Nurse care activity recognition is a recent but demanding study topic in human activity recognition (HAR) since it has high class imbalance and intra-class variability problem depending on both the patient and the receiver. Although traditional imbalance learning approaches are offered to address this issue, they have several limitations: 1) important information is lost while using undersampling approaches, and 2) oversampling methods shifts the class distribution, exposing the model to overfitting and over-optimism. To tackle this problem, we present a framework for a cost-sensitive hybrid optimum ensemble classifier. Our technique modified the class weights to impose higher misclassification costs on minority classes. Then we combined different weighted base classifier outputs with a stacked generalization method. This technique allowed us to leverage the skills of a varied collection of high-performing base learners while balancing each other's limitations and strengths. By utilizing our hybrid technique, we achieved 70.8% average cross-validation balanced accuracy in classifying 28 activities conducted by nurses in real-life settings. We developed this algorithmic pipeline for "The 3rd Nurse Care Activity Recognition Challenge" at ACM UbiComp 2021.

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