Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors
Due to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher c...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/13/9/862 |
| _version_ | 1797581065628418048 |
|---|---|
| author | Tong Li Yuhang Yan Minghui Yin Jing An Gang Chen Yifan Wang Chunxiu Liu Ning Xue |
| author_facet | Tong Li Yuhang Yan Minghui Yin Jing An Gang Chen Yifan Wang Chunxiu Liu Ning Xue |
| author_sort | Tong Li |
| collection | DOAJ |
| description | Due to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher costs and complex wearable designs, limiting their wide-ranging applicability. In this paper, we propose a fall detection method based on nursing aids integrated with multi-array flexible tactile sensors. We design a kind of multi-array capacitive tactile sensor and arrange the distribution of tactile sensors on the foot based on plantar force analysis and measure tactile sequences from the sole of the foot to develop a dataset. Then we construct a fall detection model based on a graph convolution neural network and long-short term memory network (GCN-LSTM), where the GCN module and LSTM module separately extract spatial and temporal features from the tactile sequences, achieving detection on tactile data of foot and walking states for specific time series in the future. Experiments are carried out with the fall detection model, the Mean Squared Error (MSE) of the predicted tactile data of the foot at the next time step is 0.0716, with the fall detection accuracy of 96.36%. What is more, the model can achieve fall detection on 5-time steps with 0.2-s intervals in the future with high confidence results. It exhibits outstanding performance, surpassing other baseline algorithms. Besides, we conduct experiments on different ground types and ground morphologies for fall detection, and the model showcases robust generalization capabilities. |
| first_indexed | 2024-03-10T22:59:52Z |
| format | Article |
| id | doaj.art-f302a180a04d4c09b98e54edcdffb2dd |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T22:59:52Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-f302a180a04d4c09b98e54edcdffb2dd2023-11-19T09:47:22ZengMDPI AGBiosensors2079-63742023-08-0113986210.3390/bios13090862Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile SensorsTong Li0Yuhang Yan1Minghui Yin2Jing An3Gang Chen4Yifan Wang5Chunxiu Liu6Ning Xue7School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaDue to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher costs and complex wearable designs, limiting their wide-ranging applicability. In this paper, we propose a fall detection method based on nursing aids integrated with multi-array flexible tactile sensors. We design a kind of multi-array capacitive tactile sensor and arrange the distribution of tactile sensors on the foot based on plantar force analysis and measure tactile sequences from the sole of the foot to develop a dataset. Then we construct a fall detection model based on a graph convolution neural network and long-short term memory network (GCN-LSTM), where the GCN module and LSTM module separately extract spatial and temporal features from the tactile sequences, achieving detection on tactile data of foot and walking states for specific time series in the future. Experiments are carried out with the fall detection model, the Mean Squared Error (MSE) of the predicted tactile data of the foot at the next time step is 0.0716, with the fall detection accuracy of 96.36%. What is more, the model can achieve fall detection on 5-time steps with 0.2-s intervals in the future with high confidence results. It exhibits outstanding performance, surpassing other baseline algorithms. Besides, we conduct experiments on different ground types and ground morphologies for fall detection, and the model showcases robust generalization capabilities.https://www.mdpi.com/2079-6374/13/9/862elderly fall detectionmulti-array flexible tactile sensornursing aidstactile sequencesmulti-task learning |
| spellingShingle | Tong Li Yuhang Yan Minghui Yin Jing An Gang Chen Yifan Wang Chunxiu Liu Ning Xue Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors Biosensors elderly fall detection multi-array flexible tactile sensor nursing aids tactile sequences multi-task learning |
| title | Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors |
| title_full | Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors |
| title_fullStr | Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors |
| title_full_unstemmed | Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors |
| title_short | Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors |
| title_sort | elderly fall detection based on gcn lstm multi task learning using nursing aids integrated with multi array flexible tactile sensors |
| topic | elderly fall detection multi-array flexible tactile sensor nursing aids tactile sequences multi-task learning |
| url | https://www.mdpi.com/2079-6374/13/9/862 |
| work_keys_str_mv | AT tongli elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT yuhangyan elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT minghuiyin elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT jingan elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT gangchen elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT yifanwang elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT chunxiuliu elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors AT ningxue elderlyfalldetectionbasedongcnlstmmultitasklearningusingnursingaidsintegratedwithmultiarrayflexibletactilesensors |