By Diane J. Cook
Defines the thought of an task version discovered from sensor information and provides key algorithms that shape the center of the field
Activity studying: getting to know, spotting and Predicting Human habit from Sensor Data offers an in-depth examine computational methods to task studying from sensor facts. every one bankruptcy is built to supply functional, step by step info on easy methods to learn and method sensor information. The e-book discusses suggestions for task studying that come with the following:
- Discovering job styles that emerge from behavior-based sensor data
- Recognizing occurrences of predefined or came across actions in genuine time
- Predicting the occurrences of activities
The innovations coated could be utilized to various fields, together with safeguard, telecommunications, healthcare, shrewdpermanent grids, and residential automation. an internet better half web site permits readers to scan with the suggestions defined within the e-book, and to conform or improve the recommendations for his or her personal use.
With an emphasis on computational techniques, Activity studying: gaining knowledge of, spotting, and Predicting Human habit from Sensor Data presents graduate scholars and researchers with an algorithmic viewpoint to task learning.
Read Online or Download Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data PDF
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Additional info for Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data
5) • Coefficient of Variation. Another indication of variability in the values is the coefficient of variation. This measure shows the relationship of the standard deviation to the mean of the set of values. 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.
While these three sensor classes may independently be weak at characterizing the Cooking activity, fusing them together leads to a stronger model. Information fusion is worthwhile for reducing uncertainty. Data gathered from sensors can be noisy and incomplete. As a result, activity learning methods that directly utilize raw sensor data are also prone to yield erroneous results. However, when different sensors and methods produce varying levels of errors and each method is reasonably accurate independently, a combination of multiple experts should reduce overall classification error and as a consequence yield more accurate outputs.
As this is for an accelerometer worn on the hip, the value may indicate many shifts between starting and stopping movement or changes in direction, both of which are common movement patterns while sweeping a floor. 4 summarizes the continuous-based sensor features that we extracted from the raw hip accelerometer data for the Sweeping activities. We next turn our attention to features commonly used in signal processing of continuous-valued data sources. • Signal Energy. Signal energy refers to the area between the signal curve and the time axis.
Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data by Diane J. Cook