original data using only the first few, or lower-order principal components (PCs), which are often associated with the most dominant movement patterns. In contrast, intermediate- and higher-order PCs are generally associated with subtle movement patterns and may contain valuable information about between-group variation and specific test conditions. Few investigations have evaluated the utility of intermediate- and higher-order PCs based on observational cross-sectional analyses of different cohorts, and no prior studies have evaluated longitudinal changes in an intervention study. This study was designed to test the utility of intermediate- and higher-order PCs in identifying differences in running patterns between different groups based on three-dimensional bilateral lower-limb kinematics. The results reveal that differences between sex- and age-groups of 128 runners were observed in the lower- and intermediate-order PCs scores (p<0.05) while differences between baseline and following a 6-week muscle strengthening program for 24 runners with patellofemoral pain were observed in the higher-order PCs scores (p<0.05), which exhibited a moderate correlation with self-reported pain scores (r=-0.43; p<0.05).

INVESTIGATING LONG-TERM EFFECTS OF FEATURE EXTRACTION METHODS FOR CONTINUOUS EMG PATTERN CLASSIFICATION

Based on recent advances in modern multifunction myoelectric control devices, a combination of effective feature extraction and classification methods is required to enhance the high classification performance, especially in accuracy viewpoint. However, for realizing practical applications of myoelectric control, the effect of long-term usage or reusability is one of the challenging issues that should be more carefully considered, whereas only a few works have investigated this effect in recent. In this study, the behavior of the state-of-the-art multiple feature extraction methods was investigated with the fluctuating electromyography (EMG) signals recorded during four different days with a large number of trials and subjects. To this end, seven multiple feature sets were compared consisting features based on time domain and time-scale representation. Two major points were emphasized: (1) the optimal robust feature set for continuous (both transient and steady-state signals) EMG pattern classification and (2) the effect of fluctuating EMG signals with feature extraction methods for long-term usage. From the classification results, time domain feature sets yielded better performance than time-scale feature sets. The classification accuracies of the time-domain-feature sets had always achieved above 80% by using linear discriminant analysis (LDA) as a classifier and uncorrelated LDA (ULDA) as a dimensionality reduction, whereas the classification accuracies of the time-scale-feature sets were lower than 70% for the fluctuating EMG signals. The effect of dimensionality reduction for the classification of fluctuating EMG signals was also discussed.

Robust Eye Movement Recognition Using EOG Signal for Human-Computer Interface

Electrooculography (EOG) signal is one of the useful biomedical signals. Development of EOG signal as a control signal has been paid more increasing interest in the last decade. In this study, we are proposing a robust classification algorithm of eight useful directional movements that it can avoid effect of noises, particularly eye-blink artifact. Threshold analysis is used to detect onset of the eye movements. Afterward, four beneficial time features are proposed that are peak and valley amplitude positions, and upper and lower lengths of two EOG channels. Suitable threshold conditions were defined and evaluated. From experimental results, optimal threshold values were selected for each parameters and classification accuracies approach to 100% for three subjects testing. To avoid the eye-blink artifact, the first derivative was additionally implemented.

Kinematic gait patterns in healthy runners: A hierarchical cluster analysis

Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.

ELECTROMYOGRAPHY (EMG) SIGNAL CLASSIFICATION BASED ON DETRENDED FLUCTUATION ANALYSIS

Electromyography (EMG) signal is a useful signal in various medical and engineering applications. To extract the useful information in the EMG signal, feature extraction method should be performed. The extracted features of the EMG signal are usually calculated based on linear or statistical methods, but the EMG signal exhibits the nonlinear and more complex in the properties. With recent advances in nonlinear analysis we are proposing the study of the EMG signals from upper-limb movements using Detrended Fluctuation Analysis (DFA) method. This study used EMG signals obtained from eight upper-limb movements and five muscle positions as representative EMG signals. The usefulness of the DFA method has been proposed to discriminate the upper-limb movements. Complete comparative studies of an optimal parameter of the DFA method were performed. From the viewpoints of maximum class separability, robustness, and complexity, scaling exponent obtained from the DFA method shows the appropriateness to be used as a feature in the classification of the EMG signal. From the experimental results, an optimal DFA method is obtained under these conditions: the minimum box size is approximately four, the maximum box size is one-tenth of the signal length, the box size increment is based on a power of two, and the quadratic polynomial fits is used in the fitting procedure. Moreover, the classification performance of the DFA method is better than other existing nonlinear methods, including the Higuchis method.

WAVELET-BASED DENOISING ALGORITHM FOR ROBUST EMG PATTERN RECOGNITION

A successful pre-processing stage based on wavelet denoising algorithm for electromyography (EMG) signal recognition is proposed. From the limitation of traditional universal wavelet denoising, the optimal weighted parameter is assigned for universal thresholding method. The optimal weight for increasing EMG recognition accuracy is 50–60% of traditional universal threshold with hard transformation. Experimental results show that it improved approximately from 2 to 50% of recognition accuracy for EMG with signal-to-noise ratio (SNR) in the range of 20 to 0 dB compared to a baseline system (without pre-processing stage) and traditional universal wavelet denoising. The results are evaluated through a large EMG dataset with seven kinds of hand movements and eight types of muscle positions.