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

By Diane J. Cook

Defines the concept of an job version discovered from sensor information and offers key algorithms that shape the center of the field

Activity studying: researching, spotting and Predicting Human habit from Sensor Data offers an in-depth examine computational methods to task studying from sensor information. every one bankruptcy is developed to supply useful, step by step details on how you can examine and technique sensor facts. The booklet discusses recommendations for job studying that come with the following:

  • Discovering job styles that emerge from behavior-based sensor data
  • Recognizing occurrences of predefined or stumbled on actions in actual time
  • Predicting the occurrences of activities

The innovations coated may be utilized to varied fields, together with safety, telecommunications, healthcare, shrewdpermanent grids, and residential automation. a web significant other web site permits readers to test with the thoughts defined within the e-book, and to evolve or increase the thoughts for his or her personal use.

With an emphasis on computational ways, Activity studying: researching, spotting, and Predicting Human habit from Sensor Data presents graduate scholars and researchers with an algorithmic standpoint to task learning.

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

Example text

When processing sensor values that are not strictly sinusoidal signals, special attention must be paid to determine what constitutes a peak. A peak may be a value within a fixed range of the maximum value, or it may be a spike or sudden increase in values. The number of peaks in the window for different thresholds of the peak detector may also be included as a separate feature value. 14. In this case, the values at time points 3, 7, and 10 could be considered peaks. The number of peaks = 3 and time between peaks = {4, 3} could be added to our feature vector.

37. This value indicates that there is a slight, but not strong, positive correlation between the two dimensions and therefore when one changes value the other will tend to change value in the same direction. 20 • Autocorrelation. Just as correlation can be measured between two different dimensions, so also correlation can be calculated for a single dimension but displaced in time. By comparing sensor values with the values that occur at previous time steps, we can observe the amount of persistence in the data, or the tendency for the system to remain in the same state over a period of time.

These sensors were originally sewn into garments, worn as watches or placed on the body. However, many of these sensors are now also found on smart phones that are routinely carried by individuals as they perform their daily activities. Here we describe some sensors that are commonly used as wearable or carryable mechanisms for monitoring activities. Accelerometer An accelerometer is a common sensor that is worn or carried by an individual. 5). 6, this three-dimensional sensor measures acceleration along the x, y, and z axes.

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