A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss

Ting Zhao, Haibao Chen (), Yuchen Bai, Yuyan Zhao and Shenghui Zhao
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Ting Zhao: School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
Haibao Chen: School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
Yuchen Bai: School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
Yuyan Zhao: School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
Shenghui Zhao: School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China

IJERPH, 2022, vol. 19, issue 18, 1-19

Abstract: Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive activities in dementia patients’ daily life, such as repeated handling of objects and repeated packing of clothes. It is particularly important to recognize the daily activities of the elderly, which can be further used to predict and monitor chronic diseases. In this paper, we propose a hierarchical ensemble deep learning activity recognition approach with wearable sensors based on focal loss. Seven basic everyday life activities including cooking, keyboarding, reading, brushing teeth, washing one’s face, washing dishes and writing are considered in order to show its performance. Based on hold-out cross-validation results on a dataset collected from elderly volunteers, the average accuracy, precision, recall and F1-score of our approach are 98.69%, 98.05%, 98.01% and 97.99%, respectively, in identifying the activities of daily life for the elderly.

Keywords: activity recognition; deep learning; wearable sensors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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