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Can a simple approach identify complex nurse care activity?

Published: 09 September 2019 Publication History

Abstract

For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, most of the researches mainly focus on identifying simple human activities, e.g., walking, eating, and running. However, many of our daily life activities are usually more complex than those. To instigate research in complex activity recognition, the "Nurse Care Activity Recognition Challenge" [1] is initiated where six nurse activities are to be identified based on location, air pressure, motion capture, and accelerometer data. Our team, "IITDU", investigates the use of simple methods for this purpose. We first extract features from the sensor data and use one of the simplest classifiers, namely K-Nearest Neighbors (KNN). Experiment using an ensemble of KNN classifiers demonstrates that it is possible to achieve approximately 87% accuracy on 10-fold cross-validation and 66% accuracy on leave-one-subject-out cross-validation.

References

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Cited By

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  • (2024)Spatio-Temporal Transformer with Hypergraph in Nursing Activity Recognition2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781545(1-4)Online publication date: 15-Jul-2024
  • (2024)A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identificationScientific Reports10.1038/s41598-024-81228-x14:1Online publication date: 30-Dec-2024
  • (2022)Multimodal Transformer for Nursing Activity Recognition2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00224(2064-2073)Online publication date: Jun-2022
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cover image ACM Conferences
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
September 2019
1234 pages
ISBN:9781450368698
DOI:10.1145/3341162
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 ACM 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]

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Published: 09 September 2019

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Author Tags

  1. KNN
  2. accelerometer
  3. activity recognition
  4. feature extraction
  5. meditag
  6. motion capture
  7. nurse care

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

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Cited By

View all
  • (2024)Spatio-Temporal Transformer with Hypergraph in Nursing Activity Recognition2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781545(1-4)Online publication date: 15-Jul-2024
  • (2024)A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identificationScientific Reports10.1038/s41598-024-81228-x14:1Online publication date: 30-Dec-2024
  • (2022)Multimodal Transformer for Nursing Activity Recognition2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00224(2064-2073)Online publication date: Jun-2022
  • (2022)Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted FeaturesSensor- and Video-Based Activity and Behavior Computing10.1007/978-981-19-0361-8_12(193-205)Online publication date: 4-May-2022
  • (2022)Identification of Food Packaging Activity Using MoCap Sensor DataSensor- and Video-Based Activity and Behavior Computing10.1007/978-981-19-0361-8_11(181-191)Online publication date: 4-May-2022
  • (2022)Automatic Scoring of Synchronization from Fingers Motion Capture and Music BeatsImage Analysis and Processing. ICIAP 2022 Workshops10.1007/978-3-031-13321-3_21(235-245)Online publication date: 23-May-2022
  • (2021)Accelerometer based Complex Nurse Care Activity Recognition using Machine Learning ApproachAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479390(452-457)Online publication date: 21-Sep-2021
  • (2021)Nurse Care Activity Recognition: A Cost-Sensitive Ensemble Approach to Handle Imbalanced Class Problem in the WildAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479389(440-445)Online publication date: 21-Sep-2021
  • (2021)Feature-based Method for Nurse Care Complex Activity Recognition from Accelerometer SensorAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479388(446-451)Online publication date: 21-Sep-2021
  • (2021)Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based FeaturesAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479387(434-439)Online publication date: 21-Sep-2021
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