Identifying car ingress movement strategies before and after total knee replacement

Background: Post-operative performance of knee bearings is typically assessed in activities of daily living by means of motion capture. Biomechanical studies predominantly explore common tasks such as walking, standing and stair climbing, while overlooking equally demanding activities such as embarking a vehicle. Aims: The aim of this work is to evaluate changes in the movement habits of patients after total knee arthroplasty surgery in comparison to healthy age-matched control participants. Methods: A mock-up car was fabricated based on the architecture of a common vehicle. Ten control participants and 10 patients with severe osteoarthritis of the knee attended a single- and three-motion capture session(s), respectively. Participants were asked to enter the car and sit comfortably adopting a driving position. Three trials per session were used for the identification of movement strategies by means of hierarchical clustering. Task completion time was also measured. Results: Patients’ movement behaviour didn’t change significantly following total knee arthroplasty surgery. Control participants favoured different movement strategies compared to patients post-operatively. Group membership, height and sidedness of the affected joint were found to be non-significant in task completion time. Conclusion: This study describes an alternative movement identification technique for the analysis of the ingress movement that may be used to clinically assess knee bearings and aid in movement simulations and vehicle design

Model-based 3D gait biometrics

There have as yet been few gait biometrics approaches which use temporal 3D data. Clearly, 3D gait data conveys more information than 2D data and it is also the natural representation of human gait perceived by human. In this paper we explore the potential of using model-based methods in a 3D volumetric (voxel) gait dataset. We use a structural model including articulated cylinders with 3D Degrees of Freedom (DoF) at each joint to model the human lower legs. We develop a simple yet effective model-fitting algorithm using this gait model, correlation filter and a dynamic programming approach. Human gait kinematics trajectories are then extracted by fitting the gait model into the gait data. At each frame we generate a correlation energy map between the gait model and the data. Dynamic programming is used to extract the gait kinematics trajectories by selecting the most likely path in the whole sequence. We are successfully able to extract both gait structural and dynamics features. Some of the features extracted here are inherently unique to 3D data. Analysis on a database of 46 subjects each with 4 sample sequences, shows an encouraging correct classification rate and suggests that 3D features can contribute even more

Features Mapping Based Human Gait Recognition

Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency. In this firstly binary silhouette of a walking person is detected from each frame of an image. Then secondly, the features from each frame are extracted using the image processing operation. In the end SVM, K-MEANS and LDA are used for training and testing purpose. Every experiment and test is done on CASIA database. The results in this paper are better and improved from previous results by using SVM , K MEANS. DOI: 10.17762/ijritcc2321-8169.15067

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Quantifying foot placement variability and dynamic stability of movement to assess control mechanisms during forward and lateral running

Research has indicated that human walking is more unstable in the secondary, rather than primary plane of progression. However, the mechanisms of controlling dynamic stability in different planes of progression during running remain unknown. The aim of this study was to compare variability (standard deviation and coefficient of variation) and dynamic stability (sample entropy and local divergence exponent) in anterior–posterior and medio-lateral directions in forward and lateral running patterns. For this purpose, fifteen healthy, male participants ran in a forward and lateral direction on a treadmill at their preferred running speeds. Coordinate data of passive reflective markers attached to body segments were recorded using a motion capture system. Results indicated that: (1) there is lower dynamic stability in the primary plane of progression during both forward and lateral running suggesting that, unlike walking, greater control might be required to regulate dynamic stability in the primary plane of progression during running, (2) as in walking, the control of stability in anterior–posterior and medio-lateral directions of running is dependent on the direction of progression, and (3), quantifying magnitude of variability might not be sufficient to understand control mechanisms in human movement and directly measuring dynamic stability could be an appropriate alternative

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

A review of vision-based gait recognition methods for human identification

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field