An Efficient Patient Activity Recognition using LSTM Network and High-Fidelity Body Pose Tracking

The need for healthcare services is growing, particularly in light of the COVID-19 epidemic's convoluted trajectory. This causes overcrowding in medical facilities, making it difficult to manage, treat, and monitor patients' health. Therefore, a method to remotely observe the patient's behavior is required, to aid in early warning and treatment, and to reduce the need for hospitalization for patients with minor diseases. This paper proposes a new real-time smart camera system to monitor, recognize and warn the patient's abnormal actions remotely with reasonable cost and easy to deploy in practice. The key benefit of the proposed methods is that patient actions may be detected without the usage of ambient sensors by employing pictures from a regular video camera. It carries out the detection using high-fidelity human body pose tracking with MediaPipe Pose. Then, the Raspberry Pi 4 device and the LSTM network are used for remote monitoring and real-time classification of patient actions. The test dataset is built from reality and reuses the existing datasets. Our system has been evaluated and tested in practice with over 96.84% accuracy, runs at over 30 frames per second, suitable for real-time execution on mobile devices with limited hardware configuration.

Understanding the Effect of COVID-19 on Fuel Consumption of Public Transportation: The Case Study of Chattanooga, TN

The COVID-19 pandemic has caused a drastic change in traffic in the U.S and throughout the world. With the drop in traffic volume, the fuel consumption used for traveling should decline. This study focuses on the changes in fuel consumption of public transportation before and during the pandemic. The fuel consumption volumes of diesel bus fleet in Chattanooga were analyzed to identify the changes between the two periods. Our study provides preparation for future disasters. © 2020 IEEE.

Can a short food supply chain create sustainable benefits for small farmers in developing countries? An exploratory study of vietnam

A number of studies have indicated that short food supply chains could create economic, social, and environmental benefits, but most of those chains were implemented in developed coun-tries. This research aims to find out the characteristics of short food supply chains and their benefits to small farmers in Vietnam, which is a developing country, based on the survey results from 338 small farmers in the third quarter of 2020, with the support of Stata 14 software. The results showed that the short food supply chains in the survey sample in Vietnam were characterized by two main actors: small farmers and distributors. Farmers could sell products flexibly at the local market. There was some initial evidence to prove that these chains helped to stabilize the input, output price, and revenue;formulated sustainable income;and increased the satisfaction and confidence of farmers. They eliminated gender discrimination in rural areas and improved livelihood for ethnic minorities. These chains also enhanced the mindset on green, organic, and clean production of farmers, which in turn created environmental benefits. COVID-19 has posed a negative impact on the income of farmers and made them change their production and sales method. Therefore, the research could suggest some policies to sustainably develop the short food supply chains in Vietnam in the future. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.