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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