Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data (Hardcover)

Diane J. Cook, Narayanan C. Krishnan

Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data (Hardcover)

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商品描述

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field

Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following:

  • Discovering activity patterns that emerge from behavior-based sensor data
  • Recognizing occurrences of predefined or discovered activities in real time
  • Predicting the occurrences of activities

The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use.

With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

商品描述(中文翻譯)

「從感測器數據中學習活動模型的概念，並介紹構成該領域核心的關鍵算法」

《活動學習：從感測器數據中發現、識別和預測人類行為》提供了對從感測器數據中進行活動學習的計算方法的深入研究。每一章都提供了實用的、逐步的信息，介紹如何分析和處理感測器數據。本書討論了以下活動學習技術：

- 從基於行為的感測器數據中發現活動模式
- 實時識別預定義或發現的活動發生
- 預測活動發生的可能性

所介紹的技術可以應用於多個領域，包括安全、電信、醫療保健、智能電網和家庭自動化。在線配套網站使讀者能夠實際操作本書中描述的技術，並將其適應或增強為自己所用。

《活動學習：從感測器數據中發現、識別和預測人類行為》強調計算方法，為研究生和研究人員提供了一個從算法角度來看待活動學習的視角。」

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