Understanding Nursing Activities with Long-term Mobile Activity Recognition with Big Dataset

Author:

INOUE Sozo1,UEDA Naonori2,NOHARA Yasunobu3,NAKASHIMA Naoki3

Affiliation:

1. Kyushu Institute of Technology

2. NTT Communication Science Laboratories

3. Kyushu University Hospital

Publisher

The Institute of Systems, Control and Information Engineers

Subject

General Medicine

Reference35 articles.

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2. [2] Ricardo Chavarriaga, Hesam Sagha, Alberto Calatroni, Sundara Tejaswi Digumarti, Gerhard Troster, Jose Del R Millan, and Daniel Roggen. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, volume 34, pages 2033–2042, 2013.

3. [3] Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. Natural Language Processing (almost) from Scratch. Journal of Machine Learning Research, volume 12, pages 2493–2537, 2011.

4. [4] Miguel Damas, Ignacio Rojas, Oliver Amft, A M Toth, Oresti Baños, Héctor Pomares, and Máté Attila Tóth. A benchmark dataset to evaluate sensor displacement in activity recognition. In The 2012 ACM Conference on Ubiquitous Computing - UbiComp ’12, page 1026, 2012.

5. [5] Kris Demuynck and Tom Laureys. A comparison of different approaches to automatic speech segmentation. In Text, Speech and Dialogue, pages 277–284, 2002.

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3. Complex nurse care activity recognition using statistical features;Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers;2020-09-10

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