scholarly journals Uniqueness of gait kinematics in a cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gunwoo Park ◽  
Kyoung Min Lee ◽  
Seungbum Koo
Keyword(s):  
Motion Capture ◽  
Gait Cycle ◽  
Three Dimensional ◽  
Support Vector ◽  
Behavioral Characteristic ◽  
Joint Angles ◽  
Facial Motion ◽  
Gait Kinematics ◽  
Body Joint ◽  
Full Body

AbstractGait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the identification power or the uniqueness. This study aims to quantify the uniqueness of gait in a cohort. Three-dimensional full-body joint kinematics were obtained during normal walking trials from 488 subjects using a motion capture system. The joint angles of the gait cycle were converted into gait vectors. Four gait vectors were obtained from each subject, and all the gait vectors were pooled together. Two gait vectors were randomly selected from the pool and tested if they could be accurately classified if they were from the same person or not. The gait from the cohort was classified with an accuracy of 99.71% using the support vector machine with a radial basis function kernel as a classifier. Gait of a person is as unique as his/her facial motion and finger impedance, but not as unique as fingerprints.

scholarly journals Next-Generation Low-Cost Motion Capture Systems Can Provide Comparable Spatial Accuracy to High-End Systems

2013 ◽  
Vol 29 (1) ◽  
pp. 112-117 ◽  
Author(s):  
Dominic Thewlis ◽  
Chris Bishop ◽  
Nathan Daniell ◽  
Gunther Paul
Keyword(s):  
Motion Capture ◽  
Low Cost ◽  
Three Dimensional ◽  
Gait Kinematics ◽  
Occupational Tasks ◽  
Soft Tissue Artifact ◽  
New Generation ◽  
The Impact ◽  
Linear Accuracy ◽  
Motion Capture Systems

The objective quantification of three-dimensional kinematics during different functional and occupational tasks is now more in demand than ever. The introduction of new generation of low-cost passive motion capture systems from a number of manufacturers has made this technology accessible for teaching, clinical practice and in small/medium industry. Despite the attractive nature of these systems, their accuracy remains unproved in independent tests. We assessed static linear accuracy, dynamic linear accuracy and compared gait kinematics from a Vicon MX-f20 system to a Natural Point OptiTrack system. In all experiments data were sampled simultaneously. We identified both systems perform excellently in linear accuracy tests with absolute errors not exceeding 1%. In gait data there was again strong agreement between the two systems in sagittal and coronal plane kinematics. Transverse plane kinematics differed by up to 3° at the knee and hip, which we attributed to the impact of soft tissue artifact accelerations on the data. We suggest that low-cost systems are comparably accurate to their high-end competitors and offer a platform with accuracy acceptable in research for laboratories with a limited budget.

Kinematic Gait Patterns in Competitive and Recreational Runners

2017 ◽  
Vol 33 (4) ◽  
pp. 268-276 ◽  
Author(s):  
Christian A. Clermont ◽  
Sean T. Osis ◽  
Angkoon Phinyomark ◽  
Reed Ferber
Keyword(s):  
Classification Accuracy ◽  
Three Dimensional ◽  
Principal Component ◽  
Treadmill Running ◽  
Support Vector ◽  
Gait Kinematics ◽  
Ankle Eversion ◽  
Machine Learning Approach ◽  
Recreational Runners ◽  
Time Point

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using multivariate analyses and a machine learning approach. Participants were allocated to the ‘competitive’ (n = 20) or ‘recreational’ group (n = 15) based on age, sex, and recent race performance. Three-dimensional (3D) kinematic data were collected during treadmill running at 2.7 m/s. A support vector machine (SVM) was used to determine if the groups were separable and classifiable based on kinematic time point variables as well as principal component (PC) scores. A cross-fold classification accuracy of 80% was found between groups using the top 5 ranked time point variables, and the groups could be separated with 100% cross-fold classification accuracy using the top 14 ranked PCs explaining 60.29% of the variance in the data. The features were primarily related to pelvic tilt, as well as knee flexion and ankle eversion in late stance. These results suggest that competitive and recreational runners have distinct, ‘typical’ running patterns that may help explain differences in injury mechanisms.

scholarly journals The Influence of X-Factor (Trunk Rotation) and Experience on the Quality of the Badminton Forehand Smash

2016 ◽  
Vol 53 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Zhao Zhang ◽  
Shiming Li ◽  
Bingjun Wan ◽  
Peter Visentin ◽  
Qinxian Jiang ◽  
...  
Keyword(s):  
Motion Capture ◽  
Teaching And Learning ◽  
Three Dimensional ◽  
Biomechanical Model ◽  
Kinematic Chain ◽  
The Body ◽  
Trunk Rotation ◽  
Preparation Phase ◽  
Full Body

AbstractNo existing studies of badminton technique have used full-body biomechanical modeling based on three-dimensional (3D) motion capture to quantify the kinematics of the sport. The purposes of the current study were to: 1) quantitatively describe kinematic characteristics of the forehand smash using a 15-segment, full-body biomechanical model, 2) examine and compare kinematic differences between novice and skilled players with a focus on trunk rotation (the X-factor), and 3) through this comparison, identify principal parameters that contributed to the quality of the skill. Together, these findings have the potential to assist coaches and players in the teaching and learning of the forehand smash. Twenty-four participants were divided into two groups (novice, n = 10 and skilled, n = 14). A 10-camera VICON MX40 motion capture system (200 frames/s) was used to quantify full-body kinematics, racket movement and the flight of the shuttlecock. Results confirmed that skilled players utilized more trunk rotation than novices. In two ways, trunk rotation (the X-factor) was shown to be vital for maximizing the release speed of the shuttlecock – an important measure of the quality of the forehand smash. First, more trunk rotation invoked greater lengthening in the pectoralis major (PM) during the preparation phase of the stroke which helped generate an explosive muscle contraction. Second, larger range of motion (ROM) induced by trunk rotation facilitated a whip-like (proximal to distal) control sequence among the body segments responsible for increasing racket speed. These results suggest that training intended to increase the efficacy of this skill needs to focus on how the X-factor is incorporated into the kinematic chain of the arm and the racket.

scholarly journals Inter-session repeatability of markerless motion capture gait kinematics

2020 ◽  
Author(s):  
Robert M. Kanko ◽  
Elise Laende ◽  
W. Scott Selbie ◽  
Kevin J. Deluzio
Keyword(s):  
Motion Capture ◽  
Lower Limb ◽  
Video Data ◽  
Joint Angles ◽  
Markerless Tracking ◽  
Gait Kinematics ◽  
Markerless Motion Capture ◽  
Adult Participants ◽  
Total Variability ◽  
Over Ground Walking

AbstractThe clinical uptake and influence of gait analysis has been hindered by inherent flaws of marker-based motion capture systems, which have long been the standard method for the collection of gait data including kinematics. Markerless motion capture offers an alternative method for the collection of gait kinematics that presents several practical benefits over marker-based systems. This work aimed to determine the reliability of lower limb gait kinematics from video based markerless motion capture using an established experimental protocol for testing reliability. Eight healthy adult participants performed three sessions of five over-ground walking trials in their own self-selected clothing, separated by an average of 8.5 days, while eight synchronized and calibrated cameras recorded video. 3D pose estimates from the video data were used to compute lower limb joint angles. Inter-session variability, inter-trial variability, and the variability ratio were used to assess the reliability of the gait kinematics. Relative to repeatability studies based on marker-based motion capture, inter-trial variability was slightly greater than previously reported for some angles, with an average across all joint angles of 2.2°. Inter-session variability was smaller on average than previously reported, with an average across all joint angles of 2.5°. Variability ratios were all smaller than those previously reported with an average of 1.2, indicating that the multi-session protocol increased the total variability of joint angles by only 20% of the inter-trial variability. These results indicate that gait kinematics measured using markerless tracking were less affected by multi-session protocols compared to marker-based motion capture.

scholarly journals Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3496
Author(s):  
Li Wang ◽  
Yajun Li ◽  
Fei Xiong ◽  
Wenyu Zhang
Keyword(s):  
Motion Capture ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Three Dimensional ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Support Vector ◽  
Surveillance Systems ◽  
Optical Motion ◽  
Optical Motion Capture

Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames.

scholarly journals Analysis of 3-D Kinematics Using H-Gait System during Walking on a Lower Body Positive Pressure Treadmill

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2619
Author(s):  
Yoshiaki Kataoka ◽  
Ryo Takeda ◽  
Shigeru Tadano ◽  
Tomoya Ishida ◽  
Yuki Saito ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Wearable Sensors ◽  
Three Dimensional ◽  
Lower Limbs ◽  
Positive Pressure ◽  
Lower Body ◽  
Lower Body Positive Pressure ◽  
Gait Kinematics ◽  
Peak Knee ◽  
Body Positive

Recently, treadmills equipped with a lower-body positive-pressure (LBPP) device have been developed to provide precise body weight support (BWS) during walking. Since lower limbs are covered in a waist-high chamber of an LBPP treadmill, a conventional motion analysis using an optical method is impossible to evaluate gait kinematics on LBPP. We have developed a wearable-sensor-based three-dimensional motion analysis system, H-Gait. The purpose of the present study was to investigate the effects of BWS by a LBPP treadmill on gait kinematics using an H-Gait system. Twenty-five healthy subjects walked at 2.5 km/h on a LBPP treadmill under the following three conditions: (1) 0%BWS, (2) 25%BWS and (3) 50%BWS conditions. Acceleration and angular velocity from seven wearable sensors were used to analyze lower limb kinematics during walking. BWS significantly decreased peak angles of hip adduction, knee adduction and ankle dorsiflexion. In particular, the peak knee adduction angle at the 50%BWS significantly decreased compared to at the 25%BWS (p = 0.012) or 0%BWS (p < 0.001). The present study showed that H-Gait system can detect the changes in gait kinematics in response to BWS by a LBPP treadmill and provided a useful clinical application of the H-Gait system to walking exercises.

scholarly journals A Novel Suture Training System for Open Surgery Replicating Procedures Performed by Experts Using Augmented Reality

2021 ◽  
Vol 45 (5) ◽  
Author(s):  
Yuri Nagayo ◽  
Toki Saito ◽  
Hiroshi Oyama
Keyword(s):  
Augmented Reality ◽  
Motion Capture ◽  
Open Surgery ◽  
Three Dimensional ◽  
Training System ◽  
Surgical Skills ◽  
Surgical Instruments ◽  
Interrupted Suture ◽  
Education Environment ◽  
Surgical Field

AbstractThe surgical education environment has been changing significantly due to restricted work hours, limited resources, and increasing public concern for safety and quality, leading to the evolution of simulation-based training in surgery. Of the various simulators, low-fidelity simulators are widely used to practice surgical skills such as sutures because they are portable, inexpensive, and easy to use without requiring complicated settings. However, since low-fidelity simulators do not offer any teaching information, trainees do self-practice with them, referring to textbooks or videos, which are insufficient to learn open surgical procedures. This study aimed to develop a new suture training system for open surgery that provides trainees with the three-dimensional information of exemplary procedures performed by experts and allows them to observe and imitate the procedures during self-practice. The proposed system consists of a motion capture system of surgical instruments and a three-dimensional replication system of captured procedures on the surgical field. Motion capture of surgical instruments was achieved inexpensively by using cylindrical augmented reality (AR) markers, and replication of captured procedures was realized by visualizing them three-dimensionally at the same position and orientation as captured, using an AR device. For subcuticular interrupted suture, it was confirmed that the proposed system enabled users to observe experts’ procedures from any angle and imitate them by manipulating the actual surgical instruments during self-practice. We expect that this training system will contribute to developing a novel surgical training method that enables trainees to learn surgical skills by themselves in the absence of experts.

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