Affiliation:
1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China
2. Department of Pneumoconiosis, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
Abstract
Background In digital medicine, human activity recognition (HAR) can be used to track and assess a patient's progress throughout rehabilitation, enhancing the quality of life for the elderly and the disabled. Methods A patch-type flexible sensor that integrated dynamic electrocardiogram (ECG) and acceleration signal (ACC) was used to record the signals of the various behavioral activities of 20 healthy volunteers and 25 patients with pneumoconiosis. Seven HAR tasks were then carried out on the data using four different deep learning methods (CNN, LSTM, CNN–LSTM and GRU). Results When ECG and ACC were obtained simultaneously, the overall accuracy rates of HAR for healthy group were 0.9371, 0.8829, 0.9843 and 0.9486 by the CNN, LSTM, CNN–LSTM and GRU models, respectively. In contrast, the overall accuracy rates of HAR for the pneumoconiosis patients’ group were 0.8850, 0.7975, 0.9425 and 0.8525 by the four corresponding models. The accuracy of HAR for both groups using all four models is higher than when only ACC signal is detected. Conclusion The addition of the ECG signal significantly improves HAR outcomes in the group of healthy individuals, while having relatively less enhancing effects on the group of patients with pneumoconiosis. When ECG and ACC signals were combined, the increase in HAR accuracy was notable compared to cases where no ECG data was provided. These results suggest that the combination of ACC and ECG data can represent a novel method for the clinical application of HAR.
Funder
Key Scientific Research Project of Zhejiang Province
Zhejiang Provincial Natural Science Foundation of China
Fundamental Research Funds for the Provincial Universities of Zhejiang
Subject
Health Information Management,Computer Science Applications,Health Informatics,Health Policy