Video Gait Research Articles

Hardware methods for dynamic stereotype gait training in patients of older age groups

Purpose of the study: to evaluate the effectiveness of the hardware biofeedback video gait reconstruction for training balance and dynamic stereotype in patients of the older age group, in order to prevent the risk of falls. The study included 40 participants, aged 78.2±9.4 years. The patients were divided into two groups: group 1, main (n=20), and group 2, control (n=32). All patients received a standard course of treatment, including group therapeutic exercises and massage according to the relaxing method of the classical therapeutic collar zone massage. The duration of the rehabilitation course in a round-the-clock hospital was 10 days. In addition to the basic course of rehabilitation, the patients of the main group had training using the C–Mill biofeedback sensor treadmill. As a result of the study, a significant improvement in the submetric indicators of the dynamic gait stereotype and the degree of mobility was noted in the patients of the main group, compared to the control group. The obtained results of the effective use of the method of hardware balance training indicate the prospect of including this technology in the rehabilitation programs for elderly patients to improve the dynamic stereotype and increase stability during walking.

Occlusion-Aware Human Mesh Model-Based Gait Recognition

Partial occlusion of the human body caused by obstacles or a limited camera field of view often occurs in surveillance videos, which affects the performance of gait recognition in practice. Existing methods for gait recognition against occlusion require a bounding box or the height of a full human body as a prerequisite, which is unobserved in occlusion scenarios. In this paper, we propose an occlusion-aware model-based gait recognition method that works directly on gait videos under occlusion without the above-mentioned prerequisite. Specifically, given a gait sequence that only contains non-occluded body parts in the images, we directly fit a skinned multi-person linear (SMPL)-based human mesh model to the input images without any pre-normalization or registration of the human body. We further use the pose and shape features extracted from the estimated SMPL model for recognition purposes, and use the extracted camera parameters in the occlusion attenuation module to reduce intra-subject variation in human model fitting caused by occlusion pattern differences. Experiments on occlusion samples simulated from the OU-MVLP dataset demonstrated the effectiveness of the proposed method, which outperformed state-of-the-art gait recognition methods by about 15% rank-1 identification rate and 2% equal error rate in the identification and verification scenarios, respectively.

A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions - A Deep Learning Approach.

This study examined the effectiveness of a vision-based framework for multiple sclerosis (MS) and Parkinson's disease (PD) gait dysfunction prediction. We collected gait video data from multi-view digital cameras during self-paced walking from MS, PD patients and age, weight, height and gender-matched healthy older adults (HOA). We then extracted characteristic 3D joint keypoints from the collected videos. In this work, we proposed a data-driven methodology to classify strides in persons with MS (PwMS), persons with PD (PwPD) and HOA that may generalize across different walking tasks and subjects. We presented a comprehensive quantitative comparison of 16 diverse traditional machine and deep learning (DL) algorithms. When generalizing from comfortable walking (W) to walking-while-talking (WT), multi-scale residual neural network achieved perfect accuracy and AUC for classifying individuals with a given gait disorder; for subject generalization in W trials, residual neural network resulted in the highest accuracy and AUC of 78.1% and 0.87 (resp.), and 1D convolutional neural network (CNN) had highest accuracy of 75% in WT trials. Finally, when generalizing over new subjects in different tasks, again 1D CNN had the top classification accuracy and AUC of 79.3% and 0.93 (resp.). This work is the first attempt to apply and demonstrate the potential of DL with a multi-view digital camera-based gait analysis framework for neurological gait dysfunction prediction. This study suggests the viability of inexpensive vision-based systems for diagnosing certain neurological disorders.

Inter-rater reliability between osteopaths of differing clinical experience on sagittal plane running gait analysis: A pilot study

ObjectiveClinical running analysis is commonly employed in the screening of running related injuries. Currently, there remains little research exploring the use of clinical running analysis within the osteopathic profession. Hence, the aim of this pilot study is to investigate the inter-rater reliability of sagittal plane visual gait observation amongst osteopathic practitioners. MethodsFourteen osteopaths were recruited through an online platform. Each participant completed five, 11-item gait analysis forms, with each form corresponding to a different running gait video recorded in the sagittal plane. Krippendorff's alpha (Kα) was calculated to determine the inter-rater reliability between all participants, and between groups based on years of clinical experience. ResultsVery low inter-rater reliability was found between all participants (Kα<0.67) across all questions. Strong reliability was found for two questions answered by the 5–10 years’ experience group (Kα<0.8155) with regard to foot strike in the runners. DiscussionThe poor inter-rater reliability found in this pilot study, is in disagreement with previous research that has identified high inter-rater reliability with practitioners of varied clinical experience on a similar gait analysis task. These discrepancies may be explained by a range of methodological differences and limitations, chiefly the sample size and the objective nature of the kinematic observation tasks. ConclusionThe results of this pilot study indicate there is very low reliability between this group of clinicians interpretation of running gait. Building on from this, future research should investigate clinician inter-rater reliability using more objective gait analysis measures.

Annotator-dependent uncertainty-aware estimation of gait relative attributes

In this paper, we describe an uncertainty-aware estimation framework for gait relative attributes. We specifically design a two-stream network model that takes a pair of gait videos as input. It then outputs a corresponding pair of Gaussian distributions of gait absolute attribute scores and annotator-dependent gait relative attribute label distributions. Moreover, we propose a differentiable annotator-independent uncertainty layer to estimate the gait relative attribute score distribution from the absolute distributions then map it to a relative attribute label distribution using the computation of cumulative distribution functions. Furthermore, we propose another annotator-dependent uncertainty layer to estimate the uncertainty on the gait relative attribute labels in terms of a set of trainable transition matrices. Finally, we design a joint loss function on the relative attribute label distribution to learn the model parameters. Experiments on two gait relative attribute datasets demonstrated the effectiveness of the proposed method against baselines in quantitative and qualitative evaluations.

Enhanced Spatial-Temporal Salience for Cross-View Gait Recognition

Gait recognition can be used in person identification and re-identification by itself or in conjunction with other biometrics. Although gait has both spatial and temporal attributes, and it has been observed that decoupling spatial feature and temporal feature can better exploit the gait feature on the fine-grained level. However, the spatial-temporal correlations of gait video signals are also lost in the decoupling process. Direct 3D convolution approaches can retain such correlations, but they also introduce unnecessary interferences. Instead of common 3D convolution solutions, this paper proposes an integration of decoupling process into a 3D convolution framework for cross-view gait recognition. In particular, a novel block consisting of a Parallel-insight Convolution layer integrated with a Spatial-Temporal Dual-Attention (STDA) unit is proposed as the basic block for global spatial-temporal information extraction. Under the guidance of the STDA unit, this block can well integrate spatial-temporal information extracted by two decoupled models and at the same time retain the spatial-temporal correlations. In addition, a Multi-Scale Salient Feature Extractor is proposed to further exploit the fine-grained features through context awareness extension of part-based features and adaptively aggregating the spatial features. Extensive experiments on three popular gait datasets, namely CASIA-B, OULP and OUMVLP, demonstrate that the proposed method outperforms state-of-the-art methods.

Reliability and validity analysis of personality assessment model based on gait video.

Personality affects an individual’s academic achievements, occupational tendencies, marriage quality and physical health, so more convenient and objective personality assessment methods are needed. Gait is a natural, stable, and easy-to-observe body movement that is closely related to personality. The purpose of this paper is to propose a personality assessment model based on gait video and evaluate the reliability and validity of the multidimensional model. This study recruited 152 participants and used cameras to record their gait videos. Each participant completed a 44-item Big Five Inventory (BFI-44) assessment. We constructed diverse static and dynamic time-frequency features based on gait skeleton coordinates, interframe differences, distances between joints, angles between joints, and wavelet decomposition coefficient arrays. We established multidimensional personality trait assessment models through machine learning algorithms and evaluated the criterion validity, split-half reliability, convergent validity, and discriminant validity of these models. The results showed that the reliability and validity of the Gaussian process regression (GPR) and linear regression (LR) models were best. The mean values of their criterion validity were 0.478 and 0.508, respectively, and the mean values of their split-half reliability were all greater than 0.8. In the formed multitrait-multimethod matrix, these methods also had higher convergent and discriminative validity. The proposed approach shows that gait video can be effectively used to evaluate personality traits, providing a new idea for the formation of convenient and non-invasive personality assessment methods.

Can aquatic exercises contribute to the improvement of the gait stereotype function in patients with Long COVID outcomes?

A variety of rehabilitation programmes can be offered to Long COVID patients, specifically physical training. Indeed 90% of these patients reports impairments of verticalization, stability and spatial orientation, making difficult exercise in the gym. The aim of our study was to assess the effectiveness and safety of aquatic exercise techniques as part of a comprehensive rehabilitation program for patients with Long COVID. The first of a two-stage program involved development of aquatic exercises technique, which was evaluated in 12 patients with impaired upright posture control before and after exercising by "Habilect" video gait analysis system. During the second phase, effectiveness and safety of aqua exercises were tested in water pool as part of a comprehensive rehabilitation programme conducted in 23 patients with Long COVID outcomes. Physical examination, 6-minute step test, Euro-QL-5D questionnaire, Borg scale, laser Doppler flowmetry, cardiointervalography, and spirometry were performed before and after the aquatic exercises program. After the training with aquatic exercises, indices of deviations of the main body axes of the head and the body mass centre ameliorated, as well as direction of body movement vector decreased (p<0.05). This study demonstrated a statistically significant improvement in exercise tolerance in both groups, as measured by the 6-minute step test after rehabilitation. The comparison group averaged 236.7 metres [126; 380] (T=8, p=0.047) after the rehabilitation course and the intervention group averaged 233.71 metres [150; 320] (T=8.0, p=0.047). When tested with the Euro-QL-5D questionnaire, a post-treatment improvement was noted in the comparison group on the anxiety/depression subscale (3 [3;3] (T=0, p=0.043)). In the intervention group, laser Doppler flowmetry revealed a statistically significant increase in microcirculation (6.36 standard units after rehabilitation) [5.54; 8.17] (T=7.0, p=0.004), and a decrease of oxidative metabolism index of 6.89 standard units. [4.76; 6.96] (T=4.0, p=0.03). No serious adverse events were reported. In conclusion, the developed aquatic exercises technique seems to contribute to recovery of impaired upright posture and motor function, normalizing the walking pattern.

Gait analysis dataset of healthy volunteers and patients before and 6 months after total hip arthroplasty

Clinical gait analysis is a promising approach for quantifying gait deviations and assessing the impairments altering gait in patients with osteoarthritis. There is a lack of consensus on the identification of kinematic outcomes that could be used for the diagnosis and follow up in patients. The proposed dataset has been established on 80 asymptomatic participants and 106 patients with unilateral hip osteoarthritis before and 6 months after arthroplasty. All volunteers walked along a 6 meters straight line at their self-selected speed. Three dimensional trajectories of 35 reflective markers were simultaneously recorded and Plugin Gait Bones, angles, Center of Mass trajectories and ground reaction forces were computed. Gait video recordings, when available, anthropometric and demographic descriptions are also available. A minimum of 10 trials have been made available in the weka file format and C3D file to enhance the use of machine learning algorithms. We aim to share this dataset to facilitate the identification of new movement-related kinematic outcomes for improving the diagnosis and follow up in patients with hip OA.

2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings.

Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer’s assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders.

Показатели кинетики локомоторных стереотипов у здоровых детей в различных скоростных диапазонах передвижения

Purpose To determine the normative values of the kinetic parameters of gait in healthy children in different speed ranges of movement. Materials and methods Locomotor profile assessment by video gait analysis (CGA) was performed on an outpatient basis in 27 children (54 limbs), aged 10–13 years. The following variables were used and processed: peak negative hip, knee and ankle power (W/kg) (relaxation); peak positive power of the hip, knee and ankle (support push) joints (W/kg) (generation); the total positive and total negative power was calculated(for the hip, knee and ankle joints); total peak power; net peak power values and overall mechanical efficiency. Kinetics indicators were generalized according to the ranges of ranking of the absolute walking speed (km/h), taking into account the weight of patients. Quantitative characteristics are presented in the table as a median, percentile range of distribution of values (25÷75 %) and the number of observations (n) equal to the number of limbs. Results The normative kinetic parameters of gait are presented at a speed of movement in the range from 1.1 to 5.0 km/h in healthy children aged 10–13 years. A strong correlation was found between the values of the power of the reference shock during the formation of the reciprocal inhibition reflex of the shin sprain and the speed parameters of movement (r = -0.925, n = 142 and p &lt; 0.001). To change the speed of movement, the motor locomotor stereotype was changed with the redistribution of the load from one joint muscle to another. If the share of the contribution of the power of the muscles of the hip joint increased, on average, by 10.8 %, and of the knee joint – by 15.3 %, then the share of the contribution of the ankle joint decreased by 16.8 %. Discussion In locomotor stereotypes in the examined children, a significant positive correlation was found between walking speed and the total (r = 0.907, n = 104) and useful peak power (r = 0.475, n = 104) of the joint muscles, while the maximum median values of the useful total peak the power of all joint muscles was recorded at a speed of movement in the range from 3.1 to 4.0 km/h.

High preoperative gait variability is a prognostic predictor of gait and balance in Parkinson disease patients with deep brain stimulation

IntroductionThe objective biomarker for prediction of gait and balance in the long-term follow-up of Parkinson's disease(PD) patients with subthalamic nucleus deep brain stimulation(STN-DBS) has not yet been elucidated. We investigated the value of pre-operative quantitative gait parameters for the prediction of long-term prognosis of gait in PD patients with DBS. MethodsWe retrospectively collected gait videos(both medication ON/OFF states) of PD patients recorded as preoperative evaluation before STN DBS. We enrolled patients who were followed-up for more than 5 years after the surgery from 2006 to 2014. We derived objective gait parameters from video-based gait analysis algorithm. We defined the clinical milestones of frequent falling, impaired walking, and loss of autonomy based on the Unified Parkinson's disease rating scale and Hoehn and Yahr stage, which were regularly followed up to 156 months after surgery. We calculated hazard ratios(HRs) of baseline gait parameters for predicting the clinical milestones. ResultsA total of 96 gait videos from 63 PD patients were analyzed. The mean follow-up duration(standard deviation) was 88.0(34.2) months after surgery. Relatively high (>mean + 1 standard deviation) variability for step length, step time and stride time (HR = 2.92[1.02–8.33], 3.91[1.38–11.11] and 7.16[2.09–24.52],respectively) in medication-ON state significantly predicted reaching any of the three clinical milestones of frequent falling, impaired walking and loss of autonomy. Gait parameters from the medication-OFF state did not predict any clinical milestone. ConclusionsHigh preoperative gait variability from the medication-ON state predicts long-term outcomes for gait and balance in PD patients with STN DBS.

Assessment of the effect of methods of robotic medical rehabilitation with biofeedback on quality of life and psychoemotional status in patients after CVA with hemiparesis

Recently, to restore the functions of the affected limbs in patients who have had an ischemic stroke with movement disorders in the form of hemiparesis, non-drug methods have been actively used, including physiotherapy, physiotherapy exercises, kinesiotherapy and, especially, robotic biomechanics. The paper presents data on the effectiveness of using gait training on the BFB equipment with video gait reconstruction and biofeedback (BFB) in the late recovery period of medical rehabilitation. Material and methods. The study included 80 patients who underwent ischemic stroke with movement disorders in the form of hemiparesis with increased muscle tone by the type of spasticity. The patients were divided into 2 groups comparable in terms of clinical and functional characteristics: control group — 40 patients who received standard drug therapy and medical rehabilitation and the main group — 40 patients receiving standard drug therapy and medical rehabilitation (exercise therapy, medical massage, mechanotherapy) who were prescribed gait training with video reconstruction on a biofeedback system (BFB) «C-Mill». Subjective indicators of cognitive impairment (Montreal Cognitive Assessment), psychoemotional defects (Hospital Anxiety and Depression Scale) and quality of life (EQ-5D questionnaire) were assessed. Results. As a result of the studies, it has been proved that the inclusion of a gait stereotype with video reconstruction on a biofeedback system (BFB) «C-Mill» in the rehabilitation training program contributes to a significantly more pronounced improvement of cognitive functions and the quality of life, as well as to a decrease in the degree of psychoemotional disorders in patients after ischemic stroke with movement disorders in the form of hemiparesis with increased muscle tone by the type of spasticity. This is confirmed by the increase in the total indicator of the MoCA scale to 26,5±0,5 points, which corresponds to the reference values. In the patients of the control group, less pronounced results were obtained; the total indicator of the MoCA scale was 25,0±1,0 points, which is lower than normal; similar dynamics was observed when studying the data of the Hospital Anxiety and Depression Scale (HADS) and the EQ-5D questionnaire, which indicated the quality of life improvement. Conclusion. The inclusion of gait training on a biofeedback system (BFB) «C-Mill» by the method of «imposing» in medical rehabilitation of patients after ischemic stroke with motor impairments in the form of hemiparesis with increased muscle tone contributes to a significant improvement in cognitive functions, a decrease in the level of anxiety and depression and improved quality of life.

Anonymization of Human Gait in Video Based on Silhouette Deformation and Texture Transfer

These days, a lot of videos are uploaded onto web-based video sharing services such as YouTube. These videos can be freely accessed from all over the world. On the other hand, they often contain the appearance of walking private people, which could be identified by silhouette-based gait recognition techniques rapidly developed in recent years. This causes a serious privacy issue. To avoid it, this paper proposes a method for anonymizing the appearance of walking people, namely human gait, in video. In the proposed method, we first crop human regions from all frames in an input video and binarize them to get their silhouettes. Next, we slightly deform the silhouettes from the aspects of static body shape and dynamic walking rhythm so that the person in the input video cannot be correctly identified by gait recognition techniques. After that, the textures of the original human regions are transferred onto the deformed silhouettes. We achieve this by a displacement field-based approach, which is training-free and thus robust to a variety of clothes. Finally, the anonymized human regions with the transferred textures are filled back into the input video. In the results of our experiments, we successfully degraded the accuracy of CNN-based gait recognition systems from 100% to 1.57% in the lowest case without yielding serious distortion in the appearance of the human regions, which demonstrated the effectiveness of the proposed method.

Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation.

A gait feature analysis method based on AlphaPose human pose estimation fused with sample entropy is proposed to address complicated, high-cost, and time-consuming postoperative rehabilitation of patients with joint diseases. First, TensorRT was used to optimize the inference of AlphaPose, which consists of the target detection algorithm YOLOv3 and the pose estimation algorithm. It can speed up latency and throughput by about 2.5 times while maintaining the algorithm's accuracy. Second, the optimized human posture estimation algorithm AlphaPose_trt was used to process gait videos of healthy people and patients with knee arthritis. The joint point motion trajectories of the two groups were extracted, and the sample entropy algorithm quantified the joint trajectory signals for feature analysis. The experimental results showed significant differences in the entropy of the heel and ankle joint motion signals between healthy people and arthritic patients (p < 0.01), which can be used to identify patients with knee arthritis. This technique can assist doctors in determining needed postoperative joint surgery rehabilitation.

CASIA-E: A Large Comprehensive Dataset for Gait Recognition.

Gait recognition plays a special role in visual surveillance due to its unique advantage, e.g., long-distance, cross-view and non-cooperative recognition. However, it has not yet been widely applied. One reason for this awkwardness is the lack of a truly big dataset captured in practical outdoor scenarios. Here, the "big" at least means: (1) huge amount of gait videos; (2) sufficient subjects; (3) rich attributes; and (4) spatial and temporal variations. Moreover, most existing large-scale gait datasets are collected indoors, which have few challenges from real scenes, such as the dynamic and complex background clutters, illumination variations, vertical view variations, etc. In this article, we introduce a newly built big outdoor gait dataset, called CASIA-E. It contains more than one thousand people distributed over near one million videos. Each person involves 26 view angles and varied appearances caused by changes of bag carrying, dressing and walking styles. The videos are captured across five months and across three kinds of outdoor scenes. Soft biometric features are also recorded for all subjects including age, gender, height, weight, and nationality. Besides, we report an experimental benchmark and examine some meaningful problems that have not been well studied previously, e.g., the influence of million-level training videos, vertical view angles, walking styles, and the thermal infrared modality. We believe that such a big outdoor dataset and the experimental benchmark will promote the development of gait recognition in both academic research and industrial applications.

Pengaruh Penggunaan Sandal Drop Foot Terhadap Perbaikan Pola Jalan Penderita Drop Foot Akibat Kusta Di Rumah Sakit Kusta Sumberglagah Mojokerto

In handling Drop Foot due to leprosy, Prosthetic Orthotics can provide services in the form of ortosis, namely Sandal Drop Foot, which aims to reduce the drop foot in the ankle joint caused by a decrease in flexor dorsal muscle tone. Sampling is done by purposive sampling. The sample count consisted of 10 drop foot sufferers due to leprosy at Sumberglagah Mojokerto Leprosy Hospital. The instrument used is Rivermead Visual Gait Assessment form, video gait analysis equipment (handycam, goniometre, stationery). Drop Foot sandals have a big effect on leprosy patients who experience drop foot in improving road patterns compared to without when using Drop foot sandals. Drop foot sandals are sutu aids or correction tools to prevent prolonged drop foot or prevent further disability, but can not cure and restore normal in people with drop foot due to leprosy. The purpose of this study is to find out the Effect of Using Drop Foot Sandals On Improving Road Patterns of People With Drop Foot Due to Leprosy. This study uses the research design" Quasi experiment pre post test with out control design" one groups pre and post design, where in this study there is only one group of conscientious subjects who will be measured the pattern of the path before and after being given treatment. From the alternative test Wilcoxon obtained a value of significance with a value of p = 0.004, because p &lt; 0.05, it can be said that there is an effect on the use of Drop Foot Sandals on improving the road pattern of drop foot sufferers due to leprosy

Pengaruh Penggunaan Sandal Drop Foot Terhadap Perbaikan Pola Jalan Penderita Drop Foot Akibat Kusta Di Rumah Sakit Kusta Sumberglagah Mojokerto

In handling Drop Foot due to leprosy, Prosthetic Orthotics can provide services in the form of ortosis, namely Sandal Drop Foot, which aims to reduce the drop foot in the ankle joint caused by a decrease in flexor dorsal muscle tone. Sampling is done by purposive sampling. The sample count consisted of 10 drop foot sufferers due to leprosy at Sumberglagah Mojokerto Leprosy Hospital. The instrument used is Rivermead Visual Gait Assessment form, video gait analysis equipment (handycam, goniometre, stationery). Drop Foot sandals have a big effect on leprosy patients who experience drop foot in improving road patterns compared to without when using Drop foot sandals. Drop foot sandals are sutu aids or correction tools to prevent prolonged drop foot or prevent further disability, but can not cure and restore normal in people with drop foot due to leprosy. The purpose of this study is to find out the Effect of Using Drop Foot Sandals On Improving Road Patterns of People With Drop Foot Due to Leprosy. This study uses the research design" Quasi experiment pre post test with out control design" one groups pre and post design, where in this study there is only one group of conscientious subjects who will be measured the pattern of the path before and after being given treatment. From the alternative test Wilcoxon obtained a value of significance with a value of p = 0.004, because p &lt; 0.05, it can be said that there is an effect on the use of Drop Foot Sandals on improving the road pattern of drop foot sufferers due to leprosy

Fresh Water Aquatic Training in Patients with Upright Posture Maintaining Disorders

The use of the fresh water aquatic training course, as a more gentle training method, may allow patients to ensure effective restoration of muscle functions responsible for maintaining an upright body position. A more accurate control of the course results can be performed using a virtual analysis carried out using the «Habilect» system that allows to determine the body parts attitude. Aim. To study the effect of training in fresh water using the Habilect system based on the Microsoft Kinect infrared sensor (video stabilometry) on motor functions that contribute to maintaining an upright body posture in patients with mild gait disturbances. Material and methods. An open descriptive study was conducted including 12 patients (7 men, 5 women), aged 40 to 62 years, with upright posture maintaining disorders, which correspond to the functional diagnosis encoded by the ICF «Gait Stereotype Functions» B770.1 – mild violations (5–24%). A group of subjects (n = 12), in addition to basic therapy and training with an exercise therapy instructor, underwent aquatic training in fresh water for two weeks (30 minutes, 6 days a week). Assessment methods: the research was carried out using the Habilect gait video analysis system before and after the rehabilitation course. The χ2 test was used to assess the significance of differences between groups of qualitative variables. When analyzing quantitative variables, the Shapiro-Wilk’s (W) test was performed. For abnormal distribution, the data is in Me format [Q25-Q75]. The Wilcoxon T-test was used to assess the significance of differences in quantitative variables of the two studied groups. The processing of the obtained research results was carried out using the Statistica for Windows, v. 8.0 (StatSoft Inc., USA) and Microsoft Excel (Microsoft, USA). The significance of the differences was considered established at p &lt;0.05. Result and discussion. When examining the amplitude of body deflection along the X-axis before training, they were 3.25 cm [-98 cm; 93.9 cm], after – -9.96 cm [-100.92 cm; -81.96 cm], on the Y-axis before training – -29.01 cm [-29.01 cm; 13.76 cm], after –-30.59 cm [-30.59 cm; 31.09 cm], on the Z-axis before training – 388.1 cm [369.22 cm; 393.39 cm], after training – 380.96 cm [377.98cm, 400.05 cm], deviation of the body movement vector before training 16.45 cm [7.46 cm; 338.67 cm], after training – 324.7 cm [324.7 cm; 342.56 cm]. When examining the amplitude of head deflection along the X-axis before training, they were -0.92 cm [-1.24 cm; -0.92 cm], after – 1.5 cm [-10.19 cm; 2.38 cm], Y-axis before training – 125.33 cm [61.13 cm; 128.94 cm], after – 107.42 cm [52.49 cm; 107.42 cm], along the Z-axis before training – -8.59 cm [-8.97 cm; -5.33 cm], after training – -14.89 cm [-14.89 cm, -3.45cm]. When calculating the increase in deviation (deviations of the main body axes from the initial value) using the Wilcoxon T-test revealed statistically significant deviations in the X-axis (an increase of 306.5%, p = 0.0504), the Z-axis (an increase of 112.68%, p =0.0225) and the Body Angle parameter (an increase of 1973.86% p = 0.0323). When calculating the increase in the deviation of the head axes from the initial value using the Wilcoxon T-test, statistically significant deviations were revealed along the X axis (increase of 163.04%, p = 0.0280), the Y axis (increase of 85.71%, p = 0.0199) and the parameter Z (an increase of 173.34% p = 0.0292). The study revealed a decrease in the body axes deviations amplitude in all 3 planes, which indicates an improvement in the work of all brain parts that are responsible for the coordination of motor functions and their vegetative support, an improvement in functional interaction within individual muscle chains. The reduction in the head and neck muscles in compensatory balancing participation during walking and maintaining a vertical body posture mainly due to the muscles of the lower extremities and pelvis contributes to the prevention of arterial and venous circulation disorders in the head and neck and makes training not only more effective, but also safer. Conclusion. Due to the decrease in the amplitude of deviations along all three axes (Z, Y, X), the course of aquatic training contributes to the correction of upright posture maintaining disorders, a statistically significant decrease in the amplitude of head and neck movements.

Minimal clinically important difference of the gait assessment and intervention tool for adults with stroke

BackgroundThe Gait Assessment and Intervention Tool (GAIT) provides a valid and reliable gait quality measure for adults with stroke, however a minimal clinically important difference (MCID) is yet to be determined. Research QuestionWhat is the GAIT MCID in community dwelling adults with a stroke diagnosis? MethodsThe GAIT scores for a consecutive series of 63 adults with stroke, referred for video gait assessment within an outpatient rehabilitation program, were retrospectively identified from a gait laboratory database. Patients were classified by Functional Ambulation Category (FAC) and had walking speed measured, which classified them into one of three walking speed-based ambulatory levels (I.e. household, limited community or community ambulator). Linear regression models were fitted to assess the association between GAIT score and FAC level and GAIT score and ambulatory level. MCIDs were determined based on estimates calculated in the two models ResultsThe FAC of patients ranged from 3 to 5. GAIT score was negatively correlated with FAC level and ambulatory level (ρ = −0.73, p < 0.001 and ρ =-0.69, p < 0.001, respectively). Pairwise comparisons from the models showed absolute mean differences between estimated GAIT scores of 10.84 (95% confidence interval 7.59–14.09) for changes from FAC level 3–4 and 12.13 (8.90–15.36) for household to limited community ambulator, and 5.90 (3.44–8.37) for changes from FAC level 4–5 and 4.39 (2.01–6.76) for limited community to community ambulator. The proposed MCID for FAC level 3 or household ambulators is 11.48, and for FAC level 4 and 5 or limited community/community ambulators is 5.19. SignificanceThe proposed MCIDs represent real changes in gait quality measured by the GAIT for adults with stroke who exhibit lower or higher functional mobility levels. The MCIDs will assist clinicians and researchers using the tool to determine if meaningful change in gait quality has taken place for adults with stroke undergoing rehabilitation.