Mobile Activity Recognition for a Whole Day: Recognizing Real Nursing Activities with Big Dataset

Reviewed, Featured
Sozo Inoue, Naonori Ueda, Yasunobu Nohara, Naoki Nakashima,
ACM Int'l Conf. Pervasive and Ubiquitous Computing (UbiComp)
(Not Available)
(Not Available)
1269-1280
2015-09-09
Osaka
http://ubicomp.org/ubicomp2015/
In this paper, we provide a real nursing sensor dataset for mobile activity recognition that can be used for supervised machine learning, and the big data combined with patient medical records and sensors attempted for 2 years, and also propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day and utilizing importance sampling and Bayesian estimation, and demonstrate data mining by applying our method to the bigger data with additional hospital data. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge. Moreover, the proposed method significantly reduces the duration errors of activity segments. We also demonstrate a data mining applying our method to bigger data in a hospital.

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