The marker M is the same for all the movements, therefore different paths cannot be distinguished based only on the RSS values collected at M.Įach input file in the provided dataset contains data pertaining to one temporal sequence of input RSS data (1 user trajectory for each file). Each path produces a trace of RSS measurements from the beginning of the trajectory until a marker point, which is denoted as M in the Figure. The Figure also shows a simplified illustration of the types of user trajectories considered, with straight paths yielding to a spatial context change and curved ones leading to spatial context preservation. In each environmental setting, the anchors are deployed in fixed positions near the rooms corners (at the height of 1.5 m from the ground), while the mobile is worn on the chest of the user. A sketch of the common setup considered is provided in the attached Figure. The measurement campaign involved a number of 3 different environmental settings, each of which comprises 2 rooms (containing typical office furniture) separated by a corridor. In particular, the target class +1 is associated to the location changing movements, while the target class -1 is associated to the location preserving movements.
![rss series rss series](https://data2.manualslib.com/product_thumbs/32/156/15512/1551159_rss_series_product.jpg)
Target data consists in a class label indicating whether the user's trajectory will lead to a change in the spatial context (i.e. In the provided dataset, the RSS signals have been rescaled to the interval, singly on the set of traces collected from each anchor (as in ). Data has been collected during user movements at the frequency of 8 Hz (8 samples per second). Input data contains temporal streams of radio signal strength (RSS) measured between the nodes of a WSN, comprising 5 sensors: 4 anchors deployed in the environment and 1 mote worn by the user. The binary classification task consists in predicting the pattern of user movements in real-world office environments from time-series generated by a Wireless Sensor Network (WSN). This dataset represents a real-life benchmark in the area of Ambient Assisted Living applications, as described in. Paolo Barsocchi: paolo.barsocchi Ĭlaudio Gallicchio: gallicch di. (b) Institute of Information Science and Technologies, Italian National Research Council.
![rss series rss series](https://economictimes.indiatimes.com/thumb/msid-65782204,width-1600,height-900,resizemode-4/news/politics-and-nation/delhi-rss-invites-leaders-across-political-spectrum-for-three-day-lecture-series.jpg)
Largo Bruno Pontecorvo 3, 56127 Pisa, Italy (a) Department of Computer Science, University of Pisa. The task is intended as real-life benchmark in the area of Ambient Assisted Living.ĭavide Bacciu (a), Paolo Barsocchi (b), Stefano Chessa (a), Claudio Gallicchio (a), Alessio Micheli (a) Indoor User Movement Prediction from RSS data Data Setĭownload: Data Folder, Data Set DescriptionĪbstract: This dataset contains temporal data from a Wireless Sensor Network deployed in real-world office environments. Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns.