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Learning and Recognizing The Hierarc...

Cheng, Heng-Tze.

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Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities./
Author:	Cheng, Heng-Tze.
Description:	146 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
Contained By:	Dissertation Abstracts International75-05B(E).
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3578660
ISBN:	9781303727122

Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities.
Cheng, Heng-Tze.

Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities. - 146 p.

Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2013.

The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms.

ISBN: 9781303727122Subjects--Topical Terms:

1669061
Engineering, Computer.

Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities.
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020 $a 9781303727122
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100 1 $a Cheng, Heng-Tze. $3 2096257
245 1 0 $a Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
500 $a Adviser: Martin Griss.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2013.
520 $a The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms.
520 $a While having shown promising results in human activity recognition, most existing approaches using supervised or semi-supervised learning have two fundamental problems. Firstly, most existing approaches require a large set of labeled sensor data for every target class, which requires a costly effort from human annotators. Secondly, an unseen new activity cannot be recognized if no training samples of that activity are available in the dataset. In light of these problems, a new approach in this area is proposed in our research.
520 $a This thesis presents our novel approach to address the problem of human activity recognition when few or no training samples of the target activities are available. The main hypothesis is that the problem can be solved by the proposed NuActiv activity recognition framework, which consists of modeling the hierarchical and sequential structure of human activities, as well as bringing humans in the loop of model training. By injecting human knowledge about the hierarchical nature of human activities, a semantic attribute representation and a two-layer attribute-based learning approach are designed. To model the sequential structure, a probabilistic graphical model is further proposed to take into account the temporal dependency of activities and attributes. Finally, an active learning algorithm is developed to reinforce the recognition accuracy using minimal user feedback.
520 $a The hypothesis and approaches presented in this thesis are validated by two case studies and real-world experiments on exercise activities and daily life activities. Experimental results show that the NuActiv framework can effectively recognize unseen new activities even without any training data, with up to 70-80% precision and recall rate. It also outperforms supervised learning with limited labeled data for the new classes. The results significantly advance the state of the art in human activity recognition, and represent a promising step towards bridging the gap between computers and humans.
590 $a School code: 0041.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0464
690 $a 0984
690 $a 0544
710 2 $a Carnegie Mellon University. $3 1018096
773 0 $t Dissertation Abstracts International $g 75-05B(E).
790 $a 0041
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3578660