| Popis: |
As per the data provided by the World Health Organization (WHO), falls are one of the major reasons for unintentional deaths or injuries in elderly people. Even though there are a lot of fall detection methods and algorithms exist, there is no efficient artificial intelligence strategy for detecting falls. Various literature states that Fall Detection among Elderly Person (FDEP) provides the possibility of bringing up an efficient and cost-effective way to tackle this problem. This paper generated a signal-based image dataset, SimgFall from the existing accelerometer or gyroscope-based sensor data of the SiSFall dataset for early detection of fall to fasten the medical assistance process. This SimgFall data is proposed by the proposed FallCNN model, a novel deep Convolutional Neural Network (CNN) based architecture that includes multiple folds of CNN network. These models utilize depth-wise convolution with varying dilation rates for efficiently extracting diversified features from the SimgFall dataset. 1992 signal-based images of which 498 are the samples collected for fall, jump, stumble and walk of four classes respectively. Further, performance evaluation on the generated dataset using different pre-trained and custom models has been analysed based on the loss and accuracy curve. The experimental results show that the highest classification accuracy (98%) is achieved using the proposed customized FallCNN model. |
