Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building.<\/p>\n
It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior.<\/p>\n
The \u2018indoor user movement<\/em>\u2018 dataset is a standard and freely available time series classification problem.<\/p>\n In this tutorial, you will discover the indoor movement prediction time series classification problem and how to engineer features and evaluate machine learning algorithms for the problem.<\/p>\n After completing this tutorial, you will know:<\/p>\n Let\u2019s get started.<\/p>\n Indoor Movement Time Series Classification with Machine Learning Algorithms This tutorial is divided into five parts; they are:<\/p>\n The \u2018indoor user movement<\/em>\u2018 prediction problem involves determining whether an individual has moved between rooms based on the change in signal strength measured by wireless detectors in the environment.<\/p>\n The dataset was collected and made available by Davide Bacciu, et al. from the University of Pisa in Italy and first described in their 2011 paper \u201cPredicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing<\/a>\u201d as a dataset for exploring a methodology that seems like recurrent neural networks called \u2018reservoir computing.\u2019<\/p>\n The problem is a special case of the more generic problem of predicting indoor user localization and movement patterns.<\/p>\n Data was collected by positioning four wireless sensors in the environment and one on the subject. The subject moved through the environment while the four wireless sensors detected and recorded a time series of sensor strength.<\/p>\n The result is a dataset comprised of variable length time series with four variates describing trajectory through a well-defined static environment, and the classification of whether the movement led to the subject changing rooms in the environment.<\/p>\n It is a challenging problem because there is no obvious and generic way to relate signal strength data to subject location in an environment.<\/p>\n The relationship between the RSS and the location of the tracked object cannot be easily formulated into an analytical model, as it strongly depends on the characteristics of the environment as well as on the wireless devices involved. I<\/p>\n<\/blockquote>\n \u2014 Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing<\/a>, 2011.<\/p>\n The data was collected under controlled experimental conditions.<\/p>\n Sensors were placed in three pairs of two connected rooms containing typical office furniture. Two sensors were placed in the corners of each of the two rooms and the subject walked one of six predefined paths through the rooms. Predictions are made at a point along each path that may or may not lead to a change of room.<\/p>\n The cartoon below makes this clear, showing the sensor locations (A1-A4), the six possible paths that may be walked, and the two points (M) where a prediction will be made.<\/p>\n Overview of two rooms, sensor locations and the 6 pre-defined paths. Three datasets were collected from the three pairs of two rooms in which the paths were walked and sensor measurements taken, referred to as Dataset 1, Dataset 2, and Dataset 3.<\/p>\n The table below, taken from the paper, summarizes the number of paths walked in each of the three datasets, the total number of room changes and non-room-changes (class label), and the lengths of the time series inputs.<\/p>\n Summary of sensor data collected from the three pairs of two rooms. Technically, the data is comprised of multivariate time series inputs and a classification output and may be described as a time series classification problem.<\/p>\n The RSS values from the four anchors are organized into sequences of varying length corresponding to trajectory measurements from the starting point until marker M. A target classification label is associated to each input sequence to indicate whether the user is about to change its location (room) or not.<\/p>\n<\/blockquote>\n\n
![\"Indoor](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2018\/09\/Indoor-Movement-Time-Series-Classification-with-Machine-Learning-Algorithms.jpg\")
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Photo by Nola Tularosa<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
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Indoor User Movement Prediction<\/h2>\n
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![\"Overview](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2018\/06\/Overview-of-two-rooms-sensor-locations-and-the-6-pre-defined-paths.png\")
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Taken from \u201cPredicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing.\u201d<\/p>\n<\/div>\n![\"Summary](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2018\/06\/Summary-of-sensor-data-collected-from-the-three-pairs-of-two-rooms.png\")
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Taken from \u201cPredicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing.\u201d<\/p>\n<\/div>\n\n