Gait Anomalies Research Articles

Mouse developmental defects, but not paraganglioma tumorigenesis, upon conditional Complex II loss in early Sox10+ cells.

In humans, loss of heterozygosity for defective alleles of any of the four subunits of mitochondrial tricarboxylic acid cycle enzyme succinate dehydrogenase (SDH, also Complex II of the electron transport chain) can lead to paraganglioma tumors in neuroendocrine cells. With the goal of developing mouse models of this rare disorder, we have developed various SDH conditional loss strategies. Based on recent lineage tracing studies, we hypothesized that conditional SDHC loss in early embryogenesis during migration of primordial neural crest cells that form the susceptible chromaffin cells of the adrenal medulla might induce paraganglioma. We triggered low levels of detectable SDHC loss in Sox10+ cells at E11.5 of mouse development. We report that, rather than developing adrenal medulla paraganglioma (pheochromocytoma), offspring survived with evidence of neural crest cell dysfunction. Phenotypes included mild lower extremity gait anomalies suggestive of neural tube closure defects and patches of unpigmented fur consistent with neural crest-derived melanocyte dysfunction. These defects were not observed in mice lacking Sdhc knockout. Our results add to existing data suggesting that, unlike humans, even early embryonic (Sox10-driven) SDHx loss is inadequate to trigger paraganglioma in mice of the genetic backgrounds that have been investigated. Instead, low levels of tricarboxylic acid cycle-deficient neural crest cells cause mild developmental defects in hind limb and melanocyte function. This new model may be of interest for studies of metabolism during early neural crest cell development.

Australia and New Zealand Clinical Motion Analysis Group (ANZ-CMAG) clinical practice recommendations

Clinical motion analysis involves quantitative measurement of gait patterns to identify gait anomalies that currently or have the potential to impact function, activities of daily living and participation. Clinical motion analysis services are equipped with motion capture technology and comprise specialised staff who deliver 3-dimensional motion analysis services to children and adults who present with varying levels of gait impairment. Data is then used to inform intervention recommendations to clinicians with a view to maintaining independent, functional and pain free walking (or appropriate mobility).The ANZ-CMAG (established in 2013) identified a need to establish recommendations to assist in standardising practice guidelines for both current and new clinical motion analysis services within the region. The group serves to promote collaboration between services in quality assurance processes, clinical practices, data sets and research activities. The clinical practice recommendations described in this paper cover: i) requirements for a motion analysis service (including staffing, facilities and equipment), ii) patient assessments (requirements, clinical information and data gathered, reporting and interpretation of patient data), iii) quality assurance processes (including motion capture system / biomechanical models & limitations, marker placement, data storage / record keeping, creation of normative dataset); iv) helpful resources.Better outcomes for children and adults with gait deviations is dependent upon accurate measurement and evaluation of walking and requires input from multidisciplinary clinical teams with specialist knowledge and skills. The ANZ-CMAG hopes these clinical practice recommendations are beneficial to motion analysis services with an aim to improve clinical practices, patient outcomes, and support research collaboration.

Machine Learning Approach Based on Automated Classification for Leg Rehabilitation

Human gait data follows distinct and identifiable patterns that are critical for movement analysis and evaluation Like other biological signals. The success of a rehabilitation program is dependent on the execution of proper progress monitoring. To ensure success, diagnosis of gait anomalies, as well as the implementation of therapy to address them, must be validated in a constant and timely manner in developing youngsters. In this paper, machine learning techniques were utilized to classify foot diseases and the purpose is to increase the accuracy of disease detection and diagnosis because intelligent systems can contribute significantly in the medical field and have proven their worth in diagnosing many diseases. The results show high accuracy of the used machine learning algorithms, where the accuracy of the classifiers reached 100% for Random Forest (RF), Decision Tree (DT), and k-nearest neighbors (KNN), while it reached 98% for Logistic Regression. Index Terms—Biometrics, Machine Learning (ML), Drop foot (DF), Leg Rehabilitation, and Human gait.

Gait analysis to quantitatively classify Ataxia and Parkinson’s disease patients: a pilot study using tree-based Machine Learning algorithms

Posture and gait anomalies investigations have demonstrated to improve to date the rehabilitation programs and patients’ outcome for several types of disorders [1,2]; however, the diagnosis and progression of some illnesses, such as Ataxia and Parkinson’s Disease (PD), still rely on “gold standard” (clinical) methodologies, which hardly ever allows to fully distinguish the two pathologies. Consequently, the objective of this paper is to provide a new strategy to classify Ataxia and PD subjects using quantitative gait analysis tools. For this purpose, two cohorts of 43 PD and 22 ataxic patients were enrolled. Each patient underwent an Instrumented Stand and Walking [3] test using a microelectromechanical system equipped by a series of inertial measurement units (OPAL, ADPM Inc.) capable to extract – from the raw data acquired – ten posture and gait parameters. A Machine Learning (ML) approach, considering tree-based algorithms fed with the aforementioned features, was implemented in KNIME Analytics Platform (version 4.1.3, KNIME AG) to assess the degree of separability among the two classes of patients. Table 1 summarizes the results of the preliminary ML analysis conducted using three tree-based algorithms. This paper presents a potential strategy to distinguish quantitatively – through ML – patients affected by Ataxia and PD. Despite the unbalanced patients’ cohorts (PD subjects are double respect to Ataxia ones), ML tree-based algorithm demonstrated to discriminate the two classes achieving quite fair scores. Further research will involve a deeper evaluation of patients and additional/more informative kinematic parameters to better corroborate the preliminary results of this study.

Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series.

Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.

In-home Health Monitoring using Floor-based Gait Tracking

Gait assessments are commonly used for clinical evaluations of neurocognitive disease progression and general wellness. However, gait measurements in clinical settings do not accurately reflect gait in daily life. We present a non-wearable and unobtrusive method of detecting gait parameters in the home through the vibrations in the floor created by footfalls. Gait characteristics and gait asymmetry are estimated despite a low sensor density of 6.7 m2/sensor. Features from each footfall vibration signal is extracted and used to estimate gait parameters with gradient boosting regression and probabilistic models. Temporal gait asymmetry, locations of the footfalls, and peak tibial acceleration asymmetry can be predicted with a root mean square error of 0.013 s, 0.42 m, and 0.34 g respectively. This system allows for continuous at-home monitoring of gait which aids in early detection of gait anomalies.

Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures

Identifying the foot stance and foot swing phases, also known as foot-to-ground (FTG) detection, is a branch of Human Activity Recognition (HAR). Our study aims to detect two main phases of the gait (i.e., foot-off and foot-contact) corresponding to the moments when each foot is in contact with the ground or not. This will allow the medical professionals to characterize and identify the different phases of the human gait and their respective patterns. This detection process is paramount for extracting gait features (e.g., step width, stride width, gait speed, cadence, etc.) used by medical experts to highlight gait anomalies, stance issues, or any other walking irregularities. It will be used to assist health practitioners with patient monitoring, in addition to developing a full pipeline for FTG detection that would help compute gait indicators. In this paper, a comparison of different training configurations, including model architectures, data formatting, and pre-processing, was conducted to select the parameters leading to the highest detection accuracy. This binary classification provides a label for each timestamp informing whether the foot is in contact with the ground or not. Models such as CNN, LSTM, and ConvLSTM were the best fits for this study. Yet, we did not exclude DNNs and Machine Learning models, such as Random Forest and XGBoost from our work in order to have a wide range of possible comparisons. As a result of our experiments, which included 27 senior participants who had a stroke in the past wearing IMU sensors on their ankles, the ConvLSTM model achieved a high accuracy of 97.01% for raw windowed data with a size of 3 frames per window, and each window was formatted to have two superimposed channels (accelerometer and gyroscope channels). The model was trained to have the best detection without any knowledge of the participants’ personal information including age, gender, health condition, the type of activity, or the used foot. In other words, the model’s input data only originated from IMU sensors. Overall, in terms of FTG detection, the combination of the ConvLSTM model and the data representation had an important impact in outperforming other start-of-the-art configurations; in addition, the compromise between the model’s complexity and its accuracy is a major asset for deploying this model and developing real-time solutions.

A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis.

In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.

Monitoring of Gait Parameters in Post-Stroke Individuals: A Feasibility Study Using RGB-D Sensors.

Stroke is one of the most significant causes of permanent functional impairment and severe motor disability. Hemiplegia or hemiparesis are common consequences of the acute event, which negatively impacts daily life and requires continuous rehabilitation treatments to favor partial or complete recovery and, consequently, to regain autonomy, independence, and safety in daily activities. Gait impairments are frequent in stroke survivors. The accurate assessment of gait anomalies is therefore crucial and a major focus of neurorehabilitation programs to prevent falls or injuries. This study aims to estimate, using a single RGB-D sensor, gait patterns and parameters on a short walkway. This solution may be suitable for monitoring the improvement or worsening of gait disorders, including in domestic and unsupervised scenarios. For this purpose, some of the most relevant spatiotemporal parameters, estimated by the proposed solution on a cohort of post-stroke individuals, were compared with those estimated by a gold standard system for a simultaneous instrumented 3D gait analysis. Preliminary results indicate good agreement, accuracy, and correlation between the gait parameters estimated by the two systems. This suggests that the proposed solution may be employed as an intermediate tool for gait analysis in environments where gold standard systems are impractical, such as home and ecological settings in real-life contexts.

Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion

For patients suffering from drop foot due to weakness of ankle dorsiflexion muscles, an ankle foot orthosis provides increased foot clearance during the swing phase of gait, but often restricts other gait functions, such as plantarflexion. Due to steady progress in the development of lighter and smaller actuator technologies, active wearable devices such as exosuits become relevant for rehabilitation, since they can offer an extended functionality including a more comfortable wear than passive plastic orthosis. The aim of the paper is to present a lightweight exosuit supporting dorsiflexion during gait with autonomous recognition of gait phases and conditions. One main requirement during the iterative development of the exosuit is a non-restrictive function, thus no differences between the assisted and non-assisted gait of a healthy subject should occur. We therefore conducted a pilot biomechanics study using statistical parametric mapping to analyze kinematics of the ankle joint and muscle activity of m. tibialis anterior of nine subjects without any gait anomalies walking with and without the exosuit. The results show no significant difference between with and without support. In contrast to passive orthosis, the developed system could be an enhanced solution to assist patients suffering from drop foot, which should be analyzed in the next step for evaluating the development.

Apparent Breed Predilection for Equid Herpesvirus-1-Associated Myeloencephalopathy (EHM) in a Multiple-Breed Herd.

Equid herpesvirus type 1 (EHV-1) causes several outbreaks of abortion and/or equid herpesvirus-associated myeloencephalopathy (EHM) worldwide each year. EHM is of great concern, as permanent neurological gait anomalies can leave a horse unfit for future use. The study assesses the risk factors associated with the occurrence of EHM. During an unmitigated outbreak, 141 adult horses/ponies of several distinct breeds were evaluated—using multiple Bayesian logistic regression calculating the odds ratios for breed, age, and sex. In total, 33 of the 141 horses showed signs of EHM. Fjord horses and warmblood horses were overrepresented among those developing EHM. The pony breeds, Welsh and Shetland ponies, were underrepresented. In addition, age and sex were not associated with the risk for EHM. The main limitation was that it was a retrospective analysis with some flaws of documentation. It can be concluded that breed was a significant risk factor for developing EHM during this outbreak.

Gait acquisition and analysis system for osteoarthritis based on hybrid prediction model

Osteoarthritis (OA) is the most common type of joint-related diseases, which affects millions of people worldwide. Expensive and time-consuming medical imaging can provide precise structural description of knee joints, but lacks the functional descriptions. Gait analysis can provide functional descriptions of knee joints. However, orthopedic surgeons always observe the patient’s gait qualitatively and perform subjective assessments through rating scales at present due to the lack of a quantitative gait analysis system. To solve these problems, a gait acquisition and analysis system is developed to provide a cheap, easy-to-use solution for quantitative recording and functional description of OA patients. Firstly, an automatic gait acquisition platform is designed for the clinical setting based on the RGB-D camera and the developed software of gait data recording. In addition, the effective working space of gait acquisition platform is evaluated for clinical applications by comparing with the ground-truth from infrared optical trackers. Secondly, the acquired gait data is analyzed with a novel hybrid prediction model to assess the gait anomalies quantitatively and objectively. In the hybrid model, the extracted features of gait data contain the manually-extracted features and the automatically-extracted features from Long Short-Term Memory network. Experimental results on real patients demonstrate that the proposed gait analysis system can quantitatively predict gait anomalies with a high accuracy of 98.77 %. Therefore, this gait acquisition and analysis system achieves quantitative recording and objective assessment of gait anomalies for clinical OA treatments.

Machine learning-based motor assessment of Parkinson’s disease using postural sway, gait and lifestyle features on crowdsourced smartphone data

Objectives: Remote assessment of gait in patients’ homes has become a valuable tool for monitoring the progression of Parkinson’s disease (PD). However, these measurements are often not as accurate or reliable as clinical evaluations because it is challenging to objectively distinguish the unique gait characteristics of PD. We explore the inference of patients’ stage of PD from their gait using machine learning analyses of data gathered from their smartphone sensors. Specifically, we investigate supervised machine learning (ML) models to classify the severity of the motor part of the UPDRS (MDS-UPDRS 2.10-2.13). Our goals are to facilitate remote monitoring of PD patients and to answer the following questions: (1) What is the patient PD stage based on their gait? (2) Which features are best for understanding and classifying PD gait severities? (3) Which ML classifier types best discriminate PD patients from healthy controls (HC)? and (4) Which ML classifier types can discriminate the severity of PD gait anomalies? Methodology: Our work uses smartphone sensor data gathered from 9520 patients in the mPower study, of whom 3101 participants uploaded gait recordings and 344 subjects and 471 controls uploaded at least 3 walking activities. We selected 152 PD patients who performed at least 3 recordings before and 3 recordings after taking medications and 304 HC who performed at least 3 walking recordings. From the accelerometer and gyroscope sensor data, we extracted statistical, time, wavelet and frequency domain features, and other lifestyle features were derived directly from participants’ survey data. We conducted supervised classification experiments using 10-fold cross-validation and measured the model precision, accuracy, and area under the curve (AUC). Results: The best classification model, best feature, highest classification accuracy, and AUC were (1) random forest and entropy rate, 93% and 0.97, respectively, for walking balance (MDS-UPDRS-2.12); (2) bagged trees and MinMaxDiff, 95% and 0.92, respectively, for shaking/tremor (MDS-UPDRS-2.10); (3) bagged trees and entropy rate, 98% and 0.98, respectively, for freeze of gait; and (4) random forest and MinMaxDiff, 95% and 0.99, respectively, for distinguishing PD patients from HC. Conclusion: Machine learning classification was challenging due to the use of data that were subjectively labeled based on patients’ answers to the MDS-UPDRS survey questions. However, with use of a significantly larger number of subjects than in prior work and clinically validated gait features, we were able to demonstrate that automatic patient classification based on smartphone sensor data can be used to objectively infer the severity of PD and the extent of specific gait anomalies.

Phase-Synchronized Assistive Torque Control for the Correction of Kinematic Anomalies in the Gait Cycle.

Gait anomalies give rise to several clinical problems in stroke survivors, which restrict their functional mobility and have a negative impact on their quality of life. Robotics-aided gait training post-stroke has proven capable of improving patients' functional walking, but so far it has not performed significantly better than conventional therapy. We hypothesize that an exoskeleton-based training program, aimed at correcting deficits in the leg joints' movement, could produce greater improvements in gait function than conventional therapy. As a first step towards testing this hypothesis, we designed an exoskeleton control to correct a typical kinematic deficit post-stroke, namely, reduced knee flexion on the paretic side during swing. The proposed control attempts to minimize this deficit by delivering assistive torque synchronized with the continuous phase of the patient's gait. Nine healthy male participants walked in a unilateral cable-driven exoskeleton while subject to an artificial knee flexion impairment produced by a custom-made knee brace. The experiments employed a treadmill featuring a variable-velocity control to allow self-selected gait speed. The artificial impairment by itself caused a significant reduction in peak flexion angle (p = 0.000129). Exoskeleton assistance compensated most of the knee flexion deficit, yielding no significant difference with unrestricted flexion (p = 0.3393). No significant changes in self-selected gait speed or stride frequency were detected. The proposed control can be expanded to correct motion deficits in other joints at different stages of the gait cycle.

Symmetry of Gait in Underweight, Normal and Overweight Children and Adolescents.

Abnormal excess or lack of body mass can influence gait patterns, but in some cases such differences are subtle and not easy to detect, even with quantitative techniques for movement analysis. In these situations, the study of trunk accelerations may represent an effective way to detecting gait anomalies in terms of symmetry through the calculation of Harmonic Ratio (HR), a parameter obtained by processing trunk accelerations in the frequency domain. In the present study we used this technique to assess the existence of differences in HR during gait in a cohort of 75 healthy children and early adolescents (aged 7–14 years) stratified into 3 equally-sized age and gender-matched groups (Underweight: UW; Normal Weight: NW; Overweight: OW). The accelerometric signal, acquired using a single wearable inertial sensor, was processed to calculate stride length, speed, cadence and HR in antero-posterior, vertical and medio-lateral directions. No differences in spatio-temporal parameters were found among groups, while the HR in the medio-lateral direction was found significantly lower in UW children, while OW exhibited the highest values. On the basis of the results obtained, HR appears capable of discriminating gait symmetry in children with different body mass even when conventional gait parameters are unchanged.

Gait analysis following release of the short external rotators during an anterior approach for total hip arthroplasty.

Cadaveric and clinical studies suggest surgical release of the short external rotators is sometimes necessary to improve exposure during total hip arthroplasty (THA) using an anterior approach. The purpose of this study was to determine the impact of those surgical releases on gait following THA. 15 patients undergoing THA using an anterior approach, anterior approach with surgical releases, or posterior approach underwent 3-D gait analysis preoperatively, and at 6 and 12 weeks postoperatively. At each time point, temporal parameters, kinematics, and kinetics were compared. The anterior approach was compared to the anterior approach cohort with surgical releases, and the surgical release cohort was compared to a posterior approach cohort. The mean change score between preoperative and 6 weeks, and 6-week to 12-week analyses were analysed. There were no demographic differences between the groups. There were no significant differences between the groups for the temporal parameters and kinematic analyses at either time point comparison. The surgical release cohort had a lower hip internal rotation moment compared to the anterior approach cohort for the 6- to 12-week comparison ( p = 0.05), and compared to the posterior approach cohort for the preoperative to 6-week ( p = 0.03) and 6- to 12-week comparison ( p = 0.02). Releasing the short external rotators during an anterior approach did not cause significant temporal and kinematic changes after THA. However, small changes in hip internal rotation moments can be expected. These findings should be correlated with patient-reported outcome measures to determine if these gait anomalies predict poor outcome following THA.

Detection and Diagnosis of Paralysis Agitans

Humans’ daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems, such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people’s living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7%. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications.

Classification of gait anomalies from kinect

A persons manner of walking or their gait is an important feature in human recognition and classification tasks. Gait serves as an unobtrusive biometric modality which yields high quality results. In comparison with other biometric modalities, its main strength is its performance even in data that are captured at distance or at low resolution. In this paper, we present an algorithm for classification of gait disorders arising from neuro-degenerative diseases such as Parkinson and Hemiplegia. We focus on motion anomalies such as tremor, partial paralysis, gestural rigidity and postural instability. The analysis and classification of such motions are challenging since they consist of a multiplicity of subtle formations while lacking a regular pattern or major cycle. We introduce a gait representation which is invariant to the walking cycle and yields an efficient similarity metric. Our method performs on the joints’ motion trajectories of a 3D human skeleton captured by a Kinect sensor. The algorithm is robust, in that it does not require calibration, synchronization or a careful capturing setup. We demonstrate its efficiency by classifying different degenerative cases with high accuracy even in the presence of noise and low-resolution acquisition.

Exome sequencing coupled with mRNA analysis identifies NDUFAF6 as a Leigh gene.

We report here the case of a young male who started to show verbal fluency disturbance, clumsiness and gait anomalies at the age of 3.5years and presented bilateral striatal necrosis. Clinically, the diagnosis was compatible with Leigh syndrome but the underlying molecular defect remained elusive even after exome analysis using autosomal/X-linked recessive or de novo models. Dosage of respiratory chain activity on fibroblasts, but not in muscle, underlined a deficit in complex I. Re-analysis of heterozygous probably pathogenic variants, inherited from one healthy parent, identified the p.Ala178Pro in NDUFAF6, a complex I assembly factor. RNA analysis showed an almost mono-allelic expression of the mutated allele in blood and fibroblasts and puromycin treatment on cultured fibroblasts did not lead to the rescue of the maternal allele expression, not supporting the involvement of nonsense-mediated RNA decay mechanism. Complementation assay underlined a recovery of complex I activity after transduction of the wild-type gene. Since the second mutation was not detected and promoter methylation analysis resulted normal, we hypothesized a non-exonic event in the maternal allele affecting a regulatory element that, in conjunction with the paternal mutation, leads to the autosomal recessive disorder and the different allele expression in various tissues. This paper confirms NDUFAF6 as a genuine morbid gene and proposes the coupling of exome sequencing with mRNA analysis as a method useful for enhancing the exome sequencing detection rate when the simple application of classical inheritance models fails.

Predicting motor outcomes with 3 month prone hip angles in premature infants.

This study used kinematic analysis to identify a reliable and rapid assessment method for abnormal patterns of motor development in preterm infants. In a retrospective analysis, we examined video of n= 35 preterm infants at 3mo corrected age (CA) who had concurrent Test of Infant Motor Performance (TIMP) scores. Hyperflexion at the hip produces common gait anomalies seen in children with CP, therefore we analyzed hip angle in the prone head lift position at 3 months CA. Magnetic Resonance Spectroscopy (MRS) was performed at term equivalent (n= 23) and Bayley-III neurodevelopmental tests were performed at 1 year (n= 28). We correlated hip angles with TIMP and Bayley-III scores, and MRS neuronal metabolites. Hip angle positively correlated with TIMP at 3 months (r= 0.642, p≤ 0.001), but not with Bayley-III at 1 year (r= 0.122, p= 0.529). Hip angle correlated negatively with myo-inositol (mI) ratios in frontal white matter tracts (mI/Cr r= -0.520, p= 0.011). These results suggest prone hip angle may be a quantitative proxy for the 42-item TIMP at 3 months, and that hypertonicity in the hip flexor musculature is a manifestation of white matter metabolic abnormalities (elevated mI ratios) that may indicate occult white matter injury.