An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM)
The lack of fall data for the elderly is a challenging problem in the fall detection community. To date, the fall and activities of daily life simulated by young people have been used in most studies to train and test fall detection algorithms. However, there are differences in movement patterns bet...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9984667/ |
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| author | Zhigang Yu Jiahui Liu Mingchuan Yang Yanmin Cheng Jie Hu Xinchi Li |
| author_facet | Zhigang Yu Jiahui Liu Mingchuan Yang Yanmin Cheng Jie Hu Xinchi Li |
| author_sort | Zhigang Yu |
| collection | DOAJ |
| description | The lack of fall data for the elderly is a challenging problem in the fall detection community. To date, the fall and activities of daily life simulated by young people have been used in most studies to train and test fall detection algorithms. However, there are differences in movement patterns between young and elderly individuals due to bone aging, which leads to the degradation of the algorithm performance in the elderly population. To solve the above issue, this paper proposes a fall detection algorithm combining Federated Learning and Extreme Learning Machine (Fed-ELM). First, the online extreme learning machine can use a small amount of misclassified user data to update the parameters so that its performance is improved for individual users. Then, Federated Learning is applied to share data information among different users without involving user privacy. In this way, the generalizability of the fall detection algorithm is improved. The performance of the proposed algorithm in different age groups is analyzed by experiments. For young people, the accuracy, sensitivity and specificity reach 96.96%, 94.50% and 99.29%, respectively, and the accuracy on each individual is more than 94%. For elderly individuals, the accuracy, sensitivity and specificity reach 99.07%, 96.00% and 98.33%, respectively, and the accuracy of each individual is more than 96%. |
| format | Article |
| id | doaj-art-eebc441da20c478ca0eb6a7bb90cc511 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eebc441da20c478ca0eb6a7bb90cc5112025-08-20T03:16:28ZengIEEEIEEE Access2169-35362022-01-011013081613082410.1109/ACCESS.2022.32290449984667An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM)Zhigang Yu0https://orcid.org/0000-0002-3687-029XJiahui Liu1Mingchuan Yang2Yanmin Cheng3Jie Hu4Xinchi Li5Institute of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, ChinaInstitute of Automation, Shenyang Aerospace University, Shenyang, ChinaInstitute of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, ChinaInstitute of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, ChinaInstitute of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, ChinaInstitute of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, ChinaThe lack of fall data for the elderly is a challenging problem in the fall detection community. To date, the fall and activities of daily life simulated by young people have been used in most studies to train and test fall detection algorithms. However, there are differences in movement patterns between young and elderly individuals due to bone aging, which leads to the degradation of the algorithm performance in the elderly population. To solve the above issue, this paper proposes a fall detection algorithm combining Federated Learning and Extreme Learning Machine (Fed-ELM). First, the online extreme learning machine can use a small amount of misclassified user data to update the parameters so that its performance is improved for individual users. Then, Federated Learning is applied to share data information among different users without involving user privacy. In this way, the generalizability of the fall detection algorithm is improved. The performance of the proposed algorithm in different age groups is analyzed by experiments. For young people, the accuracy, sensitivity and specificity reach 96.96%, 94.50% and 99.29%, respectively, and the accuracy on each individual is more than 94%. For elderly individuals, the accuracy, sensitivity and specificity reach 99.07%, 96.00% and 98.33%, respectively, and the accuracy of each individual is more than 96%.https://ieeexplore.ieee.org/document/9984667/Fall detectionfederated learningextreme learning machineelderly fall |
| spellingShingle | Zhigang Yu Jiahui Liu Mingchuan Yang Yanmin Cheng Jie Hu Xinchi Li An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) IEEE Access Fall detection federated learning extreme learning machine elderly fall |
| title | An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) |
| title_full | An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) |
| title_fullStr | An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) |
| title_full_unstemmed | An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) |
| title_short | An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) |
| title_sort | elderly fall detection method based on federated learning and extreme learning machine fed elm |
| topic | Fall detection federated learning extreme learning machine elderly fall |
| url | https://ieeexplore.ieee.org/document/9984667/ |
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