Biometrics is a unique physical or behavioral characteristic of a person. This unique attribute, such as fingerprints or gait, can be used for identification or verification purposes. Gait is an emerging biometrics with great potential. Gait recognition is based on recognizing a person by the manner in which they walk. Its potential lays in that it can be captured at a distance and does not require the cooperation of the subject. This advantage makes it a very attractive tool for forensic cases and applications, where it can assist in identifying a suspect when other evidence such as DNA, fingerprints, or a face were not attainable. Gait can be used for recognition in a direct manner when the two samples are shot from similar camera resolution, position, and conditions. Yet in some cases, the only sample available is of an incomplete gait cycle, low resolution, low frame rate, a partially visible subject, or a single static image. Most of these conditions have one thing in common: static measurements. A gait signature is usually formed from a number of dynamic and static features. Static features are physical measurements of height, length, or build; while dynamic features are representations of joint rotations or trajectories. The aim of this thesis is to study the potential of predicting dynamic features from static features. In this thesis, we have created a database that utilizes a 3D laser scanner for capturing accurate shape and volumes of a person, and a motion capture system to accurately record motion data. The first analysis focused on analyzing the correlation between twenty-one 2D static features and eight dynamic features. Eleven pairs of features were regarded as significant with the criterion of a P-value less than 0.05. Other features also showed a strong correlation that indicated the potential of their predictive power. The second analysis focused on 3D static and dynamic features. Through the correlation analysis, 1196 pairs of features were found to be significantly correlated. Based on these results, a linear regression analysis was used to predict a dynamic gait signature. The predictors chosen were based on two adaptive methods that were developed in this thesis: "the top-x" method and the "mixed method". The predictions were assessed for both for their accuracy and their classification potential that would be used for gait recognition. The top results produced a 59.21% mean matching percentile. This result will act as baseline for future research in predicting a dynamic gait signature from static features. The results of this thesis bare potential for applications in biomechanics, biometrics, forensics, and 3D animation.

The use of motion capture, virtual reality and 3D animation are widely used within the entertainment sector, and simulation for training in a variety of industries. As the knowledge and technology for digital visualizations are continuously improving, the interest and areas of application are growing as well. An increasing amount of research has been conducted lately within the area of application for dance and dance pedagogy, and this study aims to investigate the possibilities that are within this area of visualizations for dance. We prepared a simple visualization prototype using recorded motion capture data of two dancers. In the next stage, participants got to discuss the prototype in an interview format. Based on the data gathered from the interviews, we could conclude some opportunities, limitations and areas of application for dance visualizations using motion capture. The conclusion was that motion capture has many opportunities to be used as a tool in dance education, given that the visualizations are done with enough level of detail This study is laying a foundation for continued research within the area, and gives an idea of the current interest among dancers who are active in dance education. / Användningen av motion capture, virtual reality och 3D animering är vida använt inom underhållningsbranschen och simulering som träning inom olika branscher. Medan kunskapen och tekniken inom digitala visualiseringar fortsätter utvecklas, så ökar även intresset och applikationsområdena. På sistone har mer forskning inom applicering på specifikt dans och danspedagogik gjorts, och den här studien fortsätter undersöka möjligheterna som finns inom området. Genom att ta fram enkla visualiseringar av dans inspelat med motion capture, användes detta som material som deltagarna i studien fick diskutera kring under en intervju. Utifrån dessa kunde slutsatser dras kring de användningsområden deltagarna såg med visualiseringarna, samt möjligheter och begränsningar. Slutsatsen var att motion capture har stora möjligheter för att fungera som ett verktyg i dansundervisning, om visualiseringarna görs tillräckligt utförliga och detaljerade. Den här studien lägger en grund för fortsatt forskning inom området och ger en bild av det nuvarande intresset bland dansare som har undervisning och pedagogik inom dans som sysselsättning.

Hand proprioception is crucial for daily activities and may be compromised by diseases or injuries,impacting patients' independence. The lack of feasible, accurate, and affordable clinical tools forhand proprioception assessment poses a significant challenge, essential for identifying dysfunctionand evaluating treatment effects.The purpose of this study was to evaluate the concurrent validity of the LeapMotion controller(LMC) for assessing hand proprioception. We compared the LMC with a 3D camera system formotion analysis (Qualisys Motion Capture, QTM), known for its high measurement accuracy as thegold standard. Twenty participants (10 men, 10 women), 15 without, and 5 with hand injury or pain,took part in this cross-sectional study. Assessments included pointing acuity with open and closedeyes using the right and left hand. There were moderate to good correlations between LMC andQTM performed with closed eyes, with intraclass correlation coefficient (ICC) values of 0.6 and0.89. Contrary, tests with open eyes showed a poor overall correlation with ICC between 0.003 and0.3. Bland-Altman analysis showed median biases of≤ 1.5 mm between LMC and QTM with eyes open, and ≤ 5.1 mm with eyes closed. Limits ofagreement ranged from -0.4 to 3.5 mm with eyes open and -31.6 to 21.5 mm with eyes closed.The results indicate that the LMC could be a cost-effective and feasible tool for quantifying handproprioception with a clinically acceptable bias. Although the median biases were small formeasurements with eyes open, the ICCs were poor. This may be due to a high pointing acuity withinthe group combined with limited variability between the participants in the eyes open tests.

Computational simulations of gait under abnormal conditions provide insights into the actions of muscles, its relationships with external reaction forces and motions of the body during slips, trips, and falls - the leading causes of occupational injuries worldwide. OpenSimTM, an open-source motion simulation software, was utilized to construct musculoskeletal structures and create dynamic simulations of body movements. Gaits of eighteen subjects were studied to extract experimentally difficult-to-obtained variables under slippery conditions. The joint angles and moments of hip, knee, ankle and the forces of four prime muscle groups were analyzed for body corrective movements during slip events. Besides, the connections between one's perception of the surrounding environment and their postural alterations to prevent falls are also discussed. Hence, this study provides a better understanding on the joint angles, moments and muscle forces of human body, evaluates the movement deviations, and contributes to the development of predictive injury thresholds during slip events.

Lower Back Disorders account for 16-19% of work related claims and 33-41% of dollars paid in workers? compensation (as cited in Marras, 1999), with impacts to society approaching $100 billion dollars annually (as cited in Marras et al., 1999). Dr. William Marras engineered a device to track trunk kinematics in order to develop a Job Risk Classification Model for predicting high-risk group probability of lower back injury. The device has been validated, but other technologies such as 3-D motion capture can potentially gather the same data. This study examined the use of motion capture to apply two- and three-plane lifting tasks to the Marras model and compare results with commonly used assessment techniques. Regardless of the fact that the Marras model results were drastically different from NIOSH and RULA, motion capture was able to gather all necessary data for running the models and has a promising future in ergonomic assessments.

This study was conducted to assess the validity and reliability of the Virtual Build methodology for ergonomics design and analysis. Thirty-six human subjects participated in this study and performed a set of six tasks. The tasks were performed twice in both real and virtual environment. The subject?s motion in performing tasks was analyzed by ergonomics assessments by using Virtual Build methodology. Criteria-related validity was evaluated by comparing the Virtual Build ergonomic assessment results with manual calculation. Test-retest reliability was evaluated by correlating ergonomics assessment results between two trials. The result shows that the Virtual Build methodology is reliable for ergonomic assessments. 48 out of 51 reliability index scores are higher than 0.8. The Virtual Build with virtual environment has lower over-time reliability performance than the real environment. The t-test shows that the Virtual Build is valid for 1991 NIOSH lifting equation assessment when using real environment. Some improvements in enhancing human perception need to be done to make Virtual Build valid when using virtual environment.

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