skip to main content
research-article

Integrating Activity Recognition and Nursing Care Records: The System, Deployment, and a Verification Study

Published: 09 September 2019 Publication History

Abstract

In this paper, we introduce a system of integrating activity recognition and collecting nursing care records at nursing care facilities as well as activity labels and sensors through smartphones, and describe experiments at a nursing care facility for 4 months. A system designed to be used even by staff not familiar with smartphones could collected enough number of data without losing but improving their workload for recording. For collected data, we revealed the nature of the collected data as for activities, care details, and timestamps, and considering them, we show a reference accuracy of recognition of nursing activity which is durable to time skewness, overlaps, and class imbalances. Moreover, we demonstrate the near future prediction to predict the next day's activities from the previous day's records which could be useful for proactive care management. The dataset collected is to be opened to the research community, and can be the utilized for activity recognition and data mining in care facilities.

References

[1]
G. Bahle, A. Gruenerbl, P. Lukowicz, E. Bignotti, M. Zeni, and F. Giunchiglia. 2014. Recognizing hospital care activities with a coat pocket worn smartphone. In 6th International Conference on Mobile Computing, Applications and Services. 175--181.
[2]
U. Blanke, B. Schiele, M. Kreil, P. Lukowicz, B. Sick, and T. Gruber. 2010. All for one or one for all? Combining heterogeneous features for activity spotting. In 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 18--24.
[3]
Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014. A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. ACM Comput. Surv. 46, 3, Article 33 (Jan. 2014), 33 pages.
[4]
Larry Chan, Vedant Das Swain, Christina Kelley, Kaya de Barbaro, Gregory D. Abowd, and Lauren Wilcox. 2018. Students' Experiences with Ecological Momentary Assessment Tools to Report on Emotional Well-being. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 3 (March 2018), 20 pages.
[5]
Yung-Ju Chang, Gaurav Paruthi, and Mark W. Newman. 2015. A Field Study Comparing Approaches to Collecting Annotated Activity Data in Real-world Settings. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). ACM, New York, NY, USA, 671--682.
[6]
Basit Chaudhry, Jerome Wang, Shinyi Wu, Margaret Maglione, Walter Mojica, Elizabeth Roth, Sally C Morton, and Paul G Shekelle. 2006. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of internal medicine 144, 10 (2006), 742--752.
[7]
Pierre Geurts, Damien Ernst, and Louis Wehenkel. 2006. Extremely randomized trees. Machine learning 63, 1 (2006), 3--42.
[8]
Ann L. Hendrich, Marilyn Chow, Boguslaw A Skierczynski, and Zhenqiang Lu. 2008. A 36-hospital time and motion study: how do medical-surgical nurses spend their time? The Permanente journal 12 3 (2008), 25--34.
[9]
Sozo Inoue and Xincheng Pan. 2016. Supervised and Unsupervised Transfer Learning for Activity Recognition from Simple In-home Sensors. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2016). ACM, New York, NY, USA, 20--27.
[10]
Sozo Inoue, Naonori Ueda, Yasunobu Nohara, and Naoki Nakashima. 2015. Mobile Activity Recognition for a Whole Day: Recognizing Real Nursing Activities with Big Dataset. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). ACM, New York, NY, USA, 1269--1280.
[11]
Ashish Kapoor and Eric Horvitz. 2008. Experience Sampling for Building Predictive User Models: A Comparative Study. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08). ACM, New York, NY, USA, 657--666.
[12]
Narayanan C. Krishnan and Diane J. Cook. 2014. Activity recognition on streaming sensor data. Pervasive and Mobile Computing 10 (2014), 138--154.
[13]
Paula Lago, Sayeda Shamma Alia, Shingo Takeda, Tittaya Mairittha, Nattaya Mairittha, Farina Faiz, Yusuke Nishimura, Kohei Adachi, Tsuyoshi Okita, FranÃğois Charpillet, and Sozo Inoue. 2019. Nurse Care Activity Recognition Challenge: Summary and Results. In Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA). 6 pages.
[14]
Paula Lago, Fréderic Lang, Claudia Roncancio, Claudia Jiménez-Guarín, Radu Mateescu, and Nicolas Bonnefond. 2017. The ContextAct@A4H Real-Life Dataset of Daily-Living Activities. In Modeling and Using Context, Patrick Brézillon, Roy Turner, and Carlo Penco (Eds.). Springer International Publishing, Cham, 175--188.
[15]
O. D. Lara and M. A. Labrador. 2013. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys Tutorials 15, 3 (Third 2013), 1192--1209.
[16]
Jonathan Lester, Tanzeem Choudhury, and Gaetano Borriello. 2006. A Practical Approach to Recognizing Physical Activities. In Pervasive Computing, Kenneth P. Fishkin, Bernt Schiele, Paddy Nixon, and Aaron Quigley (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1--16.
[17]
F. Naya, R. Ohmura, F. Takayanagi, H. Noma, and K. Kogure. 2006. Workers' Routine Activity Recognition using Body Movements and Location Information. In 2006 10th IEEE International Symposium on Wearable Computers. 105--108.
[18]
Marilyn Rantz, Marjorie Skubic, Greg Alexander, Myra Aud, Bonnie Wakefield, Colleen Galambos, Richelle Koopman, and Steven Miller. 2010. Improving Nurse Care Coordination With Technology. Computers, Informatics, Nursing 28, 6 (November-December 2010), 325--332.
[19]
Marilyn J Rantz, Marjorie Skubic, Steven J Miller, Colleen Galambos, Greg Alexander, James Keller, and Mihail Popescu. 2013. Sensor technology to support aging in place. Journal of the American Medical Directors Association 14, 6 (2013), 386--391.
[20]
D. Roggen, K. Forster, A. Calatroni, T. Holleczek, Y. Fang, G. Troster, A. Ferscha, C. Holzmann, A. Riener, P. Lukowicz, G. Pirkl, D. Bannach, K. Kunze, R. Chavarriaga, and J. d. R. Millan. 2009. OPPORTUNITY: Towards opportunistic activity and context recognition systems. In 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks Workshops. 1--6.
[21]
Valeria Soto-Mendoza, JosÃľ-Antonio GarcÃŋa-MacÃŋas, Edgar ChÃąvez, Anna Isabel MartÃŋnez-GarcÃŋa, Jesus Favela, Patricia Serrano-Alvarado, and MaythÃľ R. ZÃžÃśiga-Rojas. 2015. Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders. ACM Transactions on Intelligent Systems and Technology 6 (06 2015).
[22]
Maja Stikic and Bernt Schiele. 2009. Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning. In Location and Context Awareness, Tanzeem Choudhury, Aaron Quigley, Thomas Strang, and Koji Suginuma (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 156--173.
[23]
Emmanuel Munguia Tapia, Stephen S Intille, and Kent Larson. 2004. Activity recognition in the home using simple and ubiquitous sensors. In International conference on pervasive computing. Springer, 158--175.
[24]
Takamichi Toda, Sozo Inoue, and Naonori Ueda. 2016. Mobile Activity Recognition Through Training Labels with Inaccurate Activity Segments. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2016). ACM, New York, NY, USA, 57--64.
[25]
K. Van Laerhoven, D. Kilian, and B. Schiele. 2008. Using rhythm awareness in long-term activity recognition. In 2008 12th IEEE International Symposium on Wearable Computers. 63--66.
[26]
Robert-Andrei Voicu, Ciprian Dobre, Lidia Bajenaru, and Radu-Ioan Ciobanu. 2019. Human Physical Activity Recognition Using Smartphone Sensors. Sensors 19, 3 (2019), 458.
[27]
A. Wang, G. Chen, J. Yang, S. Zhao, and C. Chang. 2016. A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone. IEEE Sensors Journal 16, 11 (June 2016), 4566--4578.

Cited By

View all
  • (2024)Complementary Event Motion Classification for Robust Identification and Care Work Identification Using Hidden Semi-Markov Modelロバストな作業識別のための余事象動作分類と隠れセミマルコフモデルを用いた介護作業識別Transactions of the Society of Instrument and Control Engineers10.9746/sicetr.60.62060:12(620-630)Online publication date: 2024
  • (2024)Exploring the Impact of the NULL Class on In-the-Wild Human Activity RecognitionSensors10.3390/s2412389824:12(3898)Online publication date: 16-Jun-2024
  • (2024)Temporal Action Localization for Inertial-based Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997708:4(1-19)Online publication date: 21-Nov-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 3, Issue 3
September 2019
1415 pages
EISSN:2474-9567
DOI:10.1145/3361560
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2019
Published in IMWUT Volume 3, Issue 3

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. datasets
  2. gaze detection
  3. neural networks
  4. text tagging

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)70
  • Downloads (Last 6 weeks)6
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Complementary Event Motion Classification for Robust Identification and Care Work Identification Using Hidden Semi-Markov Modelロバストな作業識別のための余事象動作分類と隠れセミマルコフモデルを用いた介護作業識別Transactions of the Society of Instrument and Control Engineers10.9746/sicetr.60.62060:12(620-630)Online publication date: 2024
  • (2024)Exploring the Impact of the NULL Class on In-the-Wild Human Activity RecognitionSensors10.3390/s2412389824:12(3898)Online publication date: 16-Jun-2024
  • (2024)Temporal Action Localization for Inertial-based Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997708:4(1-19)Online publication date: 21-Nov-2024
  • (2024)Enhancing Inertial Hand based HAR through Joint Representation of Language, Pose and Synthetic IMUsProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676609(25-31)Online publication date: 5-Oct-2024
  • (2024)Preliminary Investigation of Activity Prediction in Nursing Homes using Activity History with Erroneous Time StampsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678384(851-855)Online publication date: 5-Oct-2024
  • (2024)Brief Introduction of the OpenPack Dataset and Lessons Learned from Organizing Activity Recognition Challenge Using the DatasetCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3677597(116-120)Online publication date: 5-Oct-2024
  • (2024)CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised PretrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595978:2(1-26)Online publication date: 15-May-2024
  • (2024)A Step Toward Better Care: Understanding What Caregivers and Residents in Assisted Living Facilities Value in Health Monitoring SystemsProceedings of the ACM on Human-Computer Interaction10.1145/36372908:CSCW1(1-29)Online publication date: 26-Apr-2024
  • (2024)Reenvisioning Patient Education with Smart Hospital Patient RoomsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314197:4(1-23)Online publication date: 12-Jan-2024
  • (2024)OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494448(90-97)Online publication date: 11-Mar-2024
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media