RESUMO
Elderly falls are a major concerning threat resulting in over 1.5-2 million elderly people experiencing severe injuries and 1 million deaths yearly. Falls experienced by Elderly people may lead to a long-term negative impact on their physical and psychological health conditions. Major healthcare research had focused on this lately to detect and prevent the fall. In this work, an Artificial Intelligence (AI) edge computing based wearable device is designed and developed for detection and prevention of fall of elderly people. Further, the various deep learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) are utilized for activity recognition of elderly. Also, the CNN-LSTM, RNN-LSTM and GRU-LSTM with and without attention layer respectively are utilized and the performance metrics are analyzed to find the best deep learning model. Furthermore, the three different hardware boards such as Jetson Nano developer board, Raspberry PI 3 and 4 are utilized as an AI edge computing device and the best deep learning model is implemented and the computation time is evaluated. Results demonstrate that the CNN-LSTM with attention layer exhibits the accuracy, recall, precision and F1_Score of 97%, 98%, 98% and 0.98 respectively which is better when compared to other deep learning models. Also, the computation time of NVIDIA Jetson Nano is less when compared to other edge computing devices. This work appears to be of high societal relevance since the proposed wearable device can be used to monitor the activity of elderly and prevents the elderly falls which improve the quality of life of elderly people.
RESUMO
Municipal solid waste is considered to eliminate the problem of dumping and spreading in rural and urban areas of developing countries. Accumulation of solid wastes in open spaces receives greater concern in solid waste management systems because it leads to environmental hazards and health issues. To build a clean environment, it is essential to construct an advanced and intelligent waste management system to handle different compositions of waste materials. The significant step of waste management is the separation of waste components, which is normally carried out by manual operation. As a result, it can generate improper disposal of waste materials, to simplify the separation process mechanically, a novel automated Dense Net- BiLSTM-based red fox (DNBiLSTM-RF) approach is proposed in this paper. The proposed solid waste classification framework is analyzed by using waste data which is gathered from the Tehran waste management organization. The input waste data is preprocessed initially to transform raw amorphous data into appropriate data structures and extract the most significant dense and latent data features. The abnormal variations in waste patterns generate outliers which are effectively removed by applying the interquartile range (IQR) filtering process. Finally, the proposed DNBiLSTM-RF classifier accurately discriminates municipal waste materials into six different categories such as wood waste, textiles, food residues, rubber, paper, and plastics. The hyperparameters of the DenseNet-BiLSTM model are fine-tuned using a red fox (RF) optimization algorithm to enhance the classification performance of the model. The effectiveness of the DNBiLSTM-RF approach is evaluated using performance indicators namely root mean square error (RMSE), mean absolute error (MAE), the ratio of RMSE to the standard deviation (SD), Nash-Sutcliffe efficiency, coefficient of determination, recall, precision, F-measure, and accuracy. The analytic result demonstrates the feasibility of the proposed DNBiLSTM-RF approach in classifying waste materials into respective categories precisely with an accurate rate of about 98.9% over other state-of-the-art approaches.
