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View all- Fujinami KTakuno RSato IShimmura T(2023)Evaluating Behavior Recognition Pipeline of Laying Hens Using Wearable Inertial SensorsSensors10.3390/s2311507723:11(5077)Online publication date: 25-May-2023
Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features
Authors: 
M. Ashikuzzaman Kowshik, 
Yeasin Arafat Pritom, Md.Sohanur Rahman, Ali Akbar, Md Atiqur Rahman AhadAuthors Info & Claims
Pages 434 - 439
Abstract
Nurse care activity recognition is an emerging segment in healthcare automation systems based on physical movement recognition applying machine learning techniques using various sensor-based datasets. In this paper, different machine learning models have been used to recognize the activities. However, before that, our user dataset has been preprocessed using data cleaning, resampling, data labeling, windowing, and filtering techniques in order to handle the ununiform data. Various analytical features have been extracted using Fast Fourier Transformation, Power Spectral Density, and Discrete Wavelet Transformation. After that, the best combinational features have been selected from the extracted features, and class imbalance has been mitigated before applying the conventional machine learning models. After applying all methodology, 87.00% accuracy has been obtained using the Light Gradient Boosting Machine Classifier.
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September 2021
711 pages
ISBN:9781450384612
DOI:10.1145/3460418
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Published: 24 September 2021
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UbiComp '21
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UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing
September 21 - 26, 2021
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View all- Fujinami KTakuno RSato IShimmura T(2023)Evaluating Behavior Recognition Pipeline of Laying Hens Using Wearable Inertial SensorsSensors10.3390/s2311507723:11(5077)Online publication date: 25-May-2023
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