Visual analysis of body movement in serious games for healthcare

Abstract

The advancement of motion sensing input devices has enabled the collection of multivariate time-series body movement data. Analyzing such type of data is challenging due to the large amount of data and the task of mining for interesting temporal movement patterns. To address this problem, we propose an interface to visualize and analyze body movement data. This visualization enables users to navigate and explore the evolution of movement over time for different movement areas. We also propose a clustering method based on hierarchical clustering to group similar movement patterns. The proposed visualization is illustrated with a case study which demonstrates the ability of the interface to analyze body movements.

KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis

“…A similar approach, the 'Mo-tionFlow' system [19], allows more specific grouping and analysis of patterns in motion sequences. Another system was described by Purwantiningsih et al [20]. They collected data on patients' quality of movement using serious games and different motion sensing devices.…”

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

“…From a data mining perspective, Aghabozorgi and Shirkhorshidi [12] state that Euclidean Distance and DTW are the most popular distance measures in time series data; however, Euclidian Distance is the most widely used distance measure in the surveyed visual analytics papers e.g. [34], [43], [44], [50], [52], [53], [56], [57], [59], [60], [66], [67], [69], [70], [75], [81], [85], [88]- [91], [93], [95], [96], [113]- [115] as it is the most straightforward distance measure compared to others. DTW has only been used in [48], [53], [56], [79] to calculate the similarity of time series data, and papers [34], [35], [61], [72], [83], [85], [86] use correlation and cross-correlation in their works.…”

“…Different visualization techniques (ripple, stacked, river, stream) and interaction techniques (zoom, pan, select) allow the user to select the time duration and obtain visual feedback. Interaction with the linked view will highlight regions in the time series and any pattern recognition techniques will highlight data in the time series, helping to understand and analyze data over time [38], [48], [59], [86], [90], [93]. With stacked, river, and stream graphs, each item is displayed as a colored current whose height changes continuously as it flows through time.…”

“…Similarity measures include Euclidean User interaction is principally through the parallel coordinate plot to isolate the overlapping manifolds in the data [228]. [34], [43], [44], [50], [52], [53], [57], [59], [60], [66], [67], [69], [75], [85], [88]- [91], [93], [95], [113], DTW [48], [53], MDS [36], [48], [54], Pearson, Fisher Discriminant [88], cross-correlation [83], etc. Through the SOM algorithm, as model-based clustering, the SOM grid provides trajectory bundle visualization of locations underlying prototype patterns, allowing experts to visually analyze sets of trajectory data [45] or a search interface, meaning that analysts can control clustering [91].…”

MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

“…This difference makes streamgraphs more readable and natural than ThemeRiver flows. Streamgraphs are used in some domains such as body movement [16] or data stream visualization [17].…”