Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

Key parameters and sensitivity analysis of lower limb muscle strength in young men with different gait patterns

OBJECTIVE

To explore the functional characteristics and principal component differences of electromyography in different phases of the gait cycle, to provide key parameters for identifying a complete gait, and to provide a reference for joint moment solving in the lower limb.

METHODS

Twenty young men were selected to measure the natural gait EMG of 14 muscles of the lower limb using VICON and NORAXON devices. Gait was classified into two categories according to the Niyogi S A classification, integral EMG differences were compared, and principal component analysis was performed on the differing muscles to calculate Cohen's d values for significant differences and ΔIEMG values for non-significant differences.

RESULTS

(1) Significant differences existed in the integral EMG of the left semitendinosus, right semitendinosus, right biceps femoris, and left gastrocnemius muscles, both lateral and medial. (2) Principal component analysis showed significant differences in the left semitendinosus for principal component five (P < 0.1, ES = 1.40); right biceps femoris for principal component three (ES = 0.63, 10%-30%); and left gastrocnemius medial for principal component four (P < 0.05, ES = 1.81, 40%-60%). The ΔIEMG% of the right semitendinosus principal components I-IV were 97.96%, 92.24%, 87.26%, and 75.08%, respectively; and the ΔIEMG% of the left gastrocnemius medial principal components I-IV were 90.95%, 75.08%, 96.37%, and 85.39%, respectively.

CONCLUSION

(1) Left semitendinosus, right semitendinosus, right biceps femoris, and left gastrocnemius can be used as the main muscles for gait recognition. (2) The left semitendinosus principal component V, the right biceps femoris principal component III, and the left gastrocnemius medial principal component IV are sensitive indicators for gait stage classification.

Effects of muscle strength in different parts of adolescent standing long jump on distance based on surface electromyography

Objective: To reveal the influence of muscle strength in different parts of the body on the distance of standing jump, to establish the key force phases of muscle strength in different parts, and to improve the recognition of movement norms. Methods: VICON infrared three-dimensional motion capture acquisition and analysis system and Noraxon Ultium surface electromyography acquisition and analysis system were used to complete the surface electromyography signal acquisition of 18 randomly selected subjects performing standing long jump. Results: 1) Triceps brachii, anterior deltoid, latissimus dorsi, semitendinosus, rectus femoris, upper trapezius, pectoralis major, and biceps femoris had significant effects on standing jump distance. 2) From the point of view of the key exertion phase of the standing jump mainly affecting the muscle group; the main exertion phase of the semitendinosus occurs from the rising stage to the descending stage; the rectus femoris, triceps, and latissimus dorsi occur during the ascending phase of the flight; the anterior deltoid muscle occurs in the transition stage from rising to falling in the air; the trapezius muscle occurs in the transition stage from pre-swing to kick-off. Conclusion: 1) From the regression analysis of the measured muscles on the distance of each stage of standing long jump, deltoid muscle strength is conducive to the improvement of standing long jump distance, which further indicates the importance of upper limb deltoid muscle strength. 2) Through time series analysis, it is found that the force performance of the rectus femoris muscle at this stage can be used as the main identification parameter of standing long jump, and can effectively distinguish different types of movements.

The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces

Functional principal components define modes of variation in time series, which represent characteristic movement patterns in biomechanical data. Their usefulness however depends on the prior choices made in data processing. Recent research showed that better curve alignment achieved with registration (dynamic time warping) reduces errors in linear models predicting jump height. However, the efficacy of registration in different preprocessing combinations, including time normalisation, padding and feature extraction, is largely unknown. A more comprehensive analysis is needed, given the potential value of registration to machine learning in biomechanics. We evaluated popular preprocessing methods combined with registration, creating 512 models based on ground reaction force data from 385 countermovement jumps. The models either predicted jump height or classified jumps into those performed with or without arm swing. Our results show that the classification models benefited from registration in various forms, particularly when landmarks were placed at critical points. The best classifier achieved a 5.5 percentage point improvement over the equivalent unregistered model. However, registration was detrimental to the jump height models, although this performance variable may be a special case given its direct relationship with impulse. Our meta-models revealed the relative contributions made by various preprocessing operations, highlighting that registration does not generalise so well to new data. Nonetheless, our analysis shows the potential for registration in further biomechanical applications, particularly in classification, when combined with the other appropriate preprocessing operations.

Multivariate functional principal component analysis identifies waveform features of gait biomechanics related to early-to-moderate hip osteoarthritis

Clinicians often examine movement patterns to design hip osteoarthritis (OA) interventions, yet traditional biomechanical analyses only report a single timepoint. Multivariate principal component analysis (MFPCA) analyzes the entire waveform (i.e., movement pattern), which clinicians observe to direct treatment. This study investigated hip OA indicators, by (1) employing MFPCA to characterize variance across the hip, knee, and ankle angles in healthy and early-to-moderate hip OA participants; and (2) investigating relationships between these waveform features and hip cartilage health. Bilateral hip magnetic resonance images from 72 participants with Kellgren-Lawrence grades ranging from 0 to 3 were used to calculate mean T _1ρ and T ₂ relaxation times in the femoral and acetabular cartilage. MFPCA was performed on lower-limb gait biomechanics and used to identify primary modes of variation, which were related to T _1ρ and T ₂ relaxation times. Here, a MFPC = mode of variation = waveform feature. In the femoral cartilage, transverse plane MFPCs 3 and 5 and body mass index (BMI) was related to T _1ρ , while MFPC 2 and BMI were related to T ₂ relaxation times. In the acetabular cartilage, sagittal plane MFPC 1 and BMI were related to T _1ρ , while BMI was related to T ₂ relaxation times. Greater internal rotation was related to increased T _1ρ and T ₂ relaxation times in the femoral cartilage, while the greater extension was related to increased T _1ρ relaxation times in the acetabular cartilage. This study established a data-driven framework to assess relationships between multi-joint biomechanics and quantitative assessments of cartilage health and identified waveform features that could be evaluated in future hip OA intervention studies.

Talipes Equinovarus Treatment in Infants Treated by the Ponseti Method Compared With Posterior-Only Release: A Mid-Childhood Comparison of Results

The aim of this study is to evaluate children in middle childhood with clubfoot treated with Ponseti method vs posterior-only release and to compare their results to a control group with 4 modules (physical examination, gait study, radiographic measurements, and questionnaires). From 01/01/2004 until 01/01/2009, 31 children (45 feet) were treated with the posterior-only release protocol and 22 patients (34 feet) were treated with the Ponseti method. In 2016, patients were evaluated and compared with 25 children without neuromuscular disorders. Parents completed 3 outcome questionnaires. Radiographs evaluated residual deformity and osteoarthritis. A physical examination and a 3-dimensional gait analysis were performed to evaluate range of motion, kinematic, and kinetic data. Recurrence rate was similar between treatment groups; however, type of surgery to treat residual deformity was more aggressive in the posterior-only release (91% required major surgery), p = .024. Radiographic examination showed similar residual deformity with greater hindfoot varus in posterior-only release (68%), p = .02. Reduced cadence, increased stance dorsiflexion, calcaneus gait and forced eversion prior to swing were the main characteristics of gait in posterior-only release. Four (11%) feet treated with posterior-only release vs 11 (33%) feet treated with Ponseti method had a normal gait, p = .016. Our study showed that biomechanical function and long-term outcomes of children in middle childhood treated with the Ponseti method more closely compare with healthy individuals than those treated using posterior-only surgical technique.

Comparison of Frontal and Transverse Plane Kinematics Related to Knee Injury in Novice Versus Experienced Female Runners

Novice runners experience a higher incidence of knee injury than experienced runners, which may be related to aberrant frontal and transverse plane kinematics. However, differences in kinematics between novice and experienced runners have not been fully explored. For this study, 10 novice and 10 experienced female runners ran on a treadmill at 2.68 m/s. Ankle, knee, and hip joint angles during the stance phase were measured using a 3-dimensional motion capture system and modeled using cubic splines. Spline models were compared between groups using a generalized linear model (α = .05). Ninety-five percent confidence intervals of the difference between joint angles throughout stance were constructed to identify specific periods of stance where groups differed in joint position. Angle-angle diagrams of ankle and hip position in the frontal and transverse planes were constructed to depict joint coordination. Novice runners displayed less hip adduction, but greater knee abduction and knee internal rotation compared to experienced runners. Differences in knee joint position may be explained by coordination of hip and ankle motion. Greater knee abduction and knee internal rotation displayed by novice runners compared with experienced runners may help to explain their higher risk for injury.

Cellists' sound quality is shaped by their primary postural behavior

During the last 20 years, the role of musicians’ body movements has emerged as a central question in instrument practice: Why do musicians make so many postural movements, for instance, with their torsos and heads, while playing musical instruments? The musical significance of such ancillary gestures is still an enigma and therefore remains a major pedagogical challenge, since one does not know if these movements should be considered essential embodied skills that improve musical expressivity. Although previous studies established clear connections between musicians’ body movements and musical structures (particularly for clarinet, piano or violin performances), no evidence of direct relationships between body movements and the quality of the produced timbre has ever been found. In this study, focusing on the area of bowed-string instruments, we address the problem by showing that cellists use a set of primary postural directions to develop fluid kinematic bow features (velocity, acceleration) that prevent the production of poor quality (i.e., harsh, shrill, whistling) sounds. By comparing the body-related angles between normal and posturally constrained playing situations, our results reveal that the chest rotation and vertical inclination made by cellists act as coordinative support for the kinematics of the bowing gesture. These findings support the experimental works of Alexander, especially those that showed the role of head movements with respect to the upper torso (the so-called primary control) in ensuring the smooth transmission of fine motor control in musicians all the way to the produced sound. More generally, our research highlights the importance of focusing on this fundamental postural sense to improve the quality of human activities across different domains (music, dance, sports, rehabilitation, working positions, etc.).

Smoothing and Differentiation of Kinematic Data Using Functional Data Analysis Approach: An Application of Automatic and Subjective Methods

Smoothing is one of the fundamental procedures in functional data analysis (FDA). The smoothing parameter λ influences data smoothness and fitting, which is governed by selecting automatic methods, namely, cross-validation (CV) and generalized cross-validation (GCV) or subjective assessment. However, previous biomechanics research has only applied subjective assessment in choosing optimal λ without using any automatic methods beforehand. None of that research demonstrated how the subjective assessment was made. Thus, the goal of this research was to apply the FDA method to smoothing and differentiating kinematic data, specifically right hip flexion/extension (F/E) angle during the American kettlebell swing (AKS) and determine the optimal λ . CV and GCV were applied prior to the subjective assessment with various values of λ together with cubic and quintic spline (B-spline) bases using the FDA approach. The selection of optimal λ was based on smoothed and well-fitted first and second derivatives. The chosen optimal λ was 1 × 10 − 12 with a quintic spline (B-spline) basis and penalized fourth-order derivative. Quintic spline is a better smoothing and differentiation method compared to cubic spline, as it does not produce zero acceleration at endpoints. CV and GCV did not give optimal λ , forcing subjective assessment to be employed instead.

The Variance Needed to Accurately Describe Jump Height from Vertical Ground Reaction Force Data

In functional principal component analysis (fPCA) a threshold is chosen to define the number of retained principal components, which corresponds to the amount of preserved information. A variety of thresholds have been used in previous studies and the chosen threshold is often not evaluated. The aim of this study is to identify the optimal threshold that preserves the information needed to describe a jump height accurately utilizing vertical ground reaction force (vGRF) curves. To find an optimal threshold, a neural network was used to predict jump height from vGRF curve measures generated using different fPCA thresholds. The findings indicate that a threshold from 99% to 99.9% (6–11 principal components) is optimal for describing jump height, as these thresholds generated significantly lower jump height prediction errors than other thresholds.

Time-frequency analysis methods for detecting effects of diabetic neuropathy

There have been several research studies on efficient methods for analysis and classification of electromyography (EMG) signals and adoption of wavelet functions, which is a promising approach for determining the spectral distribution of the signal. This study compares distinct time-frequency analysis methods for investigating the EMG activity of the thigh and calf muscles during gait among non-diabetic subjects and diabetic neuropathic patients. It also attempts to verify, by adaptive optimal kernel and discrete wavelet transform, whether there are EMG alterations related to diabetic neuropathy in the lower limb muscles during gait. The results show that diabetics might not keep up with the mechanical demands of walking by changing muscle fibre recruitment strategies, as seen in the control group. Moreover, principal components analysis indicates more alterations in diabetic motor strategies, and we identify that diabetic subjects need other strategies with different muscle energy production and frequencies to carry out their daily activities.

Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics

Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and footwear - control shoe and adidas MicroBounce shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes.

Kinematic and kinetic synergies of the lower extremities during the pull in olympic weightlifting

The purpose of this study was to identify multijoint lower extremity kinematic and kinetic synergies in weightlifting and compare these synergies between joints and across different external loads. Subjects completed sets of the clean exercise at loads equal to 65, 75, and 85% of their estimated 1-RM. Functional data analysis was used to extract principal component functions (PCF's) for hip, knee, and ankle joint angles and moments of force during the pull phase of the clean at all loads. The PCF scores were then compared between joints and across loads to determine how much of each PCF was present at each joint and how it differed across loads. The analyses extracted two kinematic and four kinetic PCF's. The statistical comparisons indicated that all kinematic and two of the four kinetic PCF's did not differ across load, but scaled according to joint function. The PCF's captured a set of joint- and load-specific synergies that quantified biomechanical function of the lower extremity during Olympic weightlifting and revealed important technical characteristics that should be considered in sports training and future research.