Simultaneous and Spatiotemporal Detection of Different Levels of Activity in Multidimensional Data

In this work, we present a novel and promising approach to autonomously detect different levels of simultaneous and spatiotemporal activity in multidimensional data. We introduce a new multilabeling technique, which assigns different labels to different regions of interest in the data, and thus, incorporates the spatial aspect. Each label is built to describe the level of activity/motion to be monitored in the spatial location that it represents, in contrast to existing approaches providing only a binary result as the presence or absence of activity. This novel Spatially and Motion-Level Descriptive (SMLD) labeling schema is combined with a Convolutional Long Short Term Memory-based network for classification to capture different levels of activity both spatially and temporally without the use of any foreground or object detection. The proposed approach can be applied to various types of spatiotemporal data captured for completely different application domains. In this paper, it was evaluated on video data as well as respiratory sound data. Metrics commonly associated with multilabeling, namely Hamming Loss and Subset Accuracy, as well as confusion matrix-based measurements are used to evaluate performance. Promising testing results are achieved with an overall Hamming Loss for video datasets close to 0.05, Subset Accuracy close to 80% and confusion matrix-based metrics above 0.9. In addition, our proposed approach's ability in detecting frequent motion patterns based on predicted spatiotemporal activity levels is discussed. Encouraging results have been obtained on the respiratory sound dataset as well, while detecting abnormalities in different parts of the lungs. The experimental results demonstrate that the proposed approach can be applied to various types of spatiotemporal data captured for different application domains.