By Diane J. Cook

Defines the inspiration of an task version discovered from sensor info and provides key algorithms that shape the middle of the field

Activity studying: learning, spotting and Predicting Human habit from Sensor Data offers an in-depth examine computational techniques to task studying from sensor info. each one bankruptcy is developed to supply functional, step by step info on the way to research and approach sensor information. The e-book discusses options for task studying that come with the following:

  • Discovering task styles that emerge from behavior-based sensor data
  • Recognizing occurrences of predefined or found actions in genuine time
  • Predicting the occurrences of activities

The ideas lined will be utilized to various fields, together with safety, telecommunications, healthcare, clever grids, and residential automation. an internet spouse web site allows readers to test with the concepts defined within the booklet, and to evolve or improve the thoughts for his or her personal use.

With an emphasis on computational methods, Activity studying: getting to know, spotting, and Predicting Human habit from Sensor Data presents graduate scholars and researchers with an algorithmic point of view to job learning.

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Additional resources for Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data

Example text

14 that the data are not concentrated around the mean, but have a rather flat distribution. 48, which is consistent with this observation. 10) • Correlation. The amount of correlation that exists between multiple sensors or between the dimensions of a multidimensional sensor, such as a multiple-axis accelerometer, can provide important insights on the type of activity that is being monitored. For example, an accelerometer on a hand may see similar acceleration along all three dimensions if the individual is swimming, but may see little or no acceleration for one axis while the others axes do indicate acceleration for actions such as wiping a table surface.

2 12 770 Milliseconds Duration 46,230,739 16,712 Discrete Event Features In some settings, sensors report activity behavior as a well-defined event using a string-based message rather than a numeric value. Many of these sensors also report messages only when events are detected, rather than sampling the state of the sensor at near-constant time intervals. As an example, motion sensors may report a message of “ON” when movement is detected in their field of view, and report “OFF” when movement ceases to be detected.

This value is typically only computed for features that do not have negative values, as it may not have useful meaning for features with negative values. 6) • Zero Crossings. Zero crossings are typically calculated only for time series data with a mean of 0. However, zero crossings can be computed for an arbitrary sequence of values to represent the number of times the signal crosses its median. , distinguishing Walking from Running). 00), so the number of zero crossings is 5. 7) • Percentiles, Interquartile Range.

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Activity Learning: Discovering, Recognizing, and Predicting by Diane J. Cook
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