From action to activity: Sensor-based activity recognition

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• 作者：Ye Liu ; ^{liuye@comp.nus.edu.sg" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Liqiang Nie ^{nieliqiang@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Li Liu ^{dcsliuli@nus.edu.sg" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; David S. Rosenblum ^{david@comp.nus.edu.sg" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Activity recognition ; Temporal pattern mining ; Sensor-generated data ; Discriminative feature extraction
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 March 2016
• 年：2016
• 卷：181
• 期：Complete
• 页码：108-115
• 全文大小：570 K

文摘

As compared to actions, activities are much more complex, but semantically they are more representative of a human׳s real life. Techniques for action recognition from sensor-generated data are mature. However, few efforts have targeted sensor-based activity recognition. In this paper, we present an efficient algorithm to identify temporal patterns among actions and utilize the identified patterns to represent activities for automated recognition. Experiments on a real-world dataset demonstrated that our approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation.