By Diane J. Cook
Defines the concept of an task version discovered from sensor information and offers key algorithms that shape the center of the field
Activity studying: getting to know, spotting and Predicting Human habit from Sensor Data presents an in-depth examine computational techniques to task studying from sensor info. each one bankruptcy is built to supply functional, step by step details on how you can learn and approach sensor info. The ebook discusses ideas for task studying that come with the following:
- Discovering task styles that emerge from behavior-based sensor data
- Recognizing occurrences of predefined or chanced on actions in actual time
- Predicting the occurrences of activities
The suggestions lined may be utilized to various fields, together with safety, telecommunications, healthcare, shrewdpermanent grids, and residential automation. an internet better half web site permits readers to scan with the suggestions defined within the publication, and to evolve or improve the strategies for his or her personal use.
With an emphasis on computational techniques, Activity studying: learning, spotting, and Predicting Human habit from Sensor Data offers graduate scholars and researchers with an algorithmic point of view to job learning.
Read 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
A normal distribution, for example, has a kurtosis of 3, while a uniform distribution has a kurtosis close to 0. In contrast, distributions with low kurtosis have a flat top near the mean. 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.
00 • Square Sum of Percentile Observations. Once the percentiles are defined, the square sum of observations that fall below each percentile (or alternatively, above the percentile) can be reported as a separate feature. 8 The corresponding square sum of observations for our example is: SqSumPt(S, 20) = 1, 909, 090, SqSumPt(S, 50) = 4, 913, 656, SqSumPt(S, 80) = 8, 272, 047 • Binned Distribution. This measure represents the fraction of values that fall within equally-spaced bins that span the entire range of sensor values.
Additionally, the Kalman filter does not incur heavy computational expense. Kalman filtering assumes that the sensors can be modeled as a linear system and sensor noise can be modeled as a Gaussian distribution. The algorithm operates using two steps. In the first step, referred to as the prediction step, the Kalman filter estimates the current sensor values along with their uncertainties. Once the actual sensor values are observed, the fused estimate is obtained by the weighted average of the sensor values, with more weights given to the values with higher certainty.
Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data by Diane J. Cook