Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition
Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on t...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8201 |
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| author | Domonkos Varga |
| author_facet | Domonkos Varga |
| author_sort | Domonkos Varga |
| collection | DOAJ |
| description | Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset. Specifically, the benchmark fails to separate individuals between the training and test sets, leading to inflated performance metrics as models inadvertently learn individual-specific features rather than generalizable action patterns. We analyzed this issue in depth, retrained several benchmarked models using corrected data partitioning methods, and demonstrated a significant drop in accuracy when individuals were properly separated across training and testing. Our findings highlight the importance of rigorous data partitioning in CSI-based action recognition and provide recommendations for mitigating data leakage in future research. This work contributes to the development of more robust and reliable human action recognition systems using WiFi CSI. |
| format | Article |
| id | doaj-art-a64026aecbf44a3cbae17c043c7fd1b3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a64026aecbf44a3cbae17c043c7fd1b32024-12-27T14:53:18ZengMDPI AGSensors1424-82202024-12-012424820110.3390/s24248201Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action RecognitionDomonkos Varga0Nokia Bell Labs, 1082 Budapest, HungaryHuman action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset. Specifically, the benchmark fails to separate individuals between the training and test sets, leading to inflated performance metrics as models inadvertently learn individual-specific features rather than generalizable action patterns. We analyzed this issue in depth, retrained several benchmarked models using corrected data partitioning methods, and demonstrated a significant drop in accuracy when individuals were properly separated across training and testing. Our findings highlight the importance of rigorous data partitioning in CSI-based action recognition and provide recommendations for mitigating data leakage in future research. This work contributes to the development of more robust and reliable human action recognition systems using WiFi CSI.https://www.mdpi.com/1424-8220/24/24/8201WiFi CSIhuman action recognitionmachine learning integrity |
| spellingShingle | Domonkos Varga Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition Sensors WiFi CSI human action recognition machine learning integrity |
| title | Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition |
| title_full | Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition |
| title_fullStr | Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition |
| title_full_unstemmed | Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition |
| title_short | Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition |
| title_sort | mitigating data leakage in a wifi csi benchmark for human action recognition |
| topic | WiFi CSI human action recognition machine learning integrity |
| url | https://www.mdpi.com/1424-8220/24/24/8201 |
| work_keys_str_mv | AT domonkosvarga mitigatingdataleakageinawificsibenchmarkforhumanactionrecognition |