Scenario-adaptive wireless fall detection system based on few-shot learning
A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large numb...
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China InfoCom Media Group
2023-06-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00339/ |
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| author | Yuting ZENG Suzhi BI Lili ZHENG Xiaohui LIN Hui WANG |
| author_facet | Yuting ZENG Suzhi BI Lili ZHENG Xiaohui LIN Hui WANG |
| author_sort | Yuting ZENG |
| collection | DOAJ |
| description | A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location. |
| format | Article |
| id | doaj-art-159d99b1bb264090bd07478d43964c22 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-159d99b1bb264090bd07478d43964c222025-01-15T02:54:34ZzhoChina InfoCom Media Group物联网学报2096-37502023-06-01711813259578637Scenario-adaptive wireless fall detection system based on few-shot learningYuting ZENGSuzhi BILili ZHENGXiaohui LINHui WANGA scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00339/Wi-Fi sensingfall detectionCSIcross-domain detectionfew-shot learning |
| spellingShingle | Yuting ZENG Suzhi BI Lili ZHENG Xiaohui LIN Hui WANG Scenario-adaptive wireless fall detection system based on few-shot learning 物联网学报 Wi-Fi sensing fall detection CSI cross-domain detection few-shot learning |
| title | Scenario-adaptive wireless fall detection system based on few-shot learning |
| title_full | Scenario-adaptive wireless fall detection system based on few-shot learning |
| title_fullStr | Scenario-adaptive wireless fall detection system based on few-shot learning |
| title_full_unstemmed | Scenario-adaptive wireless fall detection system based on few-shot learning |
| title_short | Scenario-adaptive wireless fall detection system based on few-shot learning |
| title_sort | scenario adaptive wireless fall detection system based on few shot learning |
| topic | Wi-Fi sensing fall detection CSI cross-domain detection few-shot learning |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00339/ |
| work_keys_str_mv | AT yutingzeng scenarioadaptivewirelessfalldetectionsystembasedonfewshotlearning AT suzhibi scenarioadaptivewirelessfalldetectionsystembasedonfewshotlearning AT lilizheng scenarioadaptivewirelessfalldetectionsystembasedonfewshotlearning AT xiaohuilin scenarioadaptivewirelessfalldetectionsystembasedonfewshotlearning AT huiwang scenarioadaptivewirelessfalldetectionsystembasedonfewshotlearning |