A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder - Soontornnapar, Tomorn; Ploysuwan, Tuchsanai (My University, 2025)
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.
Keywords: Autoencoder; Fall detection; Image processing; Short-time Fourier transform; Wearable gyroscope