Complex nurse care activity recognition using statistical features

Basak, Promit; Tasin, Shahamat Mustavi; Tapotee, Malisha Islam; Sheikh, Md. Mamun; Sakib, Ahsanul Hoque; Baray, Sriman Bidhan; Ahad, Md. Atiqur Rahman

doi:10.1145/3410530.3414338

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…In [8], ununiform datasets have been cleaned and resampled, class imbalances have been resolved after feature extraction, and finally, a classical machine learning algorithm has been applied to handle the noisy real-life data. A high pass and a low pass filter have been inaugurated by the authors of [7] to omit the noisy data; statistical features from time and frequency domain signals have been fed to different machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…This type of misprediction has occurred on account of having insufficient data against those activity ids. Moreover, the majority of cells of the confusion matrix filled with zero refers that activities are hardly confused [7].…”

Section: Figure 7: Normalized Confusion Matrix Of Activity Classificationmentioning

confidence: 99%

“…Furthermore, in different pandemic situations like covid-19, where diseases can spread faster than air through physical contact with the patients, HAR can immensely assist the caregivers and nurses or automated robotic systems in taking care of the patients more efficiently and ensure overall safety. But as activity recognition in healthcare is ultimately a recent study, it passes its premature state [7].…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, the datasets are full of ununiformed data, where data cleaning and different preprocessing methods are needed. That makes this kind of activity recognition more complicated and more challenging [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features

Kowshik

Pritom

Rahman

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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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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Section: Related Workmentioning

confidence: 99%

Section: Figure 7: Normalized Confusion Matrix Of Activity Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features

Kowshik

Pritom

Rahman

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

View full text Add to dashboard Cite

show abstract

“…They concluded that machine learning classifiers with the collaboration of proper feature extraction might be helpful to identify the activity with a low computational cost. P.Basak et al [3] processed the data by filtering noise, applying windowing technique on time and frequency domain to extract various features from lab and field data distinctly. After merging the data, they applied a unique cross-validation technique to find out the best-performed model using Random Forest (RF).…”

Section: Related Workmentioning

confidence: 99%

Accelerometer based Complex Nurse Care Activity Recognition using Machine Learning Approach

Tusar

Islam

Sharmin

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Researchers have been working for a long time to recognize human activities based on sensor-based data. Despite the ongoing advancements in this field, it remains challenging to recognize complex human activities for a specific domain. To bring attention to this issue, the "Third Nurse Care Activity Recognition Challenge" gathered accelerometer data from smartphones to estimate daily nurse care activities. The main challenge was handling the noisy and inconsistent dataset, which is a persistent issue in any real-life data. Also, each activity depends on both the subject and the receiver, making its recognition more complex. Our team, Team Alkaline, used high pass and low pass filters to reduce noise, adopting an overlapping windowing technique. Then we extracted features in multiple domains to derive the necessary information required for classifying the human activities more accurately. Later on, we used a feature selection method to select the most significant features. We applied Random Forest (RF) classifier for training and achieved 99.0% accuracy on the validation set.

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