A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder
| dc.contributor.author | Soontornnapar, Tomorn | |
| dc.contributor.author | Ploysuwan, Tuchsanai | |
| dc.date.accessioned | 2026-08-06T10:50:27Z | |
| dc.date.available | 2026-08-06T10:50:27Z | |
| dc.date.issued | 2025-02-01 | |
| dc.description.abstract | The ability to accurately detect and classify falls is critical for ensuring timely medical intervention, especially for the elderly, who face a significantly higher risk of severe injuries, loss of independence, or fatal outcomes from falls. This paper introduces a novel fall detection approach that addresses these urgent needs by using short-time Fourier transform (STFT) images and the magnitude of quaternion (MQ) signals, fused into STFT-MQ images. The proposed method leverages STFT’s time–frequency representation to capture rapid changes and dynamic characteristics in human motion data from wearable sensors, enhancing its ability to distinguish between fall and non-fall incidents. Utilizing a convolutional neural network autoencoder (CNN-AE), an unsupervised learning model, this approach analyzes transformed data without extensive labeled datasets, offering a scalable solution in diverse settings. Tested on the HIFD dataset with heart rate and IMU sensor data, the STFT-MQ-AE method achieves remarkable sensitivity of 98.08%, specificity of 98.78%, and an overall accuracy of 98.57%, setting a new benchmark in fall detection accuracy. Furthermore, the model’s reliance on an N-way K-shot learning approach enables it to manage unforeseen fall cases effectively without retraining, enhancing adaptability and real-world utility. The model achieves the highest Youden’s index (YI) of 96.85%, underlining balanced performance between fall and non-fall classification. Consistent performance across varied training scenarios yields an average accuracy of 96.10%, making this approach highly reliable. This advancement in fall detection technology offers a practical, effective solution to reduce fall-related injuries and enable timely assistance, thereby promoting safer, more independent living for at-risk populations. | |
| dc.identifier.citation | Neural Computing and Applications, 37(6), 4229-4245, 2025 | |
| dc.identifier.doi | 10.1007/s00521-024-10845-4 | |
| dc.identifier.issn | 09410643 | |
| dc.identifier.other | 2-s2.0-85212502509 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/16793 | |
| dc.source | Neural Computing and Applications | |
| dc.subject | Autoencoder | |
| dc.subject | Fall detection | |
| dc.subject | Image processing | |
| dc.subject | Short-time Fourier transform | |
| dc.subject | Wearable gyroscope | |
| dc.title | A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder | |
| dc.type | Article |