SD-CNN: A Novel Lightweight Convolutional Neural Network Model for Fall Detection

Aiming at the traditional deep learning fall detection model due to high computational complexity and a large number of parameters, this study proposes a lightweight convolutional neural network model, SD-CNN (SMA-Enhanced Depthwise Convolutional Neural Network), for fall detection. The model is first designed with an SMA attention module to enhance feature representation. Then, depth separable convolution is used to significantly reduce the model complexity. Finally, batch normalisation and Dropout regularisation techniques are combined to efficiently extract spatial-temporal features from temporal signals for accurate classification of fall and non-fall behaviours. The experiments use a sliding window to extract discrete features, three-axis acceleration, and synthetic acceleration as feature inputs. SD-CNN achieves 99.11% accuracy, 98.78% specificity, and 99.39% sensitivity on the homemade dataset Act, which are improved by 7.14%, 6.42%, and 9.38%, respectively, compared to CNN, while the number of parameters is reduced significantly. The effectiveness of the model is also verified by generalisation experiments on the public datasets SisFall and WEDAFall. The SD-CNN algorithm can efficiently complete the fall detection task, and the lightweight design makes it particularly suitable for wearable devices, which provides a highly efficient and reliable solution for real-time fall detection, and it has an important value for practical applications.