A Study on Caregiver Activity Recognition for the Elderly at Home Based on the XGBoost Model

Zhonghua Liu (), Shuang Zhang, Huihui Zhang and Xiuxiu Li
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Zhonghua Liu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Shuang Zhang: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Huihui Zhang: Journal Center, China Electric Power Research Institute, Beijing 100192, China
Xiuxiu Li: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

Mathematics, 2024, vol. 12, issue 11, 1-15

Abstract: This paper aims to discuss the implementation of data analysis and information management for elderly nursing care from a data-driven perspective. It addresses the current challenges of in-home caregivers, providing a basis for decision making in analyzing nursing service content and evaluating job performance. The characteristics of caregivers’ activities were analyzed during the design of a wearable device-wearing scheme and a sensor data collection system. XGBoost, SVM, and Random Forest models were used in the experiments, with the Cuckoo search algorithm employed to optimize the XGBoost model parameters. Based on the control group experiment, it was confirmed that the XGBoost model, after adjusting the parameters using the Cuckoo search algorithm, exhibited better recognition performance than the SVM and RandomForest models, and the accuracy reached 0.9438. Wearable devices present high recognition accuracy in caregiver activity recognition research, which greatly improves the inspection of caregivers’ work and further promotes the completion of services. This study actively explores the applications of information technology and artificial intelligence theory to address practical problems and effectively promote the digitalization and intelligent development of the elderly nursing care industry.

Keywords: elderly nursing care; behavior recognition; XGBoost; sensor data; wearable device (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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