Near-fall detection is important for medical research since it can help doctors diagnose fall-related diseases and also help alert both doctors and patients of possible falls. However, in people’s daily life, there are lots of similarities between near-falls and other Activities of Daily Living (ADLs), which makes near-falls particularly difficult to detect. In order to find the subtle difference between ADLs and near-fall and accurately identify the latter, the movement of whole human body needs to be captured and displayed by a computer generated avatar. In this thesis, a wireless inertial motion capture system consisting of a central control host and ten sensor nodes is used to capture human body movements. Each of the ten sensor nodes in the system has a tri-axis accelerometer and a tri-axis gyroscope. They are attached to separate locations of a human body to record both angular and acceleration data with which body movements can be captured by applying Euler angle based algorithms, specifically, single rotation order algorithm and the optimal rotation order algorithm. According to the experiment results of capturing ten ADLs, both the single rotation order algorithm and the optimal rotation order algorithm can track normal human body movements without significantly distortion and the latter shows higher accuracy and lower data shifting. Compared to previous inertial systems with magnetometers, this system reduces hardware complexity and software computation while ensures a reasonable accuracy in capturing human body movements.

This thesis identifies and analyzes successful movement strategies for the completion of a complex dynamic task. In the past it has been shown that movement strategies correlate well to performance for simple tasks. Therefore, in this thesis I was motivated to find out if motion based metrics correlated well to performance for more complicated motor tasks. First, the Nintendo Wiimote was verified as a suitable gaming interface enabling gross human motion capture through experimental comparisons with other gaming interfaces and precision sensors. Then, a complex motor task was rendered in an open-source gaming environment. This environment enabled the design of a rhythmic task that could be controlled with the Wiimote while data were simultaneously recorded for later analysis. For the task, success and failure could be explained by high correlation between two motion based performance metrics, mean absolute jerk (MAJ) and average frequency (AVF) per trial. A logistic regression analysis revealed that each subject had a range of MAJ and AVF values for being successful, outside of which they were unsuccessful. Therefore, this thesis identifies motion based performance metrics for a novel motor control task that is significantly difficult to master and the techniques used to identify successful movement strategies can be used for predicting success for other such complex dynamic tasks.

3D animation has developed rapidly in the multimedia nowadays, in computer games, virtual reality and films. Therefore, how to make a 3D model which is really true to life, especially in the facial expressions, and can have vivid actions, is a significant issue. At the present time, the methods to construct 3D facial model are divided into two categories: one is based on computer graphic technology, like geometric function, polygon, or simple geometric shapes, the other one is using hardware to measure a real face by laser scanning system, and three-dimensional digitizer. Moreover, the method to acquire the 3D facial expression primarily are applied as following: keyframing, motion capture, and simulation. The research covers two areas: 1. Use two CCDs to digitalize the facial expressions of a real person simultaneously from both right and left side, and save the obtained standard image. Then, get the feature match points from the two standard images in the space domain, and by using the Stereo to attain the ¡§depth information¡¨ which helps to build 3D facial model. 2. Use one CCD to continuously digitalize two facial expressions and get the feature match points¡¦ coordinates in the time domain to calculate the motion vector. By combining the ¡§depth information¡¨ from space domain and the motion vector from the time domain, the 3D facial model¡¦s motion sequence can be therefore obtained. If sufficient digitalized facial expressions are processed by the 3D facial model¡¦s motion sequence, a database could be built. By matching the feature points between the 2D test image and 2D standard image in the database, the standard image¡¦s ¡§depth information¡¨ and motion vector can be used and turn the test image into 3D model which can also imitate the facial expressions of the standard images sequences. The method to match the feature points between the test image and standard images in the database can be entirely processed by computers, and as a result eliminate unnecessary human resources.

In this thesis, a system called Video Looping is developed to analyze human cyclic motions. Video Looping allows users to extract human cyclic motion from a given video sequence. This system analyzes similarities from a large amount of live footage to find the point of smooth transition. The final cyclic loop is created using only a few output images. Video Looping is useful not only to learn and understand human movements, but also to apply the cyclic loop to various artistic applications. To provide practical animation references, the output images are presented as photo plate sequences to visualize human cyclic motion similar to Eadweard Muybridge's image sequences. The final output images can be used to create experimental projects such as composited multiple video loops or small size of web animations. Furthermore, they can be imported into animation packages, and animators can create keyframe animations by tracing them in 3D software.

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Motion Capture is an essential part of a world full of digital effects in movies and games. Understanding the pipelines between software is a crucial component of this research. Methods that create the motion capture structure today are reviewed, and how they are implemented in order to create the movements that we see in modern games and movies.

I detta arbete undersöks det vilken av två animationsmetoder, handanimation eller motion capture som främst skapar animation som upplevs av åskådaren som naturlig i sitt rörelsemönster. För att besvara denna fråga har en litteraturstudie genomförts som främst fokuserar på området kroppsspråk. Tre typer av animationer har skapats dels med hjälp av motion capture-teknik och dels med handanimation. Sedan utvärderas de olika animationsklippen genom en kvalitativ undersökning där det visade sig att de animationer som skapats med hjälp av motion capture-teknik upplevdes som naturligast i sitt rörelsemönster.

This report is a contribution to an existing research project, which works on supporting motion capture actors by improving their immersion and experiences through an augmented reality setup. The problem definition is that a motion capture studio, up to now, does not provide a large scenery for an actor. A motion capture scene is made almost entirely out of props and actors wearing motion capture (mocap) suits. To set the stage and environment there is usually a director explaining what the props are and what the situation is. The rest lies in the hands of the actors to imagine the scene. This project provided the controls for viewing a virtual environment using a smartphone with an Android operating system. The result was an application containing a virtual world that the user could look and walk around in using the smartphone's gyroscope and accelerometer respectively. This shall help the actor to get a better view of the surrounding world in which he or she is supposed to act in. The phone was connected to a pico projector and both devices were mounted on the user's head to get all needed input such as turning, tilting and physical movements. The phone can also be mounted in several positions which can be changed in real time. Some user testing was made to see how users handled the devices and what they thought of the application.

Motion capture data behöver lagra på ett eller annat sätt lagra detta med databas skulle innebära väldigt många fördelar. Den används på många olika sätt och i olika branscher därmed skulle innebära en stor förändring. Det finns två olika kategorier av databaser SQL databaser och NoSQL databaser, dem databaser som kommer testas är relationsbaserade MySQL och dokumentbaserade CouchDB med en prototyp som utvecklas för att utföra dessa tester. Testerna påvisar att CouchDB är bättre databaslösningen vid lagringen av motion capture data. Men ytterligare arbete skulle kunna utföra flera tester som påvisar att läsning av motion capture data från databaserna kan ske i realtid. Mätningar från experimentet bevisar att CouchDB är den snabbare på att lagra Motion Capture data. I framtida arbete skulle arbetet kunna införas i filmbranschen och bli effektivt genom att använda mindre hårddiskutrymme och minska kostnaderna.

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative positions of Kinect v2 sensors with a calibration wand and register the sensors’ positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach to discover occluded joint data. The synchronization approach is based on the NTP protocol, which synchronizes the time between the clocks of a server and of clients dynamically, leading to the proposed system being real time. Experiments to validate the proposed system are conducted from the perspective of calibration, occlusion, and accuracy. More specifically, the mean absolute error of the calibration results is 0.73 cm, the proposed occlusion method is tested on upper and lower limbs, and the synchronization component guarantees the clock synchronization and real-time performance for more than 99% of the measurement process. Furthermore, to demonstrate the practical performance of our system, a comparison with previously developed motion capture systems (the linear trilateration approach [52] and the geometric trilateration approach [51]) with the benchmark Opti Track system is performed for the tracked joints of the head, shoulder, elbow, and wrist, therein showing that the accuracy of our proposed system is 38.3% and 24.1% better than the aforementioned two trilateration systems. Quantitative analysis is also conducted on our proposed system with the commercial inertial motion capture system Delsys smart sensor system by comparing the measurements of lower limbs (i.e., hips, knees, and ankles), and the standard deviation of our proposed system’s measurement results is 4.92 cm.