A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder
| dc.contributor.author | Tomorn Soontornnapar | |
| dc.contributor.author | Tuchsanai Ploysuwan | |
| dc.date.accessioned | 2025-07-21T06:12:23Z | |
| dc.date.issued | 2024-12-19 | |
| dc.identifier.doi | 10.1007/s00521-024-10845-4 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/14139 | |
| dc.subject | Autoencoder | |
| dc.subject | Benchmark (surveying) | |
| dc.subject | Overfitting | |
| dc.subject.classification | Context-Aware Activity Recognition Systems | |
| dc.title | A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder | |
| dc.type | Article |