Walking Conditions Research Articles

Validation of Estimators for Weight-Bearing and Shoulder Joint Loads Using Instrumented Crutches.

This research paper aimed to validate two methods for measuring loads during walking with instrumented crutches: one method to estimate partial weight-bearing on the lower limbs and another to estimate shoulder joint reactions. Currently, gait laboratories, instrumented with high-end measurement systems, are used to extract kinematic and kinetic data, but such facilities are expensive and not accessible to all patients. The proposed method uses instrumented crutches to measure ground reaction forces and does not require any motion capture devices or force platforms. The load on the lower limbs is estimated by subtracting the forces measured by the crutches from the subject's total weight. Since the model does not consider inertia contribution in dynamic conditions, the estimation improves with low walking cadence when walking with the two-point contralateral and the three-point partial weight-bearing patterns considered for the validation tests. The shoulder joint reactions are estimated using linear regression, providing accurate values for the forces but less accurate torque estimates. The crutches data are acquired and processed in real-time, allowing for immediate feedback, and the system can be used outdoors in real-world walking conditions. The validation of this method could lead to better monitoring of partial weight-bearing and shoulder joint reactions, which could improve patient outcomes and reduce complications.

Effect of Rhythmic Finger Movement Training on Freezing of Gait and Electroencephalography Activity in People With Parkinson Disease

We examined the effects of rhythmic finger movement training on freezing of gait (FOG) and electroencephalography (EEG) activity in 3 participants with Parkinson disease (PD). Participants underwent 3 minutes' rhythmic finger movement training twice daily for 4 weeks. We measured walking condition acceleration and EEG activity with and without a doorway, pre- and postintervention. Freezing index (FI), indicating FOG, was calculated from acceleration data. The high walking condition FI observed pretraining with a doorway decreased posttraining. The high pretraining frontal β-band activity decreased posttraining. Therefore, the participants' FOG and β-band activity decreased. Rhythmic finger movement training shows potential as an effective intervention for FOG; more study is needed.

Effects of Customized Biomechanical Footwear on Gait and Balance in Individuals with Polio: A Randomized Controlled Trial

Background Evidence suggests that the factors behind the increased risk of falls among polio survivors are mainly categorized into intrinsic and extrinsic domains. The intrinsic category involves factors like muscle weaknesses, fear of falls and self-reported balance problem. In contrast, extrinsic factors include poor environmental circumstances and ambulatory issues that cause reduced ability to adapt to walking conditions in polio patients. Therefore, the present study aimed to investigate biomechanical footwear’s effects on gait and balance in individuals with polio. Methods A total of 30 participants were randomly divided into two groups of 15 each. The experimental group received customized shoe footwear with features such as medial arch support, metatarsal pad, rear foot control, fibreglass lateral counter, and a rocker sole. The control group did not receive specialized footwear. Results The intervention group showed significant improvements in several gait parameters compared to the control group, including step length (p=0.008), stride length (p=<0.001), step width (p=0.002), cadence (p<0.001) and speed (p=0.0009). The intervention group also showed significant improvements in Berg Balance Scale scores (p=0.07) compared to the control group. Conclusion Biomechanical footwear with medial arch support, metatarsal pad, and rear foot control can improve gait and balance in individuals with polio. Keywords Disability, Gait, Foot Orthosis, Poliovirus.

Walking performance of persons with chronic stroke changed when looking down but not in dimly lit environment.

It is common to walk under different conditions, such as looking straight head, looking down at the feet or in dimly lit environment. The purpose of this study was to determine the impact of these different conditions on walking performance in persons with and without stroke. This was a case-control study. Persons with chronic unilateral stroke and age-matched control (n = 29 each) underwent visual acuity test, Mini Mental Status Examination (MMSE) and joint position sense test of the knee and ankle. The participants walked at their preferred speed under three walking conditions, looking ahead (AHD), looking down (DWN), and in dimly lit environment (DIM). A motion analysis system was used for the recording of the limb matching test and walking tasks. Stroke participants differed from the control group in MMSE, but not in age, visual acuity or joint position sense. For the control group, the differences between the three walking conditions were nonsignificant. For the stroke group, DWN had significantly slower walking speed, greater step width and shorter single leg support phase, but not different symmetry index or COM location, compared to AHD. The differences between AHD and DIM were nonsignificant. Healthy adults did not change their gait patterns under the different walking conditions. Persons with chronic stroke walked more cautiously but not more symmetrically when looking down at the feet, but not in dimly lit environment. Ambulatory persons with stroke may need to be advised that looking down at the feet while walking could be more challenging.

Robust Gait Recognition Based on Deep CNNs With Camera and Radar Sensor Fusion

In recent years, gait recognition has emerged as an important and promising solution for human identification. Generally, gait recognition is based on a single type of sensor, such as a camera or a radar. However, data of a single modality may only capture inadequate gait features of a person, such as camera data lacking the intuitive micro-motion pattern information and radar data lacking the information about gait appearance, making gait-based human identification system vulnerable to complex covariate conditions, e.g. cross-view and cross-walking-condition. To build a robust and reliable gait-based human identification system, in this study, we propose a multi-sensor gait recognition framework with deep convolutional neural networks (CNNs) by fusing camera gait energy images (GEIs) and radar time-Doppler spectrograms. To learn the fine-grained gait appearance features, we propose a body-part spatial attention (BPSA) module to obtain more discriminative body part representations of GEIs. To learn the gait micro-motion pattern, we propose a long-short temporal relation modeling (LSTRM) module to obtain the local and global micro-motion representation of time-Doppler spectrograms. Finally, we fuse the discriminative body part representation and the micro-motion pattern at the multi-scale feature space to obtain richer and more robust gait features for human identification. We provide an extensive empirical evaluation in terms of various complex covariate conditions, namely, cross-view and cross-walking-condition. Experiments on 121 subjects with eight views and three walking conditions of camera and radar data show our proposed method is more robust and accurate.

Biomechanical analysis of internal fixation system stability for tibial plateau fractures.

Background: Complex bone plateau fractures have been treated with bilateral plate fixation, but previous research has overemphasized evaluating the effects of internal fixation design, plate position, and screw orientation on fracture fixation stability, neglecting the internal fixation system's biomechanical properties in postoperative rehabilitation exercises. This study aimed to investigate the mechanical properties of tibial plateau fractures after internal fixation, explore the biomechanical mechanism of the interaction between internal fixation and bone, and make suggestions for early postoperative rehabilitation and postoperative weight-bearing rehabilitation. Methods: By establishing the postoperative tibia model, the standing, walking and running conditions were simulated under three axial loads of 500N, 1000N, and 1500N. Accordingly, finite element analysis (FEA) was performed to analyze the model stiffness, displacement of fractured bone fragments, titanium alloy plate, screw stress distribution, and fatigue properties of the tibia and the internal fixation system under various conditions. Results: The stiffness of the model increased significantly after internal fixation. The anteromedial plate was the most stressed, followed by the posteromedial plate. The screws at the distal end of the lateral plate, the screws at the anteromedial plate platform and the screws at the distal end of the posteromedial plate are under greater stress, but at a safe stress level. The relative displacement of the two medial condylar fracture fragments varied from 0.002-0.072mm. Fatigue damage does not occur in the internal fixation system. Fatigue injuries develop in the tibia when subjected to cyclic loading, especially when running. Conclusion: The results of this study indicate that the internal fixation system tolerates some of the body's typical actions and may sustain all or part of the weight early in the postoperative period. In other words, early rehabilitative exercise is recommended, but avoid strenuous exercise such as running.

GAIT Technology for Human Recognition using CNN

Gait is a distinctive biometric characteristic that can be detected from a distance; as a result, it has several uses in social security, forensic identification, and crime prevention. Existing gait identification techniques use a gait template, which makes it difficult to keep temporal information, or a gait sequence, which maintains pointless sequential limitations and loses the ability to portray a gait. Our technique, which is based on this deep set viewpoint, is immune to frame permutations and can seamlessly combine frames from many videos that were taken in various contexts, such as diversified watching, angles, various outfits, or various situations for transporting something. According to experiments, our single-model strategy obtains an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset when used under typical walking conditions. Our model also demonstrates a great degree of robustness under numerous challenging circumstances. When carrying bags and wearing a coat while walking, it obtains accuracy on the CASIA-B of 90.8% and 70.3%, respectively, greatly surpassing the best approach currently in use. Additionally, the suggested method achieves a satisfactory level of accuracy even when there are few frames available in the test samples; for instance, it achieves 85.0% on the CASIA-B even with only 7 frames.

Omni-Domain Feature Extraction Method for Gait Recognition

As a biological feature with strong spatio-temporal correlation, the current difficulty of gait recognition lies in the interference of covariates (viewpoint, clothing, etc.) in feature extraction. In order to weaken the influence of extrinsic variable changes, we propose an interval frame sampling method to capture more information about joint dynamic changes, and an Omni-Domain Feature Extraction Network. The Omni-Domain Feature Extraction Network consists of three main modules: (1) Temporal-Sensitive Feature Extractor: injects key gait temporal information into shallow spatial features to improve spatio-temporal correlation. (2) Dynamic Motion Capture: extracts temporal features of different motion and assign weights adaptively. (3) Omni-Domain Feature Balance Module: balances fine-grained spatio-temporal features, highlight decisive spatio-temporal features. Extensive experiments were conducted on two commonly used public gait datasets, showing that our method has good performance and generalization ability. In CASIA-B, we achieved an average rank-1 accuracy of 94.2% under three walking conditions. In OU-MVLP, we achieved a rank-1 accuracy of 90.5%.

Assessing falls in the elderly population using G-STRIDE foot-mounted inertial sensor

Falls are one of the main concerns in the elderly population due to their high prevalence and associated consequences. Guidelines for the management of the elder with falls are comprised of multidimensional assessments, especially gait and balance. Daily clinical practice needs for timely, effortless, and precise tools to assess gait. This work presents the clinical validation of the G-STRIDE system, a 6-axis inertial measurement unit (IMU) with onboard processing algorithms, that allows the calculation of walking-related metrics correlated with clinical markers of fall risk. A cross-sectional case-control study was conducted with 163 participants (falls and non-falls groups). All volunteers were assessed with clinical scales and conducted a 15-min walking test at a self-selected pace while wearing the G-STRIDE. G-STRIDE is a low-cost solution to facilitate the transfer to society and clinical evaluations. It is open hardware and flexible and, thus, has the advantage of providing runtime data processing. Walking descriptors were derived from the device, and a correlation analysis was conducted between walking and clinical variables. G-STRIDE allowed measuring walking parameters in non-restricted walking conditions (e.g. hallway). Walking parameters statistically discriminate between falls and non-falls groups. We found good/excellent estimation accuracy (ICC = 0.885; p<0.000) for walking speed, showing good/excellent correlation between gait speed and several clinical variables. G-STRIDE can calculate walking-related metrics that allow for discrimination between falls and non-falls groups, which correlates with clinical indicators of fall risk. A preliminary fall-risk assessment based on the walking parameters was found to improve the Timed Up and Go test in the identification of fallers.

Gait Stability Characteristics in Able-Bodied Individuals During Self-paced Inclined Treadmill Walking: Within-Subject Repeated-Measures Study.

Inclined walking is a challenging task that requires active neuromuscular control to maintain stability. However, the adaptive strategies that preserve stability during inclined walking are not well understood. Investigating the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation patterns of key lower limb muscles can provide insights into these strategies. The aim of this study was to investigate the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation of key lower limb muscles. Twenty-eight able-bodied individuals (mean age 25.02, SD 2.06 years) walked on an augmented instrumented treadmill for 3 minutes at 3 inclination angles (-8°, 0°, and 8°) at their preferred walking speed. Changes in gait characteristics (ie, stability, walking speed, spatial-temporal, kinematic, and muscle forces) across inclination angles were assessed using a repeated measures ANOVA and the Friedman test. The study revealed that inclined treadmill walking has a significant impact on gait characteristics (P<.001). Changes were observed in spatial-temporal parameters, joint angles, and muscle activations depending on the treadmill inclination. Specifically, stability and walking speed decreased significantly during uphill walking, indicating that it was the most challenging walking condition. Uphill walking also led to a decrease in spatial parameters by at least 13.53% and a 5.26% to 10.96% increase in temporal parameters. Furthermore, joint kinematics and peak activation of several muscles, including the hamstrings (biceps femoris, long head=109.5%, biceps femoris, short head=53.3%, semimembranosus=98.9%, semitendinosus=90.9%), gastrocnemius (medial gastrocnemius=40.6%, lateral gastrocnemius=35.3%), and vastii muscles (vastus intermedius=12.8%, vastus lateralis=16.7%) increased significantly during uphill walking. In contrast, downhill walking resulted in bilateral reductions in spatial-temporal gait parameters, with knee flexion increasing and hip flexion and ankle dorsiflexion decreasing. The peak activation of antagonist muscles, such as the quadriceps, tibialis anterior, and tibialis posterior, significantly increased during downhill walking (rectus femoris=97.7%, vastus lateralis =70.6%, vastus intermedius=68.7%, tibialis anterior=72%, tibialis posterior=107.1%). Our findings demonstrate that able-bodied individuals adopt specific walking patterns during inclined treadmill walking to maintain a comfortable and safe walking performance. The results suggest that inclined treadmill walking has the potential to serve as a functional assessment and rehabilitation tool for gait stability by targeting muscle training. Future research should investigate the effects of inclined treadmill walking on individuals with gait impairments and the potential benefits of targeted muscle training. A better understanding of the adaptive strategies used during inclined walking may lead to the development of more effective rehabilitation interventions for individuals with lower limb injuries.

A multi-view human gait recognition using hybrid whale and gray wolf optimization algorithm with a random forest classifier

Gait recognition has become one of the furthest promising behavioral biometric techniques for identifying individuals. Gait recognition models are capable of identifying humans at a distance based on their walking manner without their permission or interference. However, it is usually noted that the performance of a gait recognition approach will drop drastically in the presence of covariates such as carrying conditions, clothing conditions, and variations in the view angle. Therefore, it is necessary to develop a robust gait recognition system in order to identify the most significant gait features. In this paper, we introduced a recently developed hybrid whale and gray wolf optimization algorithm (WGWOA) for determining the optimal subset of gait features by combining the advantages of whale optimization and gray wolf optimization techniques. Moreover, we employed principal component analysis (PCA) to extract the essential gait features from the gradient gait energy image (GGEI) and random forest (RF) approach to classify the optimal gait features. The proposed method has been assessed on the publicly available largest multi-view CASIA-B and OU-MVLP benchmark datasets. Experimental results indicate that the proposed model achieved an accuracy of 99.25%, 98.39%, and 97.97% under normal, carrying a bag and wearing coat walking conditions, respectively on the CASIA-B dataset. Furthermore, the proposed model achieved an accuracy of 97.63% under normal conditions on the OU-MVLP dataset. The comparative results also confirm that the proposed algorithm is superior to the contemporary approaches.

Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach.

Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that are measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they are limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, Kinetics-FM-DLR-Ensemble-Net for single limb prediction of hip, knee, and ankle joint moments and 3 dimensional Ground Reaction Forces (GRFs) using three IMU sensors on the thigh, shank, and foot under several representatives walking conditions in daily living, such as treadmill, level-ground, stair, and ramp. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and our model outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.

Directed connectivity in Parkinson's disease patients during over-ground and treadmill walking

Treadmill walking is considered a useful therapeutic tool for improving gait in Parkinson's disease (PD) patients. The study investigated the role of top-down, frontal-parietal versus bottom-up parietal-frontal networks, during over-ground and treadmill walking in PD and control subjects, using functional connectivity. To this end, EEG was recorded simultaneously, during a ten-minute period of continuous walking either over-ground or on a treadmill, in thirteen PD patients and thirteen age-matched controls. We evaluated EEG directed connectivity, using phase transfer entropy in three frequency bands: theta, alpha and beta. PD patients showed increased top-down connectivity during over-ground compared with treadmill walking, in the beta frequency range. Control subjects showed no significant differences in connectivity between the two walking conditions. Our results suggest that in PD patients, OG walking was associated with increased allocation of attentional resources, compared with that on the TL. These functional connectivity modulations may shed further light on the mechanisms underlying treadmill versus overground walking in PD.

Modified stepping behaviour during outdoor winter walking increases resistance to forward losses of stability

Healthy humans are proficient at maintaining stability when faced with diverse walking conditions, however, the control strategies that lead to this proficiency are unclear. Previous laboratory-based research has predominantly concluded that corrective stepping is the main strategy, but whether this finding holds when facing everyday obstacles outside of the laboratory is uncertain. We investigated changes in gait stability behaviour when walking outdoors in the summer and winter, hypothesizing that as ground conditions worsened in the winter, the stepping strategy would be hindered. Stability would then be maintained through compensatory strategies such as with ankle torques and trunk rotation. Data was collected in the summer and winter using inertial measurement units to collect kinematics and instrumented insoles to collect vertical ground reaction forces. Using the goodness of fit for a multivariate regression between the centre of mass state and foot placement we found that, counter to our hypothesis, stepping was not hindered by winter conditions. Instead, the stepping strategy was modified to increase the anterior-posterior margin of stability, increasing the resistance to a forward loss of stability. With stepping being unhindered, we did not observe any additional compensation from the ankle or trunk strategies.

How do crosswalk delays affect pedestrian access in zoning areas? Walking access reduction by signalized crosswalks in Seoul, South Korea

Long waiting times at signalized crosswalks delay pedestrians and reduce the effective walksheds around urban facilities. In this study, we identify to what extent crosswalk delays at signalized crosswalks reduce pedestrians’ effective walking access and how the impact of crosswalk delays is differentiated by zoning types in Seoul, Korea. We demonstrate a pedestrian network analysis method considering crosswalk delay times, using a detailed pedestrian path geospatial layer and dataset of signal timings. Our results show significant reductions in walkshed size and served users. Employing multiple regression modeling, we identified that these reductions were not uniform and depended on zoning patterns in urban areas. In addition, our findings suggest that commercial zoning areas and recent residential developments, comprised of high-rise towers in mega-blocks, create greater delays to pedestrians and constrict effective walksheds. We discuss the need for further studies and the potential contribution of zoning system and its land use managements on improving walking conditions such as underground space developments and mixed residential density patterns.

Vibration Test and Analysis of Crawler Pepper Harvester under Multiple Working Conditions

Nowadays, many scholars at home and abroad have studied the vibration of agricultural machinery, especially harvesting machinery. However, this research has lacked the analysis of vibration characteristics of harvesters under the condition of multi-vibration excitation in field work. Therefore, by taking the chassis frame and main vibration sources of a 4JZ-1700 crawler pepper harvester as the research object, this paper aims to investigate the vibration characteristics of the pepper harvester under different working conditions, and the impact of the excitation of various working parts on the chassis frame. Firstly, a modal simulation was carried out with the modal module of ANSYS Workbench to study the natural frequency of the chassis frame. The results demonstrated that the natural frequency of the chassis frame was within 23–76 Hz. A DH5902 dynamic signal acquisition instrument was used to collect vibration signals from seven measuring points under different working conditions of the whole machine, and the collected time domain signals were extracted by Fourier transform. According to the time domain signal, the amplitude at the engine support was the largest under the static no-load condition, and the transmission of engine vibration was attenuated to a certain extent, which imposes a significant effect on the vibration isolation and vibration reduction of the harvester frame. Under the field walking condition, the amplitudes of the left front of the chassis frame and the driving shaft of the cleaning separation device were abnormal, which was mainly attributed to the unequal road surface and the high center of gravity of the cleaning separation device. Through frequency domain analysis, it can be found that the main vibration frequency of most measuring points of the harvester was close to the vibration frequency of the engine under the static no-load condition, and the excitation frequency of most measuring points approximated to the working frequency of the picking drum and the cleaning separation device under the field walking condition. In addition, there were plenty of phenomena in which the main frequency of vibration was detected in the high frequency region above 200 Hz, with messy frequency values. This is due to the poor lubrication of the bearing part of the harvester, causing intense friction between the rotating shaft and the bearing, which also drives the high frequency vibration of the chassis frame. In general, this study can provide a method reference for vibration analysis of agricultural machinery and propose effective measures to reduce vibration based on the conclusions.

An urban plan evaluation for park accessibility: a case in Izmir (Türkiye)

Plan evaluations about park accessibility are rare at the neighbourhood scale. Moreover, urban plans traditionally identify park accessibility with predetermined measurements that may ignore limited walking conditions of children, the elderly, women with children, and low-income groups. Alternatively, this paper considers equitable (rather than equal) park accessibility as an important goal concerning environmental justice. To guide a path to achieving this goal, it investigates how to assess and revise urban plans with parks within walking distance to social groups in the case of a plan (1/1000 scale) in Izmir (Türkiye). Deployment of the location-allocation analysis (a multi-criteria assessment methodology in Geographic Information Systems, GIS) allows this research to consider physical/geographical barriers to walkability in actual neighbourhood settings and reconfigure such barriers as contextual variables, including limited walking distances of disadvantaged groups. Ultimately, this study also contributes to how to handle spatial and demographic data deficiencies in Türkiye when measuring equitable accessibility of public facilities by walking. Results identify an uneven distribution of park accessibility even within the neighbourhood on the plan and the potential for improving park accessibility by designing some non-park public lands with park features.

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson's Disease.

Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months' falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.

Electroencephalographic-based wearable instrumentation to monitor the executive functions during gait: a feasibility study

A feasibility study on electroencephalographic monitoring of executive functions during dual (motor and cognitive) task execution is presented. Electroencephalographic (EEG) signals are acquired by means of a wearable device with few channels and dry electrodes. The light weight and wireless device allow for walking in a natural way. The most significant EEG features are investigated to classify different levels of activation for two fundamental Executive Functions (EF) both in sitting and walking conditions. Power spectral density in the gamma band resulted in the most relevant feature in discriminating low and high levels of Inhibition. Power spectral density in the beta and gamma bands resulted the most discriminating the level of activation of Working Memory. The study poses the basis for (i) monitoring the activation levels of EF during Gait allowing loss prevention in the elderly and (ii) specific cognitive rehabilitation aimed at the most relevant executive functions during walking.

Efficacy of Hinged Ankle–Foot Orthosis on Gait Improvement of Children with Spastic Diplegic Cerebral Palsy: A Literature Review

Purpose: The purpose is to study the effect of hinged ankle–foot orthosis (HAFO) on gait kinematics and temporospatial parameters of children with diplegic cerebral palsy. Methods: A literature search has been conducted from 2005 to 2022 to find the studies measuring the effect of HAFO on children with diplegic spastic cerebral palsy (CP) during gait through various bibliographic databases: PubMed, Scopus, EMBASE, O and P Virtual Library and Google Scholar. Results: We found 96 pieces of potentially relevant literature from which only six studies met the inclusion criteria were included. One hundred and fifty-three participants, with a mean age of 8.6 years (4–17 years), were investigated, of which 128 were spastic diplegic CP. Among spastic diplegic, 45 individuals were identified as walking with true equinus gait and 34 with jump gait. Gait parameters of participants were evaluated with ankle–foot orthoses (AFO) (hinged, solid and supramalleolar orthosis), and barefoot walking conditions showed improvement in some kinematic and spatial-temporal parameters, making their gait closer to that of typically developing children. Conclusion: HAFO was found very satisfactory, as it improves dynamic stability and ankle range of motion resulting in a more natural gait in children with spastic diplegic CP.