scholarly journals Spatial-temporal and kinematic characteristics of gait carrying loaded backpack with elastic straps

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5207-5213
Author(s):  
Zhiguan Huang ◽  
Xinyi Sui ◽  
Xiaxu He
Keyword(s):  
Muscle Fatigue ◽  
Motion Capture ◽  
Metabolic Cost ◽  
Gait Pattern ◽  
Vertical Position ◽  
Test Plate ◽  
3D Motion ◽  
Gait Parameters ◽  
Elastic Strap ◽  
3D Motion Capture

Objective: Applying 3D Motion Capture technology to analyze the kinematic and spatial temporal characteristics of gait when carrying loaded backpacks with elastic straps, the study investigates the effect of backpacks with elastic-strap on fatigue during walking. Methods: Ten healthy male undergraduates did not do strenuous or fatigue exercise two days before the trials and served as subjects (aged 19 ? 1.8 years). All subjects provided a written and oral explanation of the trial procedures. In two trials departed by two days apart, the subjects randomly carried one of the two kinds of backpacks ( with non-elastic straps or with elastic straps backpacks loaded the same weight books (about 10% of their bodyweight). After pasted with markers as the guide of the Vicon 3D motion capture system, the subjects walked on the treadmill at 4.5 km/h for 20 min in each trial. Before and after each walking trial, the gait parameters of the subjects were measured by a 6 meters-long plantar pressure test plate(Zebras FDM System). Results: The change of the COP and the change of the gait parameters in the subjects with elastic straps backpacks after 20 min walking were relatively less than those with no elastic-strap backpacks, showing better stability and delaying fatigue. From this statement, it can be reasoned that being able to reduce the effect of the load on the person by returning the gait pattern closer to an unloaded walking pattern may reduce problems associated with loaded carriage. The elastic straps decreased the elevation of the mean vertical position of the backpack and create a gap between the shoulders and the shoulder pads. Conclusion: The decompression backpack with elastic straps shows the effect of protecting the user?s spine and in delaying the cervical back muscle fatigue, during walking at appropriate pace maybe reduce the risk of musculoskeletal injuries and muscle fatigue associated with carrying heavy backpack loads while reducing the metabolic cost of loaded walking.

scholarly journals A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3287 ◽  
Author(s):  
Satyabrata Aich ◽  
Pyari Pradhan ◽  
Jinse Park ◽  
Nitin Sethi ◽  
Vemula Vathsa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Motion Capture ◽  
Error Rate ◽  
Freezing Of Gait ◽  
Motion Capture System ◽  
3D Motion ◽  
Mean Error ◽  
Gait Parameters ◽  
Fog Prediction ◽  
3D Motion Capture

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.

Gender differences in the Achilles tendon load during the fencing lunge

2014 ◽  
Vol 6 (3) ◽  
Author(s):  
Jonathan Kenneth Sinclair ◽  
Lindsay Bottoms
Keyword(s):  
Gender Differences ◽  
Achilles Tendon ◽  
Motion Capture ◽  
Tendon Injury ◽  
Force Platform ◽  
Motion Capture System ◽  
3D Motion ◽  
Loading Rates ◽  
Tendon Pathology ◽  
3D Motion Capture

AbstractRecent epidemiological analyses in fencing have shown that injuries and pain linked specifically to fencing training/competition were evident in 92.8% of fencers. Specifically the prevalence of Achilles tendon pathology has increased substantially in recent years, and males have been identified as being at greater risk of Achilles tendon injury compared to their female counterparts. This study aimed to examine gender differences in Achilles tendon loading during the fencing lunge.Achilles tendon load was obtained from eight male and eight female club level epee fencers using a 3D motion capture system and force platform information as they completed simulated lunges. Independent t-tests were performed on the data to determine whether differences existed.The results show that males were associated with significantly greater Achilles tendon loading rates in comparison to females.This suggests that male fencers may be at greater risk from Achilles tendon pathology as a function of fencing training/ competition.

Validity of Linear Position Transducers Versus the Optotrak 3D Motion Capture System

2017 ◽  
Vol 49 (5S) ◽  
pp. 757
Author(s):  
Jessica L. Halle ◽  
Jacob A. Goldsmith ◽  
Cameron Trepeck ◽  
Ryan K. Byrnes ◽  
Daniel M. Cooke ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture

ENHANCING DYNAMOMETER EXPERIMENTS WITH 6D FORCE REGISTRATION AND 3D MOTION CAPTURE

2012 ◽  
Vol 45 ◽  
pp. S376
Author(s):  
Anke A. Van Campen ◽  
Friedl De Groote ◽  
Ilse Jonkers ◽  
Joris De Schutter
Keyword(s):  
Motion Capture ◽  
3D Motion ◽  
3D Motion Capture

scholarly journals 3D Motion Capture May Detect Spatiotemporal Changes in Pre-Reaching Upper Extremity Movements with and without a Real-Time Constraint Condition in Infants with Perinatal Stroke and Cerebral Palsy: A Longitudinal Case Series

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7312
Author(s):  
Julia Mazzarella ◽  
Mike McNally ◽  
Daniel Richie ◽  
Ajit M. W. Chaudhari ◽  
John A. Buford ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Real Time ◽  
Upper Extremity ◽  
Motion Capture ◽  
The Body ◽  
Laboratory Simulation ◽  
Perinatal Stroke ◽  
3D Motion ◽  
Spatiotemporal Characteristics ◽  
3D Motion Capture

Perinatal stroke (PS), occurring between 20 weeks of gestation and 28 days of life, is a leading cause of hemiplegic cerebral palsy (HCP). Hallmarks of HCP are motor and sensory impairments on one side of the body—especially the arm and hand contralateral to the stroke (involved side). HCP is diagnosed months or years after the original brain injury. One effective early intervention for this population is constraint-induced movement therapy (CIMT), where the uninvolved arm is constrained by a mitt or cast, and therapeutic activities are performed with the involved arm. In this preliminary investigation, we used 3D motion capture to measure the spatiotemporal characteristics of pre-reaching upper extremity movements and any changes that occurred when constraint was applied in a real-time laboratory simulation. Participants were N = 14 full-term infants: N = six infants with typical development; and N = eight infants with PS (N = three infants with PS were later diagnosed with cerebral palsy (CP)) followed longitudinally from 2 to 6 months of age. We aimed to evaluate the feasibility of using 3D motion capture to identify the differences in the spatiotemporal characteristics of the pre-reaching upper extremity movements between the diagnosis group, involved versus uninvolved side, and with versus and without constraint applied in real time. This would be an excellent application of wearable sensors, allowing some of these measurements to be taken in a clinical or home setting.

Sign in / Sign up

Export Citation Format

Share Document