European Journal of Molecular & Clinical Medicine

Because nurse care activity identification has a high class imbalance issue and intra-class variability depending on both the subject and the receiver, it is a novel and demanding study topic in human activity recognition (HAR). To address the issue of class imbalance in the Heiseikai data, nurse care activity dataset, we used the Random Forest-based resampling approach. A Gini impurity-based feature selection, model training, and validation using Stratified KFold cross-validation are all part of this technique. Random Forest classification yielded 65.9 percent average cross-validation accuracy in categorising 12 tasks performed by nurses in both laboratory and real-world contexts.. This algorithmic pipeline was created by the "Britter Baire" team for the "2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data."

Paralysis is the inability to move muscles on their own. This is caused as a result of damage in the nervous system therefore the message passing between the brain and the muscles is not proper. Paralysis can be caused due to various reasons like diseases like Parkinsons disease, multiple sclerosis, Guillian Barre Syndrome, stroke, etc. It is also caused by accidents which results in the spinal cord injury or broken necks damaging the nervous system. Our proposed system is to help the paralyzed patient to convey the basic requirements and emergency messages by just moving the finger to display the required message in order for the patient to be motivated as much as possible. It also consists of a buzzer to alert the attender when a message is displayed