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

By Diane J. Cook

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

Activity studying: studying, spotting and Predicting Human habit from Sensor Data presents an in-depth examine computational techniques to job studying from sensor information. each one bankruptcy is developed to supply sensible, step by step details on find out how to study and technique sensor information. The e-book discusses strategies for task 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 genuine time
  • Predicting the occurrences of activities

The concepts lined should be utilized to varied fields, together with safety, telecommunications, healthcare, clever grids, and residential automation. an internet better half website allows readers to scan with the suggestions defined within the booklet, and to evolve or increase the ideas for his or her personal use.

With an emphasis on computational ways, Activity studying: studying, spotting, and Predicting Human habit from Sensor Data offers graduate scholars and researchers with an algorithmic viewpoint to job learning.

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

Sample text

4 shows how RFID codes can be “hidden” under a visible object barcode. The RFID reader, or interrogator, queries a tag. A passive tag does not require a power source but collects energy from the interrogating electromagnetic (EM) field and then acts as a passive transponder to modulate the signal. The reader can interpret the signal to determine the tags that are in its area of coverage. 4 15 RFID tag positioned beneath visible object barcode. track the presence of objects or humans that are wearing a tag and are in close proximity to a reader.

1 Sensors in the Environment Some sensors that monitor activities are not affixed to the individuals performing the activity but are placed in the environment surrounding the individual. These sensors are valuable in passively providing readings without requiring individuals to comply with rules regarding wearing or carrying sensors in prescribed manners. Because they are not customized for each person, environment sensors can monitor activities for a group of individuals but may have difficulty separating movements or actions among individuals that are part of that group.

Additional sensors included in these datasets are A001 (gas usage sensor attached to burner), A002 and A003 (hot and cold water consumption sensors, respectively), and P001 (whole-home power usage meter). 8 Positioning of wearable accelerometers on an individual’s dominant arm (left) and hip (right). 9 Time plot of sensor activity during the Hand Washing activity. The x axis shows the time of day when the sensor message was generated. The y axis shows the identifier (on the left) and functional location (on the right) for each sensor generating messages.

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