Generating an Event Timeline About Daily Activities From a Semantic Concept Stream

Authors

  • Taiki Miyanishi Advanced Telecommunications Research Institute International (ATR)
  • Jun-ichiro Hirayama RIKEN Center for Advanced Intelligence Project (AIP); Advanced Telecommunications Research Institute International (ATR)
  • Takuya Maekawa Graduate School of Information Science and Technology, Osaka University; Advanced Telecommunications Research Institute International (ATR)
  • Motoaki Kawanabe Advanced Telecommunications Research Institute International (ATR); RIKEN Center for Advanced Intelligence Project (AIP)

Keywords:

Activity Recognition, Event Timeline Generation, Activities of Daily Living, Semantic Concept Stream

Abstract

Recognizing activities of daily living (ADLs) in the real world is an important task for understanding everyday human life. However, even though our life events consist of chronological ADLs with the corresponding places and objects (e.g., drinking coffee in the living room after making coffee in the kitchen and walking to the living room), most existing works focus on predicting individual activity labels from sensor data. In this paper, we introduce a novel framework that produces an event timeline of ADLs in a home environment. The proposed method combines semantic concepts such as action, object, and place detected by sensors for generating stereotypical event sequences with the following three real-world properties. First, we use temporal interactions among concepts to remove objects and places unrelated to each action. Second, we use commonsense knowledge mined from a language resource to find a possible combination of concepts in the real world. Third, we use temporal variations of events to filter repetitive events, since our daily life changes over time. We use cross-place validation to evaluate our proposed method on a daily-activities dataset with manually labeled event descriptions. The empirical evaluation demonstrates that our method using real-world properties improves the performance of generating an event timeline over diverse environments.

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Published

2018-04-25

How to Cite

Miyanishi, T., Hirayama, J.- ichiro, Maekawa, T., & Kawanabe, M. (2018). Generating an Event Timeline About Daily Activities From a Semantic Concept Stream. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11308