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Wearable Computing: Accelerometer-Ba...

Li, Chong.

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Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree./
作者:	Li, Chong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	60 p.
附註:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10270475
ISBN:	9781369708929

Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree.
Li, Chong.

Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 60 p.

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--Utah State University, 2017.

This study focused on the use of wearable sensors in human activity recognition and proposes an accelerometer-based real-time human activity recognition approach using the decision tree as the classifier. We aimed to create an approach that requires only one accelerometer to be worn on the user's wrist and recognizes activities in real-time based on the acceleration data. The decision tree was adopted as the classification algorithm and a classifier simplification technique and a novel decision tree storage structure were designed. Feature selection and tree pruning were applied to reduce the decision tree complexity. With this approach, the designed system has fairly low computational cost and consumes small memory space, and therefore can be easily implemented to a wristband or smart watch that has an embedded accelerometer.

ISBN: 9781369708929Subjects--Topical Terms:

523869
Computer science.

Wearable Computing: Accelerometer-Based Human Activity Classification Using Decision Tree.
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520 $a This study focused on the use of wearable sensors in human activity recognition and proposes an accelerometer-based real-time human activity recognition approach using the decision tree as the classifier. We aimed to create an approach that requires only one accelerometer to be worn on the user's wrist and recognizes activities in real-time based on the acceleration data. The decision tree was adopted as the classification algorithm and a classifier simplification technique and a novel decision tree storage structure were designed. Feature selection and tree pruning were applied to reduce the decision tree complexity. With this approach, the designed system has fairly low computational cost and consumes small memory space, and therefore can be easily implemented to a wristband or smart watch that has an embedded accelerometer.
520 $a The proposed approach follows a process of feature extraction, feature selection, decision tree training, and decision tree pruning. We categorized human daily activities into three activity states, including stationary, walking, and running. Through experiments, the effects of feature extraction window length, feature discretization intervals, feature selection, and decision tree pruning were tested. On top of this process, we also implemented misclassification correction and decision tree simplification to improve classification performance and reduce classifier implementation size. The experimental results showed that based on the particular set of data we collected, the combination of 2-second window length and 8 intervals yielded the best decision tree performance. The feature selection process reduced the number of features from 37 to 7, and increased the classification accuracy by 1.04%. The decision tree pruning slightly decreased the classification performance, while significantly reducing the tree size by around 80%. The proposed misclassification mechanism effectively eliminated single misclassifications caused by interruptive activities. In addition, with the proposed decision tree simplification approach, the trained decision tree could be saved to three arrays. The implemented decision tree could be initiated simply by reading configurations from the three arrays.
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