Статті в журналах: "Gait parameter"

1

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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2

Lindsey, Bryndan, Oladipo Eddo, Matthew Prebble, Shane V. Caswell, Ana M. Azevedo, and Nelson Cortes. "Single-Parameter Gait Modifications Cause Involuntary Secondary Gait Changes." Medicine & Science in Sports & Exercise 51, Supplement (June 2019): 704. http://dx.doi.org/10.1249/01.mss.0000562594.73889.9d.

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3

Gong, Zewu, Yunwei Zhang, Dongfeng Lu, and Tiannan Wu. "Vision-Based Quadruped Pose Estimation and Gait Parameter Extraction Method." Electronics 11, no. 22 (November 11, 2022): 3702. http://dx.doi.org/10.3390/electronics11223702.

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In the study of animal behavior, the prevention of sickness, and the gait planning of legged robots, pose estimation, and gait parameter extraction of quadrupeds are of tremendous importance. However, there are several varieties of quadrupeds, and distinct species frequently have radically diverse body types, limb configurations, and gaits. Currently, it is challenging to forecast animal pose estimation with any degree of accuracy. This research developed a quadruped animal pose estimation and gait parameter extraction method to address this problem. A computational framework including three components of target screening, animal pose estimation model, and animal gaits parameter extraction, which can totally and efficiently solve the problem of quadruped animal pose estimation and gait parameter extraction, makes up its core. On the basis of the HRNet network, an improved quadruped animal keypoint extraction network, RFB-HRNet, was proposed to enhance the extraction effect of quadruped pose estimation. The basic concept was to use a DyConv (dynamic convolution) module and an RFB (receptive field block) module to propose a special receptive field module DyC-RFB to optimize the feature extraction capability of the HRNet network at stage 1 and to enhance the feature extraction capability of the entire network model. The public dataset AP10K was then used to validate the model’s performance, and it was discovered that the proposed method was superior to alternative methods. Second, a two-stage cascade network was created by adding an object detection network to the front end of the pose estimation network to filter the animal object in input images, which enhanced the pose estimation effect of small targets and multitargets. The acquired keypoints data of animals were then utilized to extract the gait parameters of the experimental objects. Experiment findings showed that the gait parameter extraction model proposed in this research could effectively extract the gait frequency, gait sequence, gait duty cycle, and gait trajectory parameters of quadruped animals, and obtain real-time and accurate gait trajectory.

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4

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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5

Mostafa, Kazi, Innchyn Her, and Yi-Hsien Wu. "The Offset Model of a Hexapod Robot and the Effect of the Offset Parameter." International Journal of Manufacturing, Materials, and Mechanical Engineering 2, no. 3 (July 2012): 52–59. http://dx.doi.org/10.4018/ijmmme.2012070104.

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Generally, it is claimed that hexapod walking robots are superior to others. However, in some conditions hexapod suffers from stability problems. To solve the problem of stability, this paper proposes a new gait model of hexapod robot named offset model and also investigates the effects of morphological factor of hexapod robots on their locomotion. A comparison between the offset model and general model of hexapod robot is also included. The stability margin and error margin are used to indicate the stability of a hexapod robot, as it walks with different gaits in arbitrary directions. Two hexapod gaits are compared, which are the diametrical gait and the paired metachronal gait. The former is an artificial gait and the latter is a natural gait. The authors conclude that that the stability of a hexapod robot with the diametrical gait can be enhanced by increasing the offset parameter.

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6

Pepa, Lucia, Federica Verdini, and Luca Spalazzi. "Gait parameter and event estimation using smartphones." Gait & Posture 57 (September 2017): 217–23. http://dx.doi.org/10.1016/j.gaitpost.2017.06.011.

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7

Rabin, Ely, Peter Shi, and William Werner. "Gait parameter control timing with dynamic manual contact or visual cues." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 2880–92. http://dx.doi.org/10.1152/jn.00670.2015.

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We investigated the timing of gait parameter changes (stride length, peak toe velocity, and double-, single-support, and complete step duration) to control gait speed. Eleven healthy participants adjusted their gait speed on a treadmill to maintain a constant distance between them and a fore-aft oscillating cue (a place on a conveyor belt surface). The experimental design balanced conditions of cue modality (vision: eyes-open; manual contact: eyes-closed while touching the cue); treadmill speed (0.2, 0.4, 0.85, and 1.3 m/s); and cue motion (none, ±10 cm at 0.09, 0.11, and 0.18 Hz). Correlation analyses revealed a number of temporal relationships between gait parameters and cue speed. The results suggest that neural control ranged from feedforward to feedback. Specifically, step length preceded cue velocity during double-support duration suggesting anticipatory control. Peak toe velocity nearly coincided with its most-correlated cue velocity during single-support duration. The toe-off concluding step and double-support durations followed their most-correlated cue velocity, suggesting feedback control. Cue-tracking accuracy and cue velocity correlations with timing parameters were higher with the manual contact cue than visual cue. The cue/gait timing relationships generalized across cue modalities, albeit with greater delays of step-cycle events relative to manual contact cue velocity. We conclude that individual kinematic parameters of gait are controlled to achieve a desired velocity at different specific times during the gait cycle. The overall timing pattern of instantaneous cue velocities associated with different gait parameters is conserved across cues that afford different performance accuracies. This timing pattern may be temporally shifted to optimize control. Different cue/gait parameter latencies in our nonadaptation paradigm provide general-case evidence of the independent control of gait parameters previously demonstrated in gait adaptation paradigms.

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8

Decavel, Pierre, Thierry Moulin, and Yoshimasa Sagawa. "Which gait parameter can be used to evaluate gait improvement in multiple sclerosis?" Annals of Physical and Rehabilitation Medicine 59 (September 2016): e118. http://dx.doi.org/10.1016/j.rehab.2016.07.267.

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9

Lee, Daewook, Jiman Soon, Gyuri Choi, Kijoon Kim, and Sangwoo Bahn. "Identification of the Visually Prominent Gait Parameters for Forensic Gait Analysis." International Journal of Environmental Research and Public Health 19, no. 4 (February 21, 2022): 2467. http://dx.doi.org/10.3390/ijerph19042467.

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Walking patterns can be used as a key parameter in identifying individuals, as it varies visually depending on one’s body size as well as their habits, gender, and age group. In this study, we measure the gait characteristics of a large number of subjects using 34 visual parameters to identify significant parameters that can be used to distinguish individual walking features. We recorded 291 subjects’ walking on a constructed footpath using four video cameras, and data on parameters was calculated at the points of double support, toe-off, and heel-strike. K-means Clustering Analysis and ANOVA were conducted to determine the difference between age, gender, and BMI. As a result, we confirm that parameters related to the spine, neck, and feet are useful for identifying individuals. In the comparative analysis between age groups, the older the age, the more significant variables appeared in the upper body. The difference between genders showed significant parameters in both the upper and lower bodies of males. Similarly, among the large BMI groups, we also derived significant results in the upper and lower bodies. The key parameters derived from this study can be used more effectively in the real-world visual analysis of gait, as the walking characteristics of a large number of subjects have been measured with a similar view as real-world CCTV. This study will be effectively utilized as a foundation for future research attempting to identify people through their gait by distinguishing major gait characteristic differences.

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10

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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11

Xu, Junkai, Fangyuan Cao, Shi Zhan, Ming Ling, Hai Hu, and Peter B. Shull. "Mapping-Based Dosage of Gait Modification Selection for Multi-Parameter, Subject-Specific Gait Retraining." IEEE Access 8 (2020): 106354–63. http://dx.doi.org/10.1109/access.2020.2999473.

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12

Liu, Long, Huihui Wang, Haorui Li, Jiayi Liu, Sen Qiu, Hongyu Zhao, and Xiangyang Guo. "Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model." Sensors 21, no. 4 (February 14, 2021): 1347. http://dx.doi.org/10.3390/s21041347.

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Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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13

Li, Pengcheng, Yasuhiro Akiyama, Xianglong Wan, Kazunori Yamada, Mayu Yokoya, and Yoji Yamada. "Gait Phase Estimation Based on User–Walker Interaction Force." Applied Sciences 11, no. 17 (August 26, 2021): 7888. http://dx.doi.org/10.3390/app11177888.

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Smart walkers have been developed for assistance and rehabilitation of elderly people and patients with physical health conditions. A force sensor mounted under the handle is widely used in smart walkers to establish a human–machine interface. The interaction force can be used to control the walker and estimate gait parameters using methods such as the Kalman filter for real-time estimation. However, the estimation performance decreases when the peaks of the interaction force are not captured. To improve the stability and accuracy of gait parameter estimation, we propose an online estimation method to continuously estimate the gait phase and cadence. A multiple model switching mechanism is introduced to improve the estimation performance when gait is asymmetric, and an adaptive rule is proposed to improve the estimation robustness and accuracy. Simulations and experiments demonstrate the effectiveness and accuracy of the proposed gait parameter estimation method. Here, the average estimation error for the gait phase is 0.691 rad when the gait is symmetric and 0.722 rad when it is asymmetric.

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14

Dallali, Houman, Petar Kormushev, Zhibin Li, and Darwin Caldwell. "On Global Optimization of Walking Gaits for the Compliant Humanoid Robot, COMAN Using Reinforcement Learning." Cybernetics and Information Technologies 12, no. 3 (September 1, 2012): 39–52. http://dx.doi.org/10.2478/cait-2012-0020.

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Abstract In ZMP trajectory generation using simple models, often a considerable amount of trials and errors are involved to obtain locally stable gaits by manually tuning the gait parameters. In this paper a 15 degrees of Freedom dynamic model of a compliant humanoid robot is used, combined with reinforcement learning to perform global search in the parameter space to produce stable gaits. It is shown that for a given speed, multiple sets of parameters, namely step sizes and lateral sways, are obtained by the learning algorithm which can lead to stable walking. The resulting set of gaits can be further studied in terms of parameter sensitivity and also to include additional optimization criteria to narrow down the chosen walking trajectories for the humanoid robot.

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15

Iida, Masahiro, and Akira Mita. "Gait Parameter Acquisition While Chasing Resident Using Home Robot." Procedia Engineering 188 (2017): 141–47. http://dx.doi.org/10.1016/j.proeng.2017.04.467.

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16

Smith, Andrew J. J., and Edward D. Lemaire. "Temporal-spatial gait parameter models of very slow walking." Gait & Posture 61 (March 2018): 125–29. http://dx.doi.org/10.1016/j.gaitpost.2018.01.003.

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17

FRANK, T. D. "A SYNERGETIC GAIT TRANSITION MODEL FOR HYSTERETIC GAIT TRANSITIONS FROM WALKING TO RUNNING." Journal of Biological Systems 24, no. 01 (March 2016): 51–61. http://dx.doi.org/10.1142/s0218339016500030.

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A model for gait transitions from walking to running is proposed. The model is based on the theory of pattern formation and synergetics. Walking and running are considered as spatiotemporal patterns, while walk-to-run and run-to-walk transitions are regarded as bifurcations. Consequently, the model is cast in the form of coupled amplitude equations as known in the literature on pattern formation. It is shown that the model can reproduce hysteretic gait transitions that have been observed in experimental studies with humans walking on treadmills when locomotion speed is gradually increased and decreased. The control parameter is an appropriately rescaled velocity measure, the so-called Froude number, which is a body-scaled parameter that takes leg length into account. It is shown that the model can reproduce observed gait transitions for individuals and populations. In particular, probabilistic functions describing gait transitions on the population level can be defined that resemble the experimentally determined probabilistic function.

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18

Xing-Hua, Lu, Huang Peng-Fen, and Huang Wei-Peng. "Robust control method for bionic gait of machine legs based on time delay feedback." MATEC Web of Conferences 173 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201817302009.

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The bionic machine leg is disturbed by the joint during the walking process, which is easy to produce time delay, which causes the robustness of the control of the machine leg is not good. In order to improve the robustness of the bionic gait control of the machine leg, a robust control method for the bionic gait of the machine leg based on time - delay feedback is proposed. The gait correlation parameters of robot leg are collected by sensor array, and the dynamic model of bionic gait is constructed. The fuzzy controller of bionic gait of robot leg is constructed by using time-delay coupling control method. The delayed feedback control error compensation method of machine leg correction is taken to improve the steady control performance of the robotic leg, reduce the steady-state error, improve the robustness of the control machine leg. The simulation results show that this method is robust to the bionic gait control of the machine leg. The output error of the gait parameter can quickly converge to zero, and the accurate estimation of the attitude parameter is stronger.

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19

Fujiwara, Shunrou, Shinpei Sato, Atsushi Sugawara, Yasumasa Nishikawa, Takahiro Koji, Yukihide Nishimura, and Kuniaki Ogasawara. "The Coefficient of Variation of Step Time Can Overestimate Gait Abnormality: Test-Retest Reliability of Gait-Related Parameters Obtained with a Tri-Axial Accelerometer in Healthy Subjects." Sensors 20, no. 3 (January 21, 2020): 577. http://dx.doi.org/10.3390/s20030577.

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The aim of this study was to investigate whether variation in gait-related parameters among healthy participants could help detect gait abnormalities. In total, 36 participants (21 men, 15 women; mean age, 35.7 ± 9.9 years) performed a 10-m walk six times while wearing a tri-axial accelerometer fixed at the L3 level. A second walk was performed ≥1 month after the first (mean interval, 49.6 ± 7.6 days). From each 10-m data set, the following nine gait-related parameters were automatically calculated: assessment time, number of steps, stride time, cadence, ground force reaction, step time, coefficient of variation (CV) of step time, velocity, and step length. Six repeated measurement values were averaged for each gait parameter. In addition, for each gait parameter, the difference between the first and second assessments was statistically examined, and the intraclass correlation coefficient (ICC) was calculated with the level of significance set at p < 0.05. Only the CV of step time showed a significant difference between the first and second assessments (p = 0.0188). The CV of step time also showed the lowest ICC, at <0.50 (0.425), among all parameters. Test–retest results of gait assessment using a tri-axial accelerometer showed sufficient reproducibility in terms of the clinical evaluation of all parameters except the CV of step time.

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20

Lee, Su-Kyoung, Sang-Yeol Lee, Min-Chull Park, and Kyung Kim. "The Correlation of Gait Velocity, Cadence and Gait Quality Parameter Using Points of Gait Quality Chart (GQC) Items in Hemiplegic Patients." Journal of Physical Therapy Science 23, no. 5 (2011): 765–67. http://dx.doi.org/10.1589/jpts.23.765.

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21

LIU, JIAN, THURMON LOCKHART, and SUKWON KIM. "PREDICTION OF THE SPATIO-TEMPORAL GAIT PARAMETERS USING INERTIAL SENSOR." Journal of Mechanics in Medicine and Biology 18, no. 07 (November 2018): 1840002. http://dx.doi.org/10.1142/s021951941840002x.

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Monitoring human gait is essential to quantify gait issues associated with fall-prone individuals as well as other gait-related movement disorders. Being portable and cost-effective, ambulatory gait analysis using inertial sensors is considered a promising alternative to traditional laboratory-based approach. The current study aimed to provide a method for predicting the spatio-temporal gait parameters using the wrist-worn inertial sensors. Eight young adults were involved in a laboratory study. Optical motion analysis system and force-plates were used for the assessment of baseline gait parameters. Spatio-temporal features of an Inertial Measurement Unit (IMU) on the wrist were analyzed. Multi-variate correlation analyses were performed to develop gait parameter prediction models. The results indicated that gait stride time was strongly correlated with peak-to-peak duration of wrist gyroscope signal in the anterio-posterior direction. Meanwhile, gait stride length was successfully predicted using a combination model of peak resultant wrist acceleration and peak sagittal wrist angle. In conclusion, current study provided the evidence that the wrist-worn inertial sensors are capable of estimating spatio-temporal gait parameters. This finding paves the foundation for developing a wrist-worn gait monitor with high user compliance.

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22

Chopra, Swati, Hossein Rouhani, Kevin Moerenhout, Julien Favre, Kamiar Aminian, and Xavier Crevoisier. "Outcome of ankle arthrodesis and total ankle replacement for ankle arthrosis in terms of gait variability." Journal of Biomedical Engineering and Informatics 2, no. 1 (September 21, 2015): 31. http://dx.doi.org/10.5430/jbei.v2n1p31.

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Background: Higher variability in gait parameters has been reported in patients’ with arthrosis of hip and knee joints, leading to dynamic instability and increased risk of falling. Higher variability and gait instability has also been noted to persist months after surgery. Ankle arthrosis is likely similar, but information regarding this is lacking. The purpose of the study was to evaluate gait variability following ankle arthrosis and its surgical treatments, ankle arthrodesis and total ankle replacement. Methods: Seventy participants, divided into controls, ankle arthrosis, ankle arthrodesis and total ankle replacement, performed gait assessment wearing 3-D inertial sensors through which gait parameters and the variability were compared among groups. Correlations between American Orthopedic Foot and Ankle score and Foot and Ankle Ability Measure score with the gait parameters and their variability were calculated. Results: All three case groups reported gait variability similar to that of the controls. On the contrary, significant differences (P < .01) were reported in several gait parameters when compared to the controls in all case groups. Furthermore, both clinical scores showed little to no correlation with gait variability and a good correlation was reported with gait parameters (P < .0001). Conclusions: The study did not find gait variability to be as reliable compared with gait parameters when assessing the outcome of ankle surgeries. Furthermore, among the gait parameters, walking speed showed a strong correlation with the patients’ functional status and is confirmed as an important parameter for ankle arthrosis.

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23

Hannink, Julius, Thomas Kautz, Cristian F. Pasluosta, Karl-Gunter Gasmann, Jochen Klucken, and Bjoern M. Eskofier. "Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks." IEEE Journal of Biomedical and Health Informatics 21, no. 1 (January 2017): 85–93. http://dx.doi.org/10.1109/jbhi.2016.2636456.

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24

NAKAE, HIDEYUKI, SHIN MURATA, YOSHIHIRO KAI, MASAYUKI SOMA, and YOUSUKE SATOU. "Relationship of gait parameter with physical function in healthy women." Japanese Journal of Health Promotion and Physical Therapy 6, no. 1 (2016): 9–15. http://dx.doi.org/10.9759/hppt.6.9.

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25

Orito, Kensuke, Shogo Kurozumi, Idaku Ishii, Akane Tanaka, Junko Sawada, and Hiroshi Matsuda. "A Sensitive Gait Parameter for Quantification of Arthritis in Rats." Journal of Pharmacological Sciences 103, no. 1 (2007): 113–16. http://dx.doi.org/10.1254/jphs.sc0060156.

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26

Pearsall, D. J., and P. A. Costigan. "The effect of segment parameter error on gait analysis results." Gait & Posture 9, no. 3 (July 1999): 173–83. http://dx.doi.org/10.1016/s0966-6362(99)00011-9.

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27

Mustapa, Amirah, Maria Justine, Nadia Mohd Mustafah, and Haidzir Manaf. "The Impact of Diabetic Peripheral Neuropathy on Spatiotemporal Gait Parameters in Stroke Survivors: A Case-Control Study." Sains Malaysiana 50, no. 1 (January 31, 2021): 191–99. http://dx.doi.org/10.17576/jsm-2021-5001-19.

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The deterioration of gait performance following stroke is related to the impairment of sensorimotor function on the paretic side. Improper gait performance in post-stroke with additional diabetic peripheral neuropathy (DPN) on paretic and non-paretic legs may create destabilizing effects, including serious injuries and falls. Therefore, this study aimed to investigate the effect of DPN on spatiotemporal gait parameters in stroke survivors and determine the correlation of movement functioning and functional balance post-stroke with gait parameters. Ten stroke survivors with DPN, 10 stroke survivors without DPN and 10 healthy controls participated in this case-control study. Movement functioning and functional balance were assessed before the actual testing. Spatiotemporal gait parameters were recorded using the Nexus Vicon motion analysis system. Kruskal-Wallis test was used to analyze the gait parameters and Spearman’s rank-order correlation coefficient was used to identify the correlation between variables. Results showed that stroke survivors with DPN had longer stride time (temporal gait parameter, p = 0.001), lower cadence (p = 0.001) and greater gait variability than those without DPN and the healthy controls. The gait parameters were significantly correlated with movement functioning and functional balance in stroke survivors with DPN (p < 0.05). These findings suggested that DPN possibly affected the gait parameters in stroke survivors. DPN could also play a role in movement functioning and functional balance in stroke survivors.

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Santinelli, Felipe Balistieri, Emerson Sebastião, Marina Hiromi Kuroda, Vinicius Christianini Moreno, Julia Pilon, Luiz Henrique Palucci Vieira, and Fabio Augusto Barbieri. "Cortical activity and gait parameter characteristics in people with multiple sclerosis during unobstructed gait and obstacle avoidance." Gait & Posture 86 (May 2021): 226–32. http://dx.doi.org/10.1016/j.gaitpost.2021.03.026.

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29

Yu, Deng, Zhang Yang, Liu Lei, Ni Chaoming, and Wu Ming. "Robot-Assisted Gait Training Plan for Patients in Poststroke Recovery Period: A Single Blind Randomized Controlled Trial." BioMed Research International 2021 (August 29, 2021): 1–7. http://dx.doi.org/10.1155/2021/5820304.

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Background. Walking dysfunction exists in most patients after stroke. Evidence regarding gait training in two weeks is scarce in resource-limited settings; this study was conducted to investigate the effects of a short-term robot-assisted gait training plan for patients with stroke. Methods. 85 patients were randomly assigned to one of two treatment groups, with 31 patients in withdrawal before treatment. The training program comprised 14 2-hour sessions, for 2 consecutive weeks. Patients allocated to the robot-assisted gait training group were treated using the Gait Training and Evaluation System A3 from NX (RT group, n = 27 ). Another group of patients was allocated to the conventional overground gait training group (PT group, n = 27 ). Outcome measurements were assessed using time-space parameter gait analysis, Fugl-Meyer Assessment (FMA), and Timed Up and Go test (TUG) scores. Results. In the time-space parameter analysis of gait, the two groups exhibited no significant changes in time parameters, but the RT group exhibited a significant effect on changes in space parameters (stride length, walk velocity, and toe out angle, P < 0.05 ). After training, FMA scores ( 20.22 ± 2.68 ) of the PT group and FMA scores ( 25.89 ± 4.6 ) of the RT group were significant. In the Timed Up and Go test, FMA scores of the PT group ( 22.43 ± 3.95 ) were significant, whereas those in the RT group ( 21.31 ± 4.92 ) were not. The comparison between groups revealed no significant differences. Conclusion. Both the RT group and the PT group can partially improve the walking ability of stroke patients within 2 weeks.

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Wang, Xin, Qing M. Wang, Zhaoxiang Meng, Zhenglu Yin, Xun Luo, and Duonan Yu. "Gait disorder as a predictor of spatial learning and memory impairment in aged mice." PeerJ 5 (January 5, 2017): e2854. http://dx.doi.org/10.7717/peerj.2854.

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Objective To investigate whether gait dysfunction is a predictor of severe spatial learning and memory impairment in aged mice. Methods A total of 100 12-month-old male mice that had no obvious abnormal motor ability and whose Morris water maze performances were not significantly different from those of two-month-old male mice were selected for the study. The selected aged mice were then divided into abnormal or normal gait groups according to the results from the quantitative gait assessment. Gaits of aged mice were defined as abnormal when the values of quantitative gait parameters were two standard deviations (SD) lower or higher than those of 2-month-old male mice. Gait parameters included stride length, variability of stride length, base of support, cadence, and average speed. After nine months, mice exhibiting severe spatial learning and memory impairment were separated from mice with mild or no cognitive dysfunction. The rate of severe spatial learning and memory impairment in the abnormal and normal gait groups was tested by a chi-square test and the correlation between gait dysfunction and decline in cognitive function was tested using a diagnostic test. Results The 12-month-old aged mice were divided into a normal gait group (n = 75) and an abnormal gait group (n = 25). Nine months later, three mice in the normal gait group and two mice in the abnormal gait group had died. The remaining mice were subjected to the Morris water maze again, and 17 out of 23 mice in the abnormal gait group had developed severe spatial learning and memory impairment, including six with stride length deficits, 15 with coefficient of variation (CV) in stride length, two with base of support (BOS) deficits, five with cadence dysfunction, and six with average speed deficits. In contrast, only 15 out of 72 mice in the normal gait group developed severe spatial learning and memory impairment. The rate of severe spatial learning and memory impairment was significantly higher in the abnormal gait group as compared to that in the normal gait group (x = 21.986, P < 0.001). All five parameters used to assess gait predicted severe spatial learning and memory impairment in aged mice (P < 0.01). However, the difference of the area under the ROC (receiver operating characteristic) curve for each quantitative gait parameter was not statistically significant. Conclusion Gait disorders are a predictor of severe spatial learning and memory impairment in aged mice, and stride length, variability of stride length, base of support, cadence, and average speed are all sensitive parameters for assessing gait.

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Leung, A. K. L., J. C. Y. Cheng, M. Zhang, Y. Fan, and X. Dong. "Contact force ratio: A new parameter to assess foot arch function." Prosthetics and Orthotics International 28, no. 2 (August 2004): 167–74. http://dx.doi.org/10.1080/03093640408726701.

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Static footprint parameters have been used to quantify arch height with conflicting results. This could be caused by the inherent inaccuracy and variations of the methodology used. Since the foot is a dynamic structure that undergoes changes during a step, it is more desirable to capture and analyse the dynamic footprint at an instant during the gait cycle that can most closely reflect the weight-bearing foot function. Forty (40) volunteer subjects were recruited for the reliability test of a new parameter, the Contact Force Ratio (CFR), derived from dynamic footprint. This is a measure of midfoot loading during gait. The mid-gait dynamic footprints were collected using a pressure sensing mat. Results of ICC tests showed that the CFR had good intratester (0.918) and intertester (0.909) reliability. The validity of the method was examined by correlating the parameter to the functional change in arch height, i.e. the Navicular Drop between the non-weight-bearing and weight-bearing conditions.

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Seo, Young-Jin, and Yong-San Yoon. "Design of a robust dynamic gait of the biped using the concept of dynamic stability margin." Robotica 13, no. 5 (September 1995): 461–68. http://dx.doi.org/10.1017/s0263574700018294.

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SummaryA computational technique for designing a physically realizable robust dynamic gait for a planar biped robot is developed. Firstly, a feasible set of gaits was constructed to satisfy the periodicity of the biped locomotion. Then the concept of dynamic, stability margin is introduced based on the robustness of a gait with respect to the external disturbances. Using that margin, we can assess the robustness of each dynamic gait in the feasible set. It is found that the parameter, called foot strike time margin, representing the readiness of the foot strike has a close positive correlation with the dynamic stability margin. We obtain a robust gait with respect to the external disturbance by maximizing the foot strike time margin. The robustness of the optimal gait is confirmed by the behavior of the gait after application of linear impulse as well as by the examination of the largest eigenvalue at the perturbed state.

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Souza, Ricardo Krause Martinez de, Samanta Fabrício Blattes da Rocha, Rodrigo Tomazini Martins, Pedro André Kowacs, and Ricardo Ramina. "Gait in normal pressure hydrocephalus: characteristics and effects of the CSF tap test." Arquivos de Neuro-Psiquiatria 76, no. 5 (May 2018): 324–31. http://dx.doi.org/10.1590/0004-282x20180037.

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ABSTRACT Normal pressure hydrocephalus (NPH), described by Hakim and Adams in 1965, is characterized by gait apraxia, urinary incontinence, and dementia. It is associated with normal cerebrospinal fluid (CSF) pressure and ventricular dilation that cannot be attributed to cerebral atrophy. Objectives: To evaluate gait characteristics in patients with idiopathic NPH and investigate the effect of the CSF tap test (CSF-TT) on gait. Methods: Twenty-five patients diagnosed with probable idiopathic NPH were submitted to the CSF-TT. The procedure aimed to achieve changes in gait parameters. Results: Fifteen gait parameters were assessed before and after the CSF-TT. Five showed a statistically significant improvement (p < 0.05): walking speed (p < 0.001), cadence (p < 0.001), step length (p < 0.001), en bloc turning (p = 0.001), and step height (p = 0.004). Conclusion: This study demonstrated that gait speed was the most responsive parameter to the CSF-TT, followed by cadence, step length, en bloc turning, and step height.

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Di Russo, Andrea, Dimitar Stanev, Stéphane Armand, and Auke Ijspeert. "Sensory modulation of gait characteristics in human locomotion: A neuromusculoskeletal modeling study." PLOS Computational Biology 17, no. 5 (May 19, 2021): e1008594. http://dx.doi.org/10.1371/journal.pcbi.1008594.

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The central nervous system of humans and other animals modulates spinal cord activity to achieve several locomotion behaviors. Previous neuromechanical models investigated the modulation of human gait changing selected parameters belonging to CPGs (Central Pattern Generators) feedforward oscillatory structures or to feedback reflex circuits. CPG-based models could replicate slow and fast walking by changing only the oscillation’s properties. On the other hand, reflex-based models could achieve different behaviors through optimizations of large dimensional parameter spaces. However, they could not effectively identify individual key reflex parameters responsible for gait characteristics’ modulation. This study investigates which reflex parameters modulate the gait characteristics through neuromechanical simulations. A recently developed reflex-based model is used to perform optimizations with different target behaviors on speed, step length, and step duration to analyze the correlation between reflex parameters and their influence on these gait characteristics. We identified nine key parameters that may affect the target speed ranging from slow to fast walking (0.48 and 1.71 m/s) as well as a large range of step lengths (0.43 and 0.88 m) and step duration (0.51, 0.98 s). The findings show that specific reflexes during stance significantly affect step length regulation, mainly given by positive force feedback of the ankle plantarflexors’ group. On the other hand, stretch reflexes active during swing of iliopsoas and gluteus maximus regulate all the gait characteristics under analysis. Additionally, the results show that the hamstrings’ group’s stretch reflex during the landing phase is responsible for modulating the step length and step duration. Additional validation studies in simulations demonstrated that the modulation of identified reflexes is sufficient to regulate the investigated gait characteristics. Thus, this study provides an overview of possible reflexes involved in modulating speed, step length, and step duration of human gaits.

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Dankowicz, Harry, Jesper Adolfsson, and Arne B. Nordmark. "Repetitive Gait of Passive Bipedal Mechanisms in a Three-Dimensional Environment." Journal of Biomechanical Engineering 123, no. 1 (October 16, 2000): 40–46. http://dx.doi.org/10.1115/1.1338121.

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The inherent dynamics of bipedal, kneed mechanisms are studied with emphasis on the existence and stability of repetitive gait in a three-dimensional environment, in the absence of external, active control. The investigation is motivated by observations that sustained anthropomorphic locomotion is largely a consequence of geometric and inertial properties of the mechanism. While the modeling excludes active control, the energy dissipated in ground and knee collisions is continuously re-injected by considering gait down slight inclines. The paper describes the dependence of the resulting passive gait in vertically constrained and unconstrained mechanisms on model parameters, such as ground compliance and ground slope. We also show the possibility of achieving statically unstable gait with appropriate parameter choices.

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Lee, Joon-Hee. "The effects of mouthguards on gait parameter in professional basketball athletes." Korean Journal of Sports Science 27, no. 2 (April 30, 2018): 1187–94. http://dx.doi.org/10.35159/kjss.2018.04.27.2.1187.

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Yang, Che-Chang, Yeh-Liang Hsu, Kao-Shang Shih, and Jun-Ming Lu. "Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System." Sensors 11, no. 8 (July 25, 2011): 7314–26. http://dx.doi.org/10.3390/s110807314.

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Lescano, C. N., S. E. Rodrigo, and D. A. Christian. "A possible parameter for gait clinimetric evaluation in Parkinson’s disease patients." Journal of Physics: Conference Series 705 (April 2016): 012019. http://dx.doi.org/10.1088/1742-6596/705/1/012019.

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WAGATSUMA, Toma, Minoru HASHIMOTO, and Atsushi TSUKAHARA. "Gait Support by Changing Synchronization Parameter in Real Time Using curara." Proceedings of Mechanical Engineering Congress, Japan 2018 (2018): J1510101. http://dx.doi.org/10.1299/jsmemecj.2018.j1510101.

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Parthasarathy, Aniruddha, Megharjun V.N., and Viswanath Talasila. "Forecasting a gait cycle parameter region to enable optimal FES triggering." IFAC-PapersOnLine 53, no. 1 (2020): 232–39. http://dx.doi.org/10.1016/j.ifacol.2020.06.040.

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Couillandre, Annabelle, and Yvon Brenière. "How Does the Heel-Off Posture Modify Gait Initiation Parameter Programming?" Journal of Motor Behavior 35, no. 3 (September 2003): 221–27. http://dx.doi.org/10.1080/00222890309602136.

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Xie, Lei, Peicheng Yang, Chuyu Wang, Tao Gu, Gaolei Duan, Xinran Lu, and Sanglu Lu. "GaitTracker: 3D Skeletal Tracking for Gait Analysis Based on Inertial Measurement Units." ACM Transactions on Sensor Networks 18, no. 2 (May 31, 2022): 1–27. http://dx.doi.org/10.1145/3502722.

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Gait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, which implies that the patients cannot receive adequate gait assessment anytime and anywhere. In this article, we propose GaitTracker, a novel system to remotely and continuously perform gait monitoring and analysis by three-dimensional (3D) skeletal tracking in a wearable approach. Specifically, this system consists of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. According to the measurements from these IMUs, we can obtain the motion signals of lower limbs during gait rehabilitation. By adaptively synchronizing coordinate systems of different IMUs and building the geometric model of lower limbs, the exact gait movements can be reconstructed, and gait parameters can be extracted without any prior knowledge. GaitTracker offers three key features: (1) a unified 3D skeletal model to depict the precise gait movement and parameters in 3D space, (2) a coordinate system synchronization scheme to perform space synchronization over all the IMU sensors, and (3) an automatic estimation method for the user-specific geometric parameters. In this way, GaitTracker is able to accurately perform 3D skeletal tracking of lower limbs for gait analysis, such as evaluating the gait symmetry and the gait parameters including the swing/stance time. We implemented GaitTracker and evaluated its performance in real applications. The experimental results show that, the average error for skeleton angle estimation, joint displacement estimation, and gait parameter estimation are 3∘, 2.3%, and 3%, respectively, outperforming the state of the art.

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Gao, Farong, Taixing Tian, Ting Yao, and Qizhong Zhang. "Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms." Computational Intelligence and Neuroscience 2021 (February 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/6693206.

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Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

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Yang, Kun, Xuewen Rong, Lelai Zhou, and Yibin Li. "Modeling and Analysis on Energy Consumption of Hydraulic Quadruped Robot for Optimal Trot Motion Control." Applied Sciences 9, no. 9 (April 28, 2019): 1771. http://dx.doi.org/10.3390/app9091771.

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Energy consumption is an important performance index of quadruped robots. In this paper, the energy consumptions of the quadruped robot SCalf with a trot gait under different gait parameters are analyzed. Firstly, the kinematics and dynamics models of the robot are established. Then, an energy model including the mechanical power and heat rate is proposed. To obtain the energy consumption, a cubic spline interpolation foot trajectory is used, and the feet forces are calculated by using the minimization of norm of the foot force method. Moreover, an energetic criterion measuring the energy cost is defined to evaluate the motion. Finally, the gait parameters such as step height, step length, standing height, gait cycle, and duty cycle that influence the energy consumption are studied, which could provide a theoretical basis for parameter optimization and motion control of quadruped robots.

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45

Damale, Pranav U., Edwin K. P. Chong, Sean L. Hammond, and Ronald B. Tjalkens. "A Low-Cost, Autonomous Gait Detection and Estimation System for Analyzing Gait Impairments in Mice." Journal of Healthcare Engineering 2021 (November 12, 2021): 1–14. http://dx.doi.org/10.1155/2021/9937904.

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With the advancement in imaging technology, many commercial systems have been developed for performing motion analysis in mice. However, available commercial systems are expensive and use proprietary software. In this paper, we describe a low-cost, camera-based design of an autonomous gait acquisition and analysis system for inspecting gait deficits in C57BL/6 mice. Our system includes video acquisition, autonomous gait-event detection, gait-parameter extraction, and result visualization. We provide a simple, user-friendly, step-by-step detailed methodology to apply well-known image processing techniques for detecting mice footfalls and calculating various gait parameters for analyzing gait abnormalities in healthy and neurotraumatic mice. The system was used in a live animal study for assessing recovery in a mouse model of Parkinson’s disease. Using the videos acquired in the study, we validate the performance of our system with receiver operating characteristic (ROC) and Hit : Miss : False (H : M : F) detection analyses. Our system correctly detected the mice footfalls with an average H : M : F score of 92.1 : 2.3 : 5.6. The values for the area under an ROC curve for all the ROC plots are above 0.95, which indicates an almost perfect detection model. The ROC and H : M : F analyses show that our system produces accurate gait detection. The results observed from the gait assessment study are in agreement with the known literature. This demonstrates the practical viability of our system as a gait analysis tool.

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Aftab, Zohaib, Gulraiz Ahmed, Asad Ali, and Nazia Gillani. "Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms." Applied Bionics and Biomechanics 2022 (October 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/2883026.

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Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.

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Misu, Shogo, Tsuyoshi Asai, Shunsuke Murata, Ryo Nakamura, Tsunenori Isa, Yamato Tsuboi, Kensuke Oshima, et al. "Association between Abnormal Gait Patterns and an Elevated Degree of Pain after Daily Walking: A Preliminary Study." International Journal of Environmental Research and Public Health 19, no. 5 (March 1, 2022): 2842. http://dx.doi.org/10.3390/ijerph19052842.

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This study aimed to investigate whether abnormal gait patterns are associated with experiencing an elevated degree of pain after daily walking. In this preliminary, cross-sectional study, 223 community-dwelling older adults were assessed for pain experienced after daily walking using a simple question that involved asking the subject about their past experiences of an elevated degree of pain after walking for 400 m or more. Gait patterns were assessed using the Comprehensive Gait Assessment using InerTial Sensor score (C-GAITS score), derived from the data measured by Inertial sensors attached to the lower trunk and heel when subjects walked along a 15 m walkway at a self-selected preferred speed. The score was the sum of 10 gait parameter scores. The lower scores indicated more and worse abnormal gait patterns. In total, 24 older adults (10.8%) reported that they experienced pain after daily walking. According to the multiple logistic regression analyses, older adults with a lower total C-GAITS score had a significantly greater probability of having past experiences of pain after walking (odds ratio = 1.11, 95% confidence interval = 1.03–1.20). The findings of this study suggest that more and worse abnormal gait patterns among older adults in a clinical walking test are associated with an elevated degree of pain after daily walking.

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Silva, Rui, Ivo Santos Dimas, Justin W. Fernandez, Nuno Alves, Pedro Morouço, Ana Colette Maurício, António Veloso, and Sandra Amado. "Sheep Gait Biomechanics and the Assessment of Musculoskeletal Conditions: A Systematic Review." Applied Mechanics and Materials 890 (April 2019): 248–59. http://dx.doi.org/10.4028/www.scientific.net/amm.890.248.

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In recent years, sheep have been reported as the ideal animal model to study osteoporosis, hence it is important to identify instruments, tools and ideal parameters needed to assess the effects of different treatments. In previous studies conducted in other animal models with osteoporosis, the most common parameters used for evaluation concerned primarily bone properties, such as the dual X-ray absorptiometry. However, biomechanical gait analysis as an integrative functional parameter and a non-invasive method, will be an important tool in research and clinical applications. This research review was performed using the PubMed database and included studies related to sheep with outcome measures concerning functional performance assessed during gait in vivo; and excluded studies related with cardiovascular disease and sperm properties, which include other animal species, with outcomes not related with functional locomotor evaluation. Only studies related with bone properties were analyzed. The most frequent and relevant included parameters were the following: mean peak vertical ground force reaction, gait cycle and stance/swing phase duration, percentage of stance/swing phase in a gait cycle, stride length and the stifle joint angles during a gait cycle. Gait biomechanical parameters have been established for the assessment of some clinical orthopedic condition using sheep models but not currently for osteoporosis.

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Kim, Jeong-Kyun, Myung-Nam Bae, Kangbok Lee, Jae-Chul Kim, and Sang Gi Hong. "Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life." Biosensors 12, no. 3 (March 7, 2022): 167. http://dx.doi.org/10.3390/bios12030167.

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Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.

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Hamandi, Sadiq J., Marwa Azzawi, and Waleed Abdulwahed. "Gait Analysis after Unilateral Total Hip Replacement Surgery." Al-Nahrain Journal for Engineering Sciences 21, no. 4 (December 20, 2018): 458–66. http://dx.doi.org/10.29194/njes.21040458.

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Total hip replacement (THR) is an elective surgical procedure with the primary indication being pain relief. The aim of this study is to analyze gait dynamics for patients after they underwent a unilateral THR surgery and compare it with normal parameters. To investigate the gait dynamics a gait analysis was performed on five patients after they underwent a unilateral THR surgery; only two of them were examined before the surgery. The gait analysis was performed using a digital video camera with two force plates. Kinematics data were obtained from 2D trajectories of seven passive markers using SkillSpector software. MATLAB software has been used for inverse dynamics computation. General gait parameters, Harris Hip Score, joints’ angles, forces, moments and powers were obtained during gait cycle. It was found that the average of improvement in Harris Hip Score (for four patients who were examined 1.5, 2.5, 3 and 9 months after surgery) is 61.8 points, which is an indication of pain relief. In the other hand, the general gait parameters were found slightly lower than normal after THR surgery. The average hip reaction force was found to be 2.988 N/BW, which is within normal range. Also, the average of maximum hip extension and maximum hip flexion angles were found to be 25.69 and -13.524 degree respectively, which both are within normal ranges. Furthermore, hip, knee and ankle moments and powers results showed some abnormality. Therefore as a conclusion, patient satisfaction and functional improvement are not related to general gait parameter. And it is not unusual that gait mechanics improvement would not reach normal after months of recovery. Also, the results of gait dynamics which are from the engineer’s perspective are compatible with Harris Hip Score, which is from the physician’s perspective, in quantifying surgical results and subsequent recovery progress.

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