research-article

Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset

Authors:

Yasunobu Nohara,

Naoki NakashimaAuthors Info & Claims

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 1269 - 1280

https://doi.org/10.1145/2750858.2807533

Published: 07 September 2015 Publication History

Abstract

In this paper, we provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, and big data combined the 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. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by balanced classification rate. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital.

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Cited By

Dewan AGunturi VNaik V(2023)Deep Learning Models for NEAT Activity Detection on Smartwatch2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449263(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449263
Lv XYu CJin HZhang R(2022)Cost Efficient Sensor Positions Determination For Human Activity RecognitionIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.31014947:1(125-134)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2021.3101494
Frank AKubota ARiek L(2022)Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS40897.2019.8968615(449-454)Online publication date: 28-Dec-2022
https://dl.acm.org/doi/10.1109/IROS40897.2019.8968615
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Index Terms

Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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cover image ACM Conferences

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2015

1302 pages

ISBN:9781450335744

DOI:10.1145/2750858

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

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Published: 07 September 2015

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UbiComp '15

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SIGMOBILE
FX Palo Alto Laboratory, Inc.
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Rakuten Institute of Technology
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Telefónica
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UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Dewan AGunturi VNaik V(2023)Deep Learning Models for NEAT Activity Detection on Smartwatch2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449263(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449263
Lv XYu CJin HZhang R(2022)Cost Efficient Sensor Positions Determination For Human Activity RecognitionIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.31014947:1(125-134)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2021.3101494
Frank AKubota ARiek L(2022)Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS40897.2019.8968615(449-454)Online publication date: 28-Dec-2022
https://dl.acm.org/doi/10.1109/IROS40897.2019.8968615
Fujinami KNaganuma TShinoda YYamazaki KKoike S(2022)Attempts Toward Behavior Recognition of the Asian Black Bears Using an AccelerometerSensor- and Video-Based Activity and Behavior Computing10.1007/978-981-19-0361-8_4(57-79)Online publication date: 4-May-2022
https://doi.org/10.1007/978-981-19-0361-8_4
Adachi KLago PHattori YInoue S(2022)Using LUPI to Improve Complex Activity RecognitionSensor- and Video-Based Activity and Behavior Computing10.1007/978-981-19-0361-8_3(39-55)Online publication date: 4-May-2022
https://doi.org/10.1007/978-981-19-0361-8_3
Mäkela SLämsä AKeränen JLiikka JRonkainen JPeltola JHäikiö JJärvinen SBordallo López M(2021)Introducing VTT-ConIot: A Realistic Dataset for Activity Recognition of Construction Workers Using IMU DevicesSustainability10.3390/su1401022014:1(220)Online publication date: 26-Dec-2021
https://doi.org/10.3390/su14010220
Alia SAdachi KHossain TLe NKaneko HLago POkita TInoue S(2021)Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data?Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479391(428-433)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479391
Kaneko HHossain TInoue S(2021)Analysis of Feature Importances for Automatic Generation of Care RecordsAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479354(316-321)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479354
Dewan AGunturi VNaik VDutta K(2021)NEAT Activity Detection using Smartwatch at Low Sampling Frequency2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI)10.1109/SWC50871.2021.00014(25-32)Online publication date: Oct-2021
https://doi.org/10.1109/SWC50871.2021.00014
Zhao BLi SGao YLi CLi W(2020)A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity RecognitionSensors10.3390/s2023698420:23(6984)Online publication date: 7-Dec-2020
https://doi.org/10.3390/s20236984
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