scholarly journals Gait analysis: technical notes

2020 ◽  
pp. 67-70
Author(s):  
Lullo Francesco ◽  
Donisi Leandro ◽  
Piscosquito Giuseppe ◽  
Lanzillo Bernardo ◽  
Coccia Armando ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Gait Analysis ◽  
Modern Medicine ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Advantages And Disadvantages ◽  
Biomedical Technologies ◽  
Measurement Units ◽  
The Right

Biomedical technologies are having an increasingly central role in the modern medicine. In fact they are at the root of the diagnosis and follow up of pathologies giving to the clinicians quantitative outcomes necessary on the choice of the right therapy. In this paper we will focus on biomedical technologies used in the context of gait analysis describing the main ones used in the clinical practice about pathologies of neurologic, orthopedic and rheumatic interest and underlining their importance in the clinical setting. The main systems for gait analysis will be presented in this article: system with passive markers, stereophotogrammetric system, force and pressure platforms, surface electromyography system, system based on inertial measurement units underling the importance of each in investigating a different aspect of movement and how integrating all of them we can have a depth and whole gait analysis. The main gait analysis protocols will be presented too. Finally, advantages and disadvantages about gait analysis will be analyzed. In conclusion, the complexity of the described biomedical technologies for gait analysis underlines the importance of the presence of an expert technician that can help the clinician to interpret and to process acquired signals during the gait analysis.

scholarly journals Biomechanical risk factors of lower back pain in cricket fast bowlers using inertial measurement units: a prospective and retrospective investigation

2020 ◽  
Vol 6 (1) ◽  
pp. e000818
Author(s):  
Billy Senington ◽  
Raymond Y Lee ◽  
Jonathan M Williams
Keyword(s):  
Back Pain ◽  
Effect Sizes ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Time To Peak ◽  
Spinal Kinematics ◽  
History Of ◽  
Measurement Units ◽  
Lumbar Extension

ObjectivesTo investigate spinal kinematics, tibial and sacral impacts during fast bowling, among bowlers with a history of low back pain (LBP) (retrospective) and bowlers who developed LBP in the follow-up season (prospective).Methods35 elite male fast bowlers; senior (n=14; age=24.1±4.3 years; height=1.89±0.05 m; weight=89.2±4.6 kg) and junior (n=21; age=16.9±0.7; height=1.81±0.05; weight=73.0±9.2 kg) were recruited from professional county cricket clubs. LBP history was gathered by questionnaire and development of LBP was monitored for the follow-up season. Spinal kinematics, tibial and sacral impacts were captured using inertial measurement units placed over S1, L1, T1 and anteromedial tibia. Bonferroni corrected pairwise comparisons and effect sizes were calculated to investigate differences in retrospective and prospective LBP groups.ResultsApproximately 38% of juniors (n=8) and 57% of seniors (n=8) reported a history of LBP. No differences were evident in spinal kinematics or impacts between those with LBP history and those without for seniors and juniors. Large effect sizes suggest greater rotation during wind-up (d=1.3) and faster time-to-peak tibial impacts (d=1.5) in those with no history of LBP. One junior (5%) and four (29%) seniors developed LBP. No differences were evident in spinal kinematics or impacts between those who developed LBP and those who did not for seniors. In seniors, those who developed LBP had lower tibial impacts (d=1.3) and greater lumbar extension (d=1.9) during delivery.ConclusionRetrospective analysis displayed non-significant differences in kinematics and impacts. It is unclear if these are adaptive or impairments. Prospective analysis demonstrated large effect sizes for lumbar extension during bowling suggesting a target for future coaching interventions.

scholarly journals An Exploratory Study Comparing the Hand Arm Vibration Exposures Measured from Bucking Bars using Traditional Tool-mounted Accelerometers and Wrist Worn IMUs

2019 ◽  
Vol 63 (1) ◽  
pp. 1048-1051
Author(s):  
Wadih Zaklit ◽  
Per Reinhall ◽  
Livia Anderson ◽  
Szymon Sarnowicz ◽  
Cassidy Quigley ◽  
...  
Keyword(s):  
Laboratory Study ◽  
Exploratory Study ◽  
Inertial Measurement Units ◽  
Upper Arm ◽  
Inertial Measurement ◽  
Steel Core ◽  
Hand Tool ◽  
Measurement Units ◽  
Left And Right ◽  
The Right

This controlled, exploratory laboratory study was conducted to measure hand-arm vibration (HAV) exposures when using a bucking bar fitted with three different handles: 1) a typical plastic handle, 2) a similarly designed handle with an aluminum core and a built-in dampening spring, and 3) a steel core handle with the same built-in spring. Using two experienced machinists, tri-axial HAV exposures were measured at the bucking bar handles; small, battery-powered, self-contained Inertial Measurement Units (IMUs) were used to measure the vibration transmitted through the tool operator’s left and right arms; and a wearable device worn on the operator’s right wrist estimated the HAV exposures occurring at the tool handle. The plastic handle produced the highest HAV exposures while the aluminum-spring and steel spring handles reduced exposures by 19% and 39%, respectively. On average, 74%, 65% and 40% of the tool-measured vibration was transmitted to the right hand, forearm and upper arm, respectively. The wrist mounted sensor appeared to accurately estimate HAV exposures measured at the hand-tool interface.

Body Motion Capture and Applications

2021 ◽  
pp. 181-223
Author(s):  
Çağlar Akman ◽  
Tolga Sönmez
Keyword(s):  
Motion Capture ◽  
Situational Awareness ◽  
Body Motion ◽  
Background Information ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Advantages And Disadvantages ◽  
Measurement Units ◽  
Popular Subject ◽  
The Mathematical Model

The motion capture (MoCap) is a highly popular subject with wide applications in different areas such as animations, situational awareness, and healthcare. An overview of MoCap utilizing different sensors and technologies is presented, and the prominent MoCap methods using inertial measurement units and optics are discussed in terms of their advantages and disadvantages. MoCap with wearable inertial measurement units is analyzed and presented specifically with the background information and methods. The chapter puts an emphasis on the mathematical model and artificial intelligence algorithms developed for the MoCap. Both the products from the important technology developers and the proof-of-concept applications conducted by Havelsan are presented within this chapter to involve an industrial perspective. MoCap system will act as a decision support system in either application by providing automatic calculation of metrics or classification, which are the basic tools for decision making.

scholarly journals Comparing Gait Trials with Greedy Template Matching

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3089 ◽  
Author(s):  
Aliénor Vienne-Jumeau ◽  
Laurent Oudre ◽  
Albane Moreau ◽  
Flavien Quijoux ◽  
Pierre-Paul Vidal ◽  
...  
Keyword(s):  
Template Matching ◽  
Neurological Diseases ◽  
Low Cost ◽  
Similarity Index ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Individual Comparison ◽  
Gait Features ◽  
Measurement Units

Gait assessment and quantification have received an increased interest in recent years. Embedded technologies and low-cost sensors can be used for the longitudinal follow-up of various populations (neurological diseases, elderly, etc.). However, the comparison of two gait trials remains a tricky question as standard gait features may prove to be insufficient in some cases. This article describes a new algorithm for comparing two gait trials recorded with inertial measurement units (IMUs). This algorithm uses a library of step templates extracted from one trial and attempts to detect similar steps in the second trial through a greedy template matching approach. The output of our method is a similarity index (SId) comprised between 0 and 1 that reflects the similarity between the patterns observed in both trials. Results on healthy and multiple sclerosis subjects show that this new comparison tool can be used for both inter-individual comparison and longitudinal follow-up.

Feasibility of a functional procedure, operator independent, for wearable inertial measurement units in gait analysis

2009 ◽  
Vol 30 ◽  
pp. S26-S27 ◽  
Author(s):  
Paolo Cappa ◽  
Eduardo Palermo ◽  
Fabrizio Patanè ◽  
Stefano Rossi ◽  
Maurizio Petrarca ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

scholarly journals Portable Gait Analysis System with an Integration of Kinect sensor and Inertial Measurement Units

2020 ◽  
Vol 9 (1) ◽  
pp. 73-78
Author(s):  
Pratima Saravanan ◽  
Jiyun Yao ◽  
Jessica Menold
Keyword(s):  
Gait Analysis ◽  
Step Length ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Kinect Sensor ◽  
Inertial Measurement Units ◽  
Combined System ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Analysis System

Clinical gait analysis is used for diagnosing, assessing, and for monitoring a patient by analyzing their kinetics, kinematics and electromyography while walking. Traditionally, gait analysis is performed in a formal laboratory environment making use of several high-resolution cameras, either video or infrared. The subject is asked to walk on a force platform or a treadmill with several markers attached to their body, allowing cameras to capture the joint coordinates across time. The space required for such a laboratory is non-trivial and often the associated costs of such an experimental setup is prohibitively expensive. The current work aims to investigate the coupled use of a Microsoft Kinect and Inertial Measurement Units as a portable and cost-efficient gait analysis system. Past studies on assessing gait using either Kinect or Inertial Measurement Units concluded that they achieve medium reliability individually due to some drawbacks related to each sensor. In this study, we propose that a combined system is efficient in detecting different phases of human gait, and the combination of sensors complement each other by overcoming the individual sensor drawbacks. Preliminary findings indicate that the IMU sensors are efficient in providing gait kinematics such as step length, stride length, velocity, cadence, etc., whereas the Kinect sensor helps in studying the gait asymmetries by comparing the right and left joint, such as hips, knees, and ankle.

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