Visual analysis and quantitative assessment of human movement

Our ability to navigate in our environment depends on the condition of the musculoskeletal and nervous systems. Any deterioration of a component of these two systems can cause instability or disability of body movements. Such deterioration can happen as a consequence of natural age-related changes, injuries and/or diseases. The ability to objectively and quantitatively assess different functional tasks such as postural control, gait or hand movements can be useful for preventing falls, following disease progression, assessing the effectiveness of medical care and interventions, and ultimately improving the accuracy of clinical decisions. The benefits are clear. However, current metrics, algorithms and tools are not enough to analyze and understand the infinite complexity of human movements. In this thesis, I developed visualizations and a novel method to assess human movement in real-time using data collected from tracking devices such as Kinect and inertial measurement units. This method was used to assess balance performance on data from exergames, digital games controlled by body movements, and to classify young and older adults achieving more than 85% accuracy. This kind of assessment can also be used to provide meaningful feedback and to automatically adapt the difficulty of exergames, which in turn could increase motivation to play and improve balance control among older adults. Additionally, the method was used to classify healthy participants and patients with a coordination disorder during a hand movement task achieving 84% accuracy. In conclusion, this thesis presents a promising method that can be used for assessing and understanding human movement

Runtime Prediction of Filter Unsupervised Feature Selection Methods

In recent years, the speed and quality of data analysis have been hindered by an increase in data size, an increase in data dimensionality, and the expensive task of data labeling. Much research has been conducted in the field of Unsupervised Feature Selection (UFS) to counteract this hindrance. Specifically, filter UFS methods are popular due to their simplicity and efficiency in counteracting performance problems in unlabeled data analysis. However, this popularity resulted in a great variety of filter UFS methods, each with their own advantages and disadvantages, making it hard to choose an appropriate method for a particular problem. Unfortunately, an inappropriate method choice can lead toa decrease in research or project quality, and it can render data analysis unfeasible due to time constraints. Importantly, terminating a method’sanalysis before completion means in most cases that no partial results areobtained either. Previous works on the evaluation of filter UFS methodsfocused mainly on assessing clustering and classification performance. Although very useful, choosing an appropriate method often requires knowledge about the method’s runtime as well. In this paper, we study the runtimes of six popular filter UFS methods using synthetic and real-world datasets. Runtime prediction models were trained on 114 synthetic datasets and tested on 29 real-world datasets. The models showed good performance on four out of the six methods. Finally, we present general runtime guidelines for each method. To the best of our knowledge, this is the first paper that investigates methods’ runtimes in this fashion