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Journal articles on the topic 'Gait'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Chibirov, G. M., T. I. Dolganova, D. V. Dolganov, S. Ducic, and D. A. Popkov. "Changes in the kinematic parameters of gait in children with cerebral palsy after multilevel interventions with the initial patterns of true equinus gait and jump gait." Genij Ortopedii 25, no. 4 (December 2019): 501–9. http://dx.doi.org/10.18019/1028-4427-2019-25-4-501-509.

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2

Ameer, Mariam A., and Mohamed E. Khallaf. "EFFECT OF TREADMILL GAIT TRAINING ON DIPLEGIC CHILDREN SWING PHASE GAIT PARAMETERS." International Journal of Physiotherapy and Research 5, no. 1 (February 11, 2017): 1829–35. http://dx.doi.org/10.16965/ijpr.2016.200.

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3

Umair Bin Altaf, M., Taras Butko, and Biing-Hwang Juang. "Acoustic Gaits: Gait Analysis With Footstep Sounds." IEEE Transactions on Biomedical Engineering 62, no. 8 (August 2015): 2001–11. http://dx.doi.org/10.1109/tbme.2015.2410142.

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4

Fedotov, V. V. "Gait – Pathology or Physiology." Ukraïnsʹkij žurnal medicini, bìologìï ta sportu 3, no. 4 (May 18, 2018): 124–27. http://dx.doi.org/10.26693/jmbs03.04.124.

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5

Gao, Shuo, Jing Yun, Yumeng Zhao, and Limin Liu. "Gait‐D: Skeleton‐based gait feature decomposition for gait recognition." IET Computer Vision 16, no. 2 (November 2021): 111–25. http://dx.doi.org/10.1049/cvi2.12070.

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6

Kim, Youngho, and Jinbok Yi. "Gait Analysis in Normal and Hemiplegic Patients Using Accelerometers(Gait & Motion Analysis)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2004.1 (2004): 113–14. http://dx.doi.org/10.1299/jsmeapbio.2004.1.113.

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7

Law, YC, AFT Mak, WN Wong, and M. Zhang. "THE VARIATION OF DYNAMIC FOOT PRESSURE WITH GAIT PARAMETER.(Gait & Motion Analysis)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2004.1 (2004): 115–16. http://dx.doi.org/10.1299/jsmeapbio.2004.1.115.

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8

Hahn, Marilyn R. "Gait." Neurology Report 16, no. 1 (1992): 23–25. http://dx.doi.org/10.1097/01253086-199216010-00011.

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9

Dubin, Andrew. "Gait." Medical Clinics of North America 98, no. 2 (March 2014): 205–11. http://dx.doi.org/10.1016/j.mcna.2013.10.002.

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10

Sutherland, David H. "Dimensionless gait measurements and gait maturity." Gait & Posture 4, no. 3 (May 1996): 209–11. http://dx.doi.org/10.1016/0966-6362(95)01056-4.

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11

Kim, Jaewook, Yekwang Kim, and Seung-Jong Kim. "Biomechanical Task-Based Gait Analysis Suggests ReWalk Gait Resembles Crutch Gait." Applied Sciences 12, no. 24 (December 8, 2022): 12574. http://dx.doi.org/10.3390/app122412574.

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Current gait rehabilitation strategies rely heavily on motor learning principles, which involve facilitating active patient participation, high-doses of biomechanical task-related motor activities and accurate feedback. Furthermore, appropriate muscle groups need to be recruited for the joint movements that constitute the biomechanical task-related activities in order to effectively promote motor learning. Recently, exoskeleton-type robots utilizing crutches have been incorporated into overground gait rehabilitation programs. However, it is unclear which gait-related tasks are being trained because the joint movements and muscle recruitment patterns deviate from those of natural gait. This raises concerns because repetitive training with these devices may not lead to desirable rehabilitative gains. In this study, we compare the lower limb joint angles and electromyography patterns of healthy subjects walking with and without ReWalk in accordance with the three major biomechanical tasks required by bipedal gait: weight acceptance (WA), single-limb support, and limb advancement. Furthermore, we investigate whether the physical constraints of ReWalk, most noticeably the use of crutches and fixed ankle joints, were responsible for the specific changes by conducting additional walking sessions with either crutches or ankle foot orthoses. The results from the six healthy male volunteers suggest that the gait patterns observed with ReWalk deviate significantly from those of natural gait, particularly during the WA, and closely resemble those of crutch gait.

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12

Su, Rung-Hung, Yeh-Liang Hsu, Lung Chan, Hanjun Lin, and Che-Chang Yang. "ASSESSING ABNORMAL GAITS OF PARKINSON'S DISEASE PATIENTS USING A WEARABLE MOTION DETECTOR." Biomedical Engineering: Applications, Basis and Communications 26, no. 02 (March 12, 2014): 1450031. http://dx.doi.org/10.4015/s1016237214500318.

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Accelerometers have been widely used in wearable systems for gait analysis. Several gait cycle parameters are provided to quantify the level of gait regularity and symmetry. This study attempts to assess abnormal gaits of Parkinson disease (PD) patients based on the gait cycle parameters derived in real-time from an accelerometry-based wearable motion detector (WMD). The results of an experiment with 25 healthy young adults showed that there were significant differences between gait cycle parameters of normal gaits and abnormal gaits derived from the WMD. Five PD patients diagnosed as Hoehn and Yahr stage I to II were recruited. It is difficult to collect data of abnormal gaits of the PD patients; therefore, ranges of the gait cycle parameters of abnormal gaits of PD patients were estimated statistically based on the "lower confidence limit" of the gait cycle parameters of their normal gaits. These results may lead to the future development of wearable sensors enabling real-time recognition of abnormal gaits of PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications.

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13

Shen, Zhe, Yudong Ma, and Takeshi Tsuchiya. "Four-Dimensional Gait Surfaces for a Tilt-Rotor—Two Color Map Theorem." Drones 6, no. 5 (April 23, 2022): 103. http://dx.doi.org/10.3390/drones6050103.

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This article presents the four-dimensional surfaces that guide the gait plan for a tilt-rotor. The previous gaits analyzed in the tilt-rotor research are inspired by animals; no theoretical base backs the robustness of these gaits. This research deduces the gaits by diminishing the adverse effect of the attitude of the tilt-rotor for the first time. Four-dimensional gait surfaces are subsequently found on which the gaits are expected to be robust to the attitude. These surfaces provide the region where the gait is suggested to be planned. However, a discontinuous region may hinder the gait plan process while utilizing the proposed gait surfaces. The ‘Two Color Map Theorem’ is then established to guarantee the continuity of each gait designed. The robustness of the typical gaits on the gait surface, obeying the Two Color Map Theorem, is demonstrated by comparing the singular curves in attitude with the gaits not on the gait surface. The result shows that the gaits on the gait surface receive wider regions of the acceptable attitudes.

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14

Koo, Dohoon, Won-Kyung Song, Sungpil Kim, Hyeongsic Kim, and Seondeok Eun. "PS5-17 COMPARISON OF GAIT PATTERN WITH THE 4 LINKED GAIT TRAINER (GTR) AND TREADMILL GAIT(PS5: Poster Short Presentation V,Poster Session)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2015.8 (2015): 314. http://dx.doi.org/10.1299/jsmeapbio.2015.8.314.

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15

Henderson, Adrienne D., A. Wayne Johnson, Sarah T. Ridge, Jonathan S. Egbert, Kevin P. Curtis, Levi J. Berry, and Dustin A. Bruening. "Diabetic Gait Is Not Just Slow Gait: Gait Compensations in Diabetic Neuropathy." Journal of Diabetes Research 2019 (November 11, 2019): 1–9. http://dx.doi.org/10.1155/2019/4512501.

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Background. Neuropathic complications from diabetes mellitus affect multiple nerve types and may manifest in gait. However, gait compensations are still poorly understood, as narrow analyses and lack of speed controls have contributed to conflicting or equivocal results. Purpose. To evaluate gait mechanics and energetics in diabetic peripheral polyneuropathy. Methods. Instrumented gait analysis was performed on 14 participants with diabetic peripheral polyneuropathy and 14 matched controls, walking at 1.0 m/s. A full-body model with a multisegment foot was used to calculate inverse dynamics and analyze sagittal plane metrics and time series waveforms across stance phase. Results. Alterations included increased hip and knee flexion in early stance followed by a prolonged hip extension moment in midstance. Late stance ankle dorsiflexion and power absorption were increased, and final push-off was delayed and truncated. Conclusion. A neuropathic diabetic gait shares important similarities to a mild crouch gait with weakness/dysfunction in the foot and ankle. This study highlights two main compensation mechanisms that have been overlooked in previous literature. First, increased triceps surae stretch in terminal stance may be used to increase proprioception and/or energy storage, while a prolonged hip extension moment in midstance compensates for a limited push-off. These result in an overall workload shift from distal to proximal joints. Clinical assessment, monitoring, and treatment of neuropathy may benefit by focusing on these specific functional alterations.

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16

Tadano, Shigeru, Ryo Takeda, Keita Sasaki, Tadashi Fujisawa, and Harukazu Tohyama. "Gait characterization for osteoarthritis patients using wearable gait sensors (H-Gait systems)." Journal of Biomechanics 49, no. 5 (March 2016): 684–90. http://dx.doi.org/10.1016/j.jbiomech.2016.01.017.

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17

Van Nuffel, A., M. Sprenger, FAM Tuyttens, and W. Maertens. "Cow gait scores and kinematic gait data: can people see gait irregularities?" Animal Welfare 18, no. 4 (November 2009): 433–39. http://dx.doi.org/10.1017/s0962728600000841.

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AbstractIncreasing lameness problems associated with intensified dairy cattle production has lead to the development of several techniques to automatically detect these problems. Comparisons of these new measuring techniques of cow locomotion with the conventional subjective observer scoring are scarce. In order to better understand human observers’ gait scoring, cows walking on a pressure-sensitive mat were evaluated for kinematic gait variables and a visual assessment of gait was also made via video recording. Forty of these videos were used for subjective gait scoring on a 3-point scale, and the observers were also asked to report any observed abnormalities (lameness indicators) that had influenced their scoring. Relationships between reported lameness indicators and subjective gait scores, between subjective gait scores and measured kinematic variables of cow locomotion and between reported lameness indicators and measured kinematic variables of cow locomotion were investigated. In general, observers based their gait score on reported indicators such as ‘tenderness’, ‘arched back’, ‘irregular gait’ and ‘increased abduction’. All of these four reported lameness indicators were correlated with measured kinematic ‘variables of asymmetry’, ‘stance time’ or both, suggesting that human observers are capable of detecting changes within these lameness indicators as measured by the pressure-sensitive mat. ‘Increased abduction’ appeared harder to detect and was reported more frequently by observers already experienced with gait scoring. Also, the measured kinematic variables of ‘stance time’ and ‘measures of asymmetry between left and right limbs’ as measured by the pressure-sensitive mat, show potential in predicting the gait score given. These reported lameness indicators and measured kinematic variables —mutually correlated and both related to the gait scores — were considered promising for subjective gait scoring in general.

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18

KITADA, Tatuo, Koji ITO, Yasuro KUROSE, and Yoshimasa UMENO. "A KNOWLEDGE-BASED GAIT ANALYSIS SUPPORTING SYSTEM (GAITS)." Biomechanisms 9 (1988): 207–16. http://dx.doi.org/10.3951/biomechanisms.9.207.

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19

Gonzalez-Islas, Juan-Carlos, Omar-Arturo Dominguez-Ramirez, Omar Lopez-Ortega, Jonatan Peña-Ramirez, Jesus-Patricio Ordaz-Oliver, and Francisco Marroquin-Gutierrez. "Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics." Applied Sciences 12, no. 20 (October 11, 2022): 10197. http://dx.doi.org/10.3390/app122010197.

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Crouch gait is one of the most common gait abnormalities; it is usually caused by cerebral palsy. There are few works related to the modeling of crouch gait kinematics, crouch gait analysis, and visualization in both the workspace and joint space. In this work, we present a quaternion-based method to solve the forward kinematics of the position of the lower limbs during walking. For this purpose, we propose a modified eight-DoF human skeletal model. Using this model, we present a geometric method to calculate the gait inverse kinematics. Both methods are applied for gait analysis over normal, mild, and severe crouch gaits, respectively. A metric-based comparison of workspace and joint space for the three gaits for a gait cycle is conducted. In addition, gait visualization is performed using Autodesk Maya for the three anatomical planes. The obtained results allow us to determine the capabilities of the proposed methods to assess the performance of crouch gaits, using a normal pattern as a reference. Both forward and inverse kinematic methods could ultimately be applied in rehabilitation settings for the diagnosis and treatment of diseases derived from crouch gaits or other types of gait abnormalities.

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20

Mogan, Jashila Nair, Chin Poo Lee, Kian Ming Lim, and Kalaiarasi Sonai Muthu. "Gait-ViT: Gait Recognition with Vision Transformer." Sensors 22, no. 19 (September 28, 2022): 7362. http://dx.doi.org/10.3390/s22197362.

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Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate. The existing works mostly leverage Convolutional Neural Networks for gait recognition. The Convolutional Neural Networks perform well in image recognition tasks; however, they lack the attention mechanism to emphasize more on the significant regions of the image. The attention mechanism encodes information in the image patches, which facilitates the model to learn the substantial features in the specific regions. In light of this, this work employs the Vision Transformer (ViT) with an attention mechanism for gait recognition, referred to as Gait-ViT. In the proposed Gait-ViT, the gait energy image is first obtained by averaging the series of images over the gait cycle. The images are then split into patches and transformed into sequences by flattening and patch embedding. Position embedding, along with patch embedding, are applied on the sequence of patches to restore the positional information of the patches. Subsequently, the sequence of vectors is fed to the Transformer encoder to produce the final gait representation. As for the classification, the first element of the sequence is sent to the multi-layer perceptron to predict the class label. The proposed method obtained 99.93% on CASIA-B, 100% on OU-ISIR D and 99.51% on OU-LP, which exhibit the ability of the Vision Transformer model to outperform the state-of-the-art methods.

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21

Yao, Lingxiang, Worapan Kusakunniran, Qiang Wu, and Jian Zhang. "Gait recognition using a few gait frames." PeerJ Computer Science 7 (March 1, 2021): e382. http://dx.doi.org/10.7717/peerj-cs.382.

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Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.

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22

Yanagizono, Taiichiro, Seiji Higuchi, Risa Kondo, Yoko Katsushima, Ichiro Kadouchi, and Akihiro Kawano. "Gait Analysis Using the Gillete Gait Index." Orthopedics & Traumatology 59, no. 2 (2010): 293–95. http://dx.doi.org/10.5035/nishiseisai.59.293.

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23

Zongyi Liu and S. Sarkar. "Improved gait recognition by gait dynamics normalization." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 6 (June 2006): 863–76. http://dx.doi.org/10.1109/tpami.2006.122.

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24

Ramakrishnan, Tyagi, Seok Hun Kim, and Kyle B. Reed. "Human Gait Analysis Metric for Gait Retraining." Applied Bionics and Biomechanics 2019 (November 11, 2019): 1–8. http://dx.doi.org/10.1155/2019/1286864.

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The combined gait asymmetry metric (CGAM) provides a method to synthesize human gait motion. The metric is weighted to balance each parameter’s effect by normalizing the data so all parameters are more equally weighted. It is designed to combine spatial, temporal, kinematic, and kinetic gait parameter asymmetries. It can also combine subsets of the different gait parameters to provide a more thorough analysis. The single number quantifying gait could assist robotic rehabilitation methods to optimize the resulting gait patterns. CGAM will help define quantitative thresholds for achievable balanced overall gait asymmetry. The study presented here compares the combined gait parameters with clinical measures such as timed up and go (TUG), six-minute walk test (6MWT), and gait velocity. The comparisons are made on gait data collected on individuals with stroke before and after twelve sessions of rehabilitation. Step length, step time, and swing time showed a strong correlation to CGAM, but the double limb support asymmetry has nearly no correlation with CGAM and ground reaction force asymmetry has a weak correlation. The CGAM scores were moderately correlated with TUG and strongly correlated to 6MWT and gait velocity.

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25

Souza, R. "ABCs of gait and running gait analysis." Osteoarthritis and Cartilage 27 (April 2019): S18. http://dx.doi.org/10.1016/j.joca.2019.02.018.

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26

Bai, Long, Hao Hu, Xiaohong Chen, Yuanxi Sun, Chaoyang Ma, and Yuanhong Zhong. "CPG-Based Gait Generation of the Curved-Leg Hexapod Robot with Smooth Gait Transition." Sensors 19, no. 17 (August 26, 2019): 3705. http://dx.doi.org/10.3390/s19173705.

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This paper presents a novel CPG-based gait generation of the curved-leg hexapod robot that can enable smooth gait transitions between multi-mode gaits. First, the locomotion of the curved leg and instability during the gait transitions are analyzed. Then, a modified Hopf oscillator is applied in the CPG control, which can realize multiple gaits by adjusting a simple parameter. In addition, a smooth gait switching method is also proposed via smooth gait transition functions and gait planning. Tripod gait, quadruped gait, and wave gait are planned for the hexapod robot to achieve quick and stable gait transitions smoothly and continuously. MATLAB and ADAMS simulations and corresponding practical experiments are conducted. The results show that the proposed method can achieve smooth and continuous mutual gait transitions, which proves the effectiveness of the proposed CPG-based hexapod robot control.

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27

Park, Sunwoo, Soonjae Ahn, Isu Shin, and Youngho Kim. "PS5-18 Influence of cadence on basic gait parameters in gait initiation, constant-speed walking and gait termination(PS5: Poster Short Presentation V,Poster Session)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2015.8 (2015): 315. http://dx.doi.org/10.1299/jsmeapbio.2015.8.315.

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28

Adachi, Hironori, Noriho Koyachi, Tatsuya Nakamura, and Eiji Nakano. "Development of Quadruped Walking Robots and Their Gait Study." Journal of Robotics and Mechatronics 5, no. 6 (December 20, 1993): 548–60. http://dx.doi.org/10.20965/jrm.1993.p0548.

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This paper describes the quadruped walking robots developed at the Mechanical Engineering Laboratory and their gait analysis. Legged locomotion has the potential to adapt itself to changes in walking conditions, but also has problems such as complexity. To overcome these problems, a new link mechanism called ASTBALLEM is used for the legs of the robots, and highly rigid and easily controllable legs are constructed by using this mechanism. To make a legged robot walk stably, it is necessary to provide a suitable gait. In this paper, two kinds of gaits are considered. One is the periodic gait, and it is systematically classified by two parameters. By using this classification method, suitable gaits for static walking and dynamic walking are selected. The other is the adaptive gait. In order to realize their potentials, walking robots must sense the walking conditions and change their gaits. Two adaptive gait schemes are proposed in this paper. One is a gait which adapts to the position of the center of gravity, and the other is a gait for incline terrain. Both gaits use force sensor data for detecting changes in the walking conditions. All the gaits discussed in this paper are experimentally evaluated.

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29

Millichap, J. Gordon. "Hysterical Gait." Pediatric Neurology Briefs 2, no. 5 (May 1, 1988): 39. http://dx.doi.org/10.15844/pedneurbriefs-2-5-13.

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30

Gibson, Margaret E., and Natalie Stork. "Gait Disorders." Primary Care: Clinics in Office Practice 48, no. 3 (September 2021): 395–415. http://dx.doi.org/10.1016/j.pop.2021.04.004.

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31

Sudarsky, Lewis. "Gait Failure." Emergency Medicine Clinics of North America 5, no. 4 (November 1987): 677–85. http://dx.doi.org/10.1016/s0733-8627(20)30123-1.

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32

Van Wassen, Cathleen. "Gait Trick." Brain & Life 18, no. 1 (February 2022): 4. http://dx.doi.org/10.1097/01.nnn.0000820788.60403.6c.

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33

Kim, Byung Gon, and Jae Kyu Roh. "Gait Disturbance." Journal of the Korean Medical Association 40, no. 4 (1997): 501. http://dx.doi.org/10.5124/jkma.1997.40.4.501.

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34

Xu, Weitao, Chitra Javali, Girish Revadigar, Chengwen Luo, Neil Bergmann, and Wen Hu. "Gait-Key." ACM Transactions on Sensor Networks 13, no. 1 (February 9, 2017): 1–27. http://dx.doi.org/10.1145/3023954.

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35

DeLuca, Peter A. "Gait analysis." Current Opinion in Orthopaedics 4, no. 6 (December 1993): 101–4. http://dx.doi.org/10.1097/00001433-199312000-00018.

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36

Nandhagopal, R. "Unsteady gait." Postgraduate Medical Journal 82, no. 967 (May 1, 2006): e7-e7. http://dx.doi.org/10.1136/pgmj.2005.040774.

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37

Paul, J. P. "Gait analysis." Annals of the Rheumatic Diseases 48, no. 3 (March 1, 1989): 179–81. http://dx.doi.org/10.1136/ard.48.3.179.

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38

Fisher, C. M. "Gait disorders." Neurology 44, no. 4 (April 1, 1994): 779. http://dx.doi.org/10.1212/wnl.44.4.779-a.

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39

Nutt, J. G., D. C. Marsden, and P. D. Thompson. "Gait disorders." Neurology 44, no. 4 (April 1, 1994): 780. http://dx.doi.org/10.1212/wnl.44.4.780.

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40

Beard, Jonathan. "Odd Gait." Scientific American Mind 17, no. 1 (February 2006): 8. http://dx.doi.org/10.1038/scientificamericanmind0206-8b.

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41

Fiol-Matta, Licia. "Chencha's gait." Women & Performance: a journal of feminist theory 18, no. 3 (November 2008): 287–301. http://dx.doi.org/10.1080/07407700802496041.

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42

Li, Li. "Gait Stability." Medicine & Science in Sports & Exercise 41 (May 2009): 23. http://dx.doi.org/10.1249/01.mss.0000352812.38243.a0.

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43

Down, J. "Kangaroo gait." Veterinary Record 119, no. 1 (July 5, 1986): 23. http://dx.doi.org/10.1136/vr.119.1.23-a.

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44

Down, J. "Kangaroo gait." Veterinary Record 119, no. 10 (September 6, 1986): 255. http://dx.doi.org/10.1136/vr.119.10.255-b.

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45

Wells, G., D. O'Toole, and S. Duffell. "Kangaroo gait." Veterinary Record 119, no. 5 (August 2, 1986): 118. http://dx.doi.org/10.1136/vr.119.5.118-c.

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46

Seidel, Geoffrey K. "Gait Analysis." American Journal of Physical Medicine & Rehabilitation 72, no. 6 (December 1993): 408. http://dx.doi.org/10.1097/00002060-199312000-00016.

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47

Roth, Elliot J., Charles Merbitz, Kenneth Mroczek, Sheila A. Dugan, and W. Warren Suh. "HEMIPLEGIC GAIT." American Journal of Physical Medicine & Rehabilitation 76, no. 2 (March 1997): 128–33. http://dx.doi.org/10.1097/00002060-199703000-00008.

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48

Perry, Jacquelin, Slac k, and Jon R. Davids. "Gait Analysis." Journal of Pediatric Orthopaedics 12, no. 6 (November 1992): 815. http://dx.doi.org/10.1097/01241398-199211000-00023.

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49

Dumurgier, Julien, and Alexis Elbaz. "Gait Decline." Hypertension 66, no. 2 (August 2015): 263–64. http://dx.doi.org/10.1161/hypertensionaha.115.05437.

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50

Phillips, K. "GAIT SHIFTING." Journal of Experimental Biology 207, no. 24 (November 15, 2004): i. http://dx.doi.org/10.1242/jeb.01351.

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