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.

Electrogastrograms (EGG) are electrical signals originating from the digestive system, which are closely correlated with its mechanical activity. Electrogastrography is an efficient non-invasive method for examining the physiological and pathological states of the human digestive system. There are several factors such as fat conductivity, abdominal thickness, change in electrode surface area etc, which affects the quality of the recorded EGG signals. In this work, the effect of variations in the contact area of surface electrodes on the information content of the measured electrogastrograms is analyzed using Rényi entropy and Teager-Kaiser Energy (TKE). Two different circular cutaneous electrodes with approximate contact areas of 201.14 mm² and 283.64 mm², have been adopted and EGG signals were acquired using the standard three electrode protocol. Further, the information content of the measured EGG signals were analyzed using the computed values of entropy and energy. Results demonstrate that the information content of the measured EGG signals increases by 6.72% for an increase in the contact area of the surface electrode by 29.09%. Further, it was observed that the average energy increases with increase in the contact surface area. This work appears to be of high clinical significance since the accurate measurement of EGG signals without loss in its information content, is highly useful for the design of diagnostic assistance tools for automated diagnosis and mass screening of digestive disorders.

Electrogastrograms (EGG) are electrical signals generated by the muscles of the stomach and the features of these signals can be used to diagnose several digestive disorders. Entropy is a measure of the disorder associated with a system and hence is a measure of complexity of the system. In medical diagnostics, entropy has proved to be an efficient feature for discriminating the normal and abnormal states of biological systems. In this work the EGG signals have been obtained from normal and abnormal subjects having different digestive abnormalities (diarrhea, vomiting and stomach ulcer), from a local hospital. Further, the Tsallis entropy associated with the collected signals has been analyzed. Results demonstrate that the Mean Tsallis Entropy (MTE) (with α=0.5) of the EGG signals obtained from normal individuals (MTE=313.861) is high when compared to the individuals having diarrhea (MTE=278.0259), vomiting (MTE=105.1278) and stomach ulcer (MTE=-839.201). Since, entropy is the complexity associated with the signal, it is found that the complexity of the normal EGG signals is high when compared to the abnormal EGG signals. This work appears to be of high clinical relevance, since feature extraction from EGG signals is highly useful for diagnosis of digestive abnormalities.

In recent years, Electric Vehicles are becoming more popular. The pollution level in the atmosphere can be effectively minimized by using Electric vehicles for large-scale transportation. A battery station is required for continuous operation; however, the Photovoltaic-based OFF grid charging station can only operate during the day. Therefore, the three-port converters have started to arise from a number of current EV charging station developments. In this study, a unique PWM and Phase Shift Controller are proposed to reduce switching losses and to improve reliability. In addition, for Maximum PowerPoint Tracking, a Fuzzy is added to the PV system. Furthermore, an appropriate interleaved boost converter topology is used to create the various charging voltages required for EV and battery stations. The proposed topology is simulated, and the hardware prototype has been created and tested. The result shows that the proposed topology has a better efficiency than the traditional converters.Article Highlights. A dual composite charging station for electric vehicle charging in environment friendly manner. Optimization of power electronics required in Electric Vehicle charging stations. Maximum utilization of battery for cyclic charging from charger and discharging through Electric Vehicles.

Wireless Body Sensor Network (WBSN) has gained increasing attention in health monitoring and service sector for real-time health monitoring and providing high-quality service. 6G wireless communication technologies overcome the limitations of the current 5G technology. An intelligent security system that prevents DDoS attack by applying solutions based on artificial intelligent is proposed. This method is used to identify the attackers in a heterogeneous environment by applying swarm intelligent-based Meta heuristic artificial intelligent algorithm called grey wolf optimisation (GWO). The improved GWO algorithm fine tunes user categorisation based on the service request from the WBSN devices. This approach includes KD tree (KDT) neighbour search algorithm to find the exact DDoS-attacked user electronic device by applying vertical and horizontal group division policy. The proposed method is evaluated with unsupervised dataset comprising 560 service requests, which include normal, anomaly and abnormal requests in proportions of 65%, 25% and 10%, respectively. The evaluation results show that the precision, recall, F1 Score and accuracy parameters were 92.3%, 95.2%, 93.7% and 91.5%, respectively for the predicting the abnormal electronic devices. Statistical analysis shows that the mean absolute error value is smaller than the other variants of GWO algorithms.