Fall is one of the most critical health challenges in the community, which can cause severe injuries and even death. The primary purpose of this study is to develop a deep neural network using wearable sensor data to detect falls. Most datasets in this field suffer from the problem of data imbalance so that the instances belonging to the Fall classes are significantly less than the data of the normal class. This study offers a dynamic sampling technique for increasing the balance rate between the samples belonging to fall and normal classes to improve the accuracy of the learning algorithms. The Sisfall dataset was used in which human activity is divided into three categories: normal activity (BKG), moments before the fall (Alert), and role on the ground (Fall). Three deep learning models, CNN, LSTM, and a hybrid model called Conv-LSTM, were implemented on this dataset, and their performance was evaluated. Accordingly, the Conv-LSTM hybrid model presents 96.23%, 98.59%, and 99.38% in the Sensitivity parameter for the BKG, Alert, and Fall classes, respectively. For the accuracy parameter, we have managed to reach 97.12%. In addition, by using noise smoothing and removal techniques, we can hit a 97.83% accuracy rate. The results indicate the proposed model's superiority compared to other similar studies.
Falling is one of the major health concerns, and its early detection is very important. The goal of this study is an early prediction of impending falls using wearable sensors data. The SisFall data set has been used along with two deep learning models (CNN and a combination model named Conv_Lstm). Also, a dynamic sampling method is offered to improve the accuracy of the models by increasing the equilibrium rate between the samples of the majority and minority classes. To fulfill the main idea of this paper, we present a future prediction strategy. Then, by defining a time variable ‘T’, the system replaces and labels the state of the next T s instead of considering the current state only. This leads to predicting falling states at the beginning moments of balance disturbance. The results of the experiments show that the Conv_Lstm model was able to predict the fall in 78% of cases and an average of 340 ms before the accident. Also, for the Sensitivity criterion, a value of 95.18% has been obtained. A post-processing module based on the median filter was implemented, which could increase the accuracy of predictions to 95%.
Fall is an inevitable part of people's lives, and its early prediction and diagnosis is significant for maintaining physical and mental health. This study aims to identify and make early predictions of impending falls based on wearable sensor data. The proposed approach considered a prediction timeslice (T) parameter. The system can view the labeling up to that time interval, and instead of labeling the current moment state, the T seconds later states are considered. The Sisfall dataset was used in this study, and two deep learning models of the convolutional neural network (CNN) and a hybrid model called Conv-Lstm were implemented on this dataset. This study also offers a dynamic sampling technique for increasing the balance rate between the samples belonging to fall and normal classes to improve the accuracy of the learning algorithms. Based on the evaluation results, the Conv-Lstm hybrid model performed better and was able to have a forecast with an accuracy of 78% and an average time of 0.34 seconds earlier than the accident in the prediction timeslice of 1 second. Also, This model has been able to provide the best result in predicting the fall in the average Sensitivity criterion with 95.18% and in the Accuracy criterion with 97.01%. In addition, a post-processing technique has been used using a median filter algorithm, which improved the accuracy of the fall prediction by up to 95%.
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