Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors
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) a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-01-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231223804 |
| _version_ | 1827390130834374656 |
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| author | Yanling Ren Minqi Liu Ying Yang Ling Mao Kai Chen |
| author_facet | Yanling Ren Minqi Liu Ying Yang Ling Mao Kai Chen |
| author_sort | Yanling Ren |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:47:55Z |
| format | Article |
| id | doaj.art-4e03c5c0412b4c68b93eb79e81ac8a78 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-03-08T16:47:55Z |
| publishDate | 2024-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-4e03c5c0412b4c68b93eb79e81ac8a782024-01-05T08:04:33ZengSAGE PublishingDigital Health2055-20762024-01-011010.1177/20552076231223804Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensorsYanling Ren0Minqi Liu1Ying Yang2Ling Mao3Kai Chen4 School of Mechanical Engineering, , Hangzhou, China Department of Pneumoconiosis, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China School of Mechanical Engineering, , Hangzhou, China Department of Pneumoconiosis, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China School of Mechanical Engineering, , Hangzhou, ChinaBackground 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.https://doi.org/10.1177/20552076231223804 |
| spellingShingle | Yanling Ren Minqi Liu Ying Yang Ling Mao Kai Chen Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors Digital Health |
| title | Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors |
| title_full | Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors |
| title_fullStr | Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors |
| title_full_unstemmed | Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors |
| title_short | Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors |
| title_sort | clinical human activity recognition based on a wearable patch of combined tri axial acc and ecg sensors |
| url | https://doi.org/10.1177/20552076231223804 |
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