Tourism in mountain regions has increased during the last decades. Tourists are attracted to the Dolomites, a mountain range located in northeastern Italy, mainly for outdoor activities. The GEOPARC Bletterbach, is one of the Dolomites UNESCO parks, that receives 60.000 tourist visits every year, that are attracted by the geological formations and spectacular landscapes. Unfortunately, the visit to the park is not without risk, as tourists expose themselves to a complex mountain-gorge terrain with the sudden occurrence of floods, rock and tree falls which may be led to dangerous incidents. Here, we propose the use of wearable Long-Range transceivers, such as ground sensors, with the support of proximal sensing techniques to monitor and track tourism activities.

We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighborhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.