Evaluation of gait in Duchenne Muscular Dystrophy: Relation of 3D gait analysis to clinical assessment

Inertial Sensor Gait Analysis of Trendelenburg Gait in Patients Who Have Hip Osteoarthritis

BACKGROUND

Gait abnormalities such as Trendelenburg gait (TG) in patients who have hip osteoarthritis (OA) have traditionally been evaluated using clinicians' visual assessment. Recent advances in portable inertial gait sensors offer more sensitive, quantitative methods for gait assessment in clinical settings. This study sought to compare sensor-derived metrics in a cohort of hip OA patients when stratified by clinical TG severity.

METHODS

There were 42 patients who had hip OA and were grouped by TG severity (mild, moderate, and severe) through visual assessment by a single arthroplasty surgeon who had > 30 years of experience. After informed consent, wireless inertial sensors placed at the midpoint of the intercristal line collected gait parameters including pelvic shift, support time, toe-off symmetry, impact, and cadence. Clinical data on hip strength, range of motion, and Kellgren-Lawrence grade were collected.

RESULTS

Worsening TG severity had a higher mean Kellgren-Lawrence grade (2.5 versus 3.2 versus 3.4; P = .014) and reduced passive hip abduction (P = .004). Severe TG group demonstrated predominantly contralateral pelvic shift (n = 9 of 10, 90.0%), while ipsilateral shift was more frequently detected in moderate (n = 10 of 18, 55.6%) and mild groups (n = 9 of 14, 64.3%; P = .021). Contralateral single support time bias was greatest in severe TG (35.7% versus 50.0 versus 90.0%; P = .027). Asymmetric toe-off, impact, and support times were observed in all groups.

CONCLUSIONS

Traditional understanding of TG is that truncal shift occurs to the ipsilateral side. Using sensor-based measurements, the present study demonstrates a shift of the weight-bearing axis toward the contralateral side with increasing TG severity, which has not been previously described. Inertial sensors are feasible, quantitative gait measuring tools, and may reveal subtle patterns not readily discernible by traditional methods.

Human Pose Estimation for Clinical Analysis of Gait Pathologies

Gait analysis serves as a critical diagnostic tool for identifying neurologic and musculoskeletal damage. Traditional manual analysis of motion data, however, is labor-intensive and heavily reliant on the expertise and judgment of the therapist. This study introduces a binary classification method for the quantitative assessment of gait impairments, specifically focusing on Duchenne muscular dystrophy (DMD), a prevalent and fatal neuromuscular genetic disorder. The research compares spatiotemporal and sagittal kinematic gait features derived from 2D and 3D human pose estimation trajectories against concurrently recorded 3D motion capture (MoCap) data from healthy children. The proposed model leverages a novel benchmark dataset, collected from YouTube and publicly available datasets of their typically developed peers, to extract time-distance variables (e.g. speed, step length, stride time, and cadence) and sagittal joint angles of the lower extremity (e.g. hip, knee, and knee flexion angles). Machine learning and deep learning techniques are employed to discern patterns that can identify children exhibiting DMD gait disturbances. While the current model is capable of distinguishing between healthy subjects and those with DMD, it does not specifically differentiate between DMD patients and patients with other gait impairments. Experimental results validate the efficacy of our cost-effective method, which relies on recorded RGB video, in detecting gait abnormalities, achieving a prediction accuracy of 96.2% for Support Vector Machine (SVM) and 97% for the deep network.

Gait classification for growing children with Duchenne muscular dystrophy

Classifying gait patterns into homogeneous groups could enhance communication among healthcare providers, clinical decision making and clinical trial designs in boys with Duchenne muscular dystrophy (DMD). Sutherland's classification has been developed 40 years ago. Ever since, the state-of-the-art medical care has improved and boys with DMD are now longer ambulatory. Therefore, the gait classification requires an update. The overall aim was to develop an up-to-date, valid DMD gait classification. A total of 137 three-dimensional gait analysis sessions were collected in 30 boys with DMD, aged 4.6-17 years. Three classes were distinguished, which only partly aligned with increasing severity of gait deviations. Apart from the mildly affected pattern, two more severely affected gait patterns were found, namely the tiptoeing pattern and the flexion pattern with distinct anterior pelvic tilt and posterior trunk leaning, which showed most severe deviations at the ankle or at the proximal segments/joints, respectively. The agreement between Sutherland's and the current classification was low, suggesting that gait pathology with the current state-of-the-art medical care has changed. However, overlap between classes, especially between the two more affected classes, highlights the complexity of the continuous gait changes. Therefore, caution is required when classifying individual boys with DMD into classes.

Population longitudinal analysis of Gait Profile Score and North Star Ambulatory Assessment in children with Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a rare X-linked recessive disorder characterized by loss-of-function mutations in the gene encoding dystrophin. These mutations lead to progressive functional deterioration including muscle weakness, respiratory insufficiency, and musculoskeletal deformities. Three-dimensional gait analysis (3DGA) has been used as a tool to analyze gait pathology through the quantification of altered joint kinematics, kinetics, and muscle activity patterns. Among 3DGA indices, the Gait Profile Score (GPS), has been used as a sensitive overall measure to detect clinically relevant changes in gait patterns in children with DMD. To enhance our understanding of the clinical translation of 3DGA, we report here the development of a population nonlinear mixed-effect model that jointly describes the disease progression of the 3DGA index, GPS, and the functional endpoint, North Star Ambulatory Assessment (NSAA). The final model consists of a quadratic structure for GPS progression and a linear structure for GPS-NSAA correlation. Our model was able to capture the improvement in function in GPS and NSAA in younger subjects, as well as the decline of function in older subjects. Furthermore, the model predicted NSAA (CFB) at 1 year reasonably well for DMD subjects ≤7 years old at baseline. The model tended to slightly underpredict the decline in NSAA after 1 year for those >7 years old at baseline, but the prediction summary statistics were well maintained within the standard deviation of observed data. Quantitative models such as this may help answer clinically relevant questions to facilitate the development of novel therapies in DMD.

Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning

Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provides highly relevant clinical information. This information is used to guide therapeutic choices; however, it is underused in diagnostic processes, probably because of the lack of standardization of data collection methods. Therefore, 3D gait analysis is currently used as an assessment rather than a diagnostic tool. In this work, we aimed to determine if deep learning could be combined with 3D gait analysis data to diagnose gait disorders in children. We tested the diagnostic accuracy of deep learning methods combined with 3D gait analysis data from 371 children (148 with unilateral cerebral palsy, 60 with neuromuscular disease, 19 toe walkers, 60 with bilateral cerebral palsy, 25 stroke, and 59 typically developing children), with a total of 6400 gait cycles. We evaluated the accuracy, sensitivity, specificity, F1 score, Area Under the Curve (AUC) score, and confusion matrix of the predictions by ResNet, LSTM, and InceptionTime deep learning architectures for time series data. The deep learning-based models had good to excellent diagnostic accuracy (ranging from 0.77 to 0.99) for discrimination between healthy and pathological gait, discrimination between different etiologies of pathological gait (binary and multi-classification); and determining stroke onset time. LSTM performed best overall. This study revealed that the gait pattern contains specific, pathology-related information. These results open the way for an extension of 3D gait analysis from evaluation to diagnosis. Furthermore, the method we propose is a data-driven diagnostic model that can be trained and used without human intervention or expert knowledge. Furthermore, the method could be used to distinguish gait-related pathologies and their onset times beyond those studied in this research.

Children with Duchenne muscular dystrophy display specific kinematic strategies during obstacle-crossing

Duchenne muscular dystrophy (DMD) is a genetic disease characterized by progressive muscle weakness with increased neuromechanical challenge and fall risks, especially during obstructed locomotion. This study aimed to identify the kinematic strategies for obstacle-crossing in DMD via synthesizing the changes in the joint kinematics and associated end-point control. Fourteen boys with DMD (age: 9.0 ± 2.5 years) and fourteen typically developed controls (age: 9.0 ± 2.8 years) each crossed obstacles of three different heights (10%, 20% and 30% of leg length) while the angular motions of the trunk-pelvis-leg apparatus and foot-obstacle clearances were measured. Two-way analyses of variance were used to analyze group and obstacle height effects. Compared to the controls, the DMD group crossed obstacles with significantly increased step width, but decreased crossing speed, crossing step length, trailing toe-obstacle clearance and leading heel-obstacle horizontal distance (p < 0.05). When the leading toe was above the obstacle, the patients showed significantly increased pelvic hiking, pelvic and trunk anterior tilt and ankle plantarflexion, but decreased hip flexion in both limbs (p < 0.05). Similar kinematic changes were found during trailing-limb crossing, except for an additional increase in swing-hip abduction and decrease in contralateral trunk side-bending and stance-knee flexion. Patients with DMD crossed obstacles via a specific kinematic strategy with altered end-point control, predisposing them to a greater risk of tripping during trailing-limb crossing. These results suggest that crossing kinematics in DMD should be monitored-especially in the proximal segments of the pelvis-leg apparatus-that may lead to an increased risk of falling.

Gait asymmetry in children with Duchenne muscular dystrophy: evaluated through kinematic synergies and muscle synergies of lower limbs

BACKGROUND

Gait is a complex, whole-body movement that requires the coordinated action of multiple joints and muscles of our musculoskeletal system. In the context of Duchenne muscular dystrophy (DMD), a disease characterized by progressive muscle weakness and joint contractures, previous studies have generally assumed symmetrical behavior of the lower limbs during gait. However, such a symmetric gait pattern of DMD was controversial. One aspect of this is criticized, because most of these studies have primarily focused on univariate variables, rather than on the coordination of multiple body segments and even less investigate gait symmetry under a motor synergy of view.

METHODS

We investigated the gait pattern of 20 patients with DMD, compared to 18 typical developing children (TD) through 3D Gait Analysis. Kinematic and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. In addition, bilateral spatiotemporal variables of gait, such as stride length, percentage of stance and swing phase, step length, and percentage of double support phase, were used for calculating the symmetry index (SI) to evaluate gait symmetry as well.

RESULTS

Compared with the TD group, the DMD group walked with decreased gait velocity (both p < 0.01), stride length (both p < 0.01), and step length (both p < 0.001). No significant difference was found between groups in SI of all spatiotemporal parameters extracted between the left and right lower limbs. In addition, the DMD group exhibited lower kinematic synergy symmetry values compared to the TD group (p < 0.001), while no such significant group difference was observed in symmetry values of muscle synergy.

CONCLUSIONS

The findings of this study suggest that DMD influences, to some extent, the symmetry of synergistic movement of multiple segments of lower limbs, and thus kinematic synergy appears capable of discriminating gait asymmetry in children with DMD when conventional spatiotemporal parameters are unchanged.

Longitudinal Alterations in Gait Features in Growing Children With Duchenne Muscular Dystrophy

Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6-15 years). They were repeatedly measured over a varying follow-up period (range: 6 months-5 years). The TD group consisted of 27 children (age: 5.4-15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The current study quantitatively described the longitudinal alterations in gait features in boys with DMD, thereby providing detailed insight into how DMD gait deteriorates. Additionally, our results highlight that gait features extracted from 3DGA are promising outcome measures for future clinical trials to quantify the efficacy of novel therapeutic strategies.

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

Comparison of the Gait Biomechanical Constraints in Three Different Type of Neuromotor Damages

Background and Objective

Absolute angle represents the inclination of a body segment relative to a fixed reference in space. This work compares the absolute and relative angles for exploring biomechanical gait constraints.

Methods

Gait patterns of different neuromotor conditions were analyzed using 3D gait analysis: normal gait (healthy, H), Cerebral Palsy (CP), Charcot Marie Tooth (CMT) and Duchenne Muscular Dystrophy (DMD), representing central and peripheral nervous system and muscular disorders, respectively. Forty-two children underwent gait analysis: 10 children affected by CP, 10 children by CMT, 10 children by DMD and 12 healthy children. The kinematic and kinetic parameters were collected to describe the biomechanical pattern of participants’ lower limbs. The absolute angles of thigh, leg and foot were calculated using the trigonometric relationship of the tangent. For each absolute series, the mean, range, maximum, minimum and initial contact were calculated. Kinematic and kinetic gait data were studied, and the results were compared with the literature.

Results

Statistical analysis of the absolute angles showed how, at the local level, the single segments (thigh, leg and foot) behave differently depending on the pathology. However, if the lower limb is studied globally (sum of the kinematics of the three segments: thigh, leg and foot), a biomechanical constraint emerges.

Conclusion

Each segment compensates separately for the disease deficit so as to maintain a global biomechanical invariance. Using a model of inter-joint co-variation could improve the interpretation of the clinical gait pattern.

Real-Time Exercise Feedback through a Convolutional Neural Network: A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application

PURPOSE

Mobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile exercise coaching application (MDMECA) is superior to video streaming-based exercise for improving quality of life and decreasing lower back pain.

MATERIALS AND METHODS

The same 14-day daily workout program consisting of five exercises was performed by 104 participants using the MDMECA and another 72 participants using video streaming. The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) and lower back pain scores were assess as pre- and post-workout measurements. Scores for the treatment-satisfaction subscale of the visual analog scale (TS-VAS), intention to use a disease-oriented exercise program, intention to recommend the program to others, and available expenses for a disease-oriented exercise program were determined after the workout.

RESULTS

The MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09; p<0.01) and a greater reduction in lower back pain score (MDMECA, -0.96; control, -0.26; p<0.01). Scores for TS-VAS, intention to use a disease-oriented exercise program, and intention to recommend the program to others were all higher (p<0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups.

CONCLUSION

The MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.

Knee Strength and Ankle Range of Motion Impacts on Timed Function Tests in Duchenne Muscular Dystrophy: In the Era of Glucocorticoids

BACKGROUND

Duchenne Muscular Dystrophy (DMD) is a neuromuscular disorder that presents in childhood and is characterized by slowly progressive proximal weakness and lower extremity contractures that limit ambulatory ability [1, 2]. Contractures develop in the ankles, knees, and hips due to muscle imbalances, fibrotic changes, loss of strength, and static positioning [2, 5]. Currently, standards of care guidelines emphasize the importance of maintaining good musculoskeletal alignment through stretching, bracing, and glucocorticoid (GC) therapy to preserve strength and function.

METHODS

This is a retrospective analysis of prospectively collected data through the CINRG Duchenne Natural history study (DNHS). The objectives of this analysis are to understand the progression of ankle contractures for individuals with DMD and to investigate the relationship between progressive lower limb contractures, knee strength, and Timed Function Tests.A collection of TFTs including supine to stand (STS), 10 meter walk test (10MWT), and timed stair climbing (4SC) have been used to monitor disease progression and are predictive of loss of ambulation in these patients [4]. Multiple factors contribute to loss of ambulation, including progressive loss of strength and contracture development that leads to changing biomechanical demands for ambulation. A better understanding of the changes in strength and range of motion (ROM) that contribute to loss of function is important in a more individualized rehabilitation management plan. In this longitudinal study, we measured strength using quantitative muscle testing (QMT) with the CINRG Quantitative Measurement System (CQMS)), ROM was measuresed with a goniometer and TFTs were measured using a standard stopwatch and methodology.

RESULTS

We enrolled 440 participants; mean baseline age was 8.9 (2.1, 28.0) years with 1321 observations used for analysis. GC use was stratified based on duration on drug with 18.7%at <  6 months or naïve; 4.3%<1 year; 58.0%1 <  10 years; and 19.3%between 10-25 years of GC use. Ankle ROM was better for those on GC compared to GC naive but did not significantly influence long-term progression rates. QMT, ROM, age and GCs contribute to speed of TFTs. Knee extension (KE) strength and Dorsiflexion (DF) ROM are significant predictors of speed for all TFTs (p <  0.001). Of the variables used in this analysis, KE strength is the primary predictor of walking speed, estimating that every pound increase in KE results in a 0.042 m/s improvement in 10MWT, and a smaller similar increase of 0.009 m/s with every degree of ankle DF ROM.

CONCLUSION

GC use provides an improvement in strength and ROM but does not affect rate of change. Knee strength has a greater influence on speed of TFTs than DF ROM, although both are statistically significant predictors of speed. Results show that retaining knee strength [1, 2], along with joint flexibility, may be important factors in the ability to perform walking, climbing and supine to stand activities.

Measurement and Analysis of Human Infant Crawling for Rehabilitation: A Narrative Review

When a child shows signs of potential motor developmental disorders, early diagnosis of central nervous system (CNS) impairment is beneficial. Known as the first CNS-controlled mobility for most of infants, mobility during crawling usually has been used in clinical assessments to identify motor development disorders. The current clinical scales of motor development during crawling stage are relatively subjective. Objective and quantitative measures of infant crawling afford the possibilities to identify those infants who might benefit from early intervention, as well as the evaluation of intervention progress. Thus, increasing researchers have explored objective measurements of infant crawling in typical and atypical developing infants. However, there is a lack of comprehensive review on infant-crawling measurement and analysis toward bridging the gap between research crawling analysis and potential clinical applications. In this narrative review, we provide a practical overview of the most relevant measurements in human infant crawling, including acquisition techniques, data processing methods, features extraction, and the potential value in objective assessment of motor function in infancy; meanwhile, the possibilities to develop crawling training as early intervention to promote the locomotor function for infants with locomotor delays are also discussed.