ABSTRACT
The current COVID-19 pandemic situation necessitates the need for a prompt, safe and a contactless method for the dispatch of basic items and other essentials in various domains such as hospitals, manufacturing industries and warehouses. Contemporary robot technology can help build robots that can handle objects safely and replace and/or assist humans in such domains. Robots with soft grippers can be used in hospitals where lightweight items like bottles, medicines and tablets can be handed over to patients. They can be used in warehouses to lift objects of varying topology. This paper discusses the design of the gripper arm for a robotic trolley that can be used to pick and place objects. The gripper arm was modelled on Autodesk Fusion360, and the analysis was done on Ansys. The arm and the gripper were manufactured using ABS plastic and a composite material consisting of elastosil silicone rubber, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The current COVID-19 pandemic situation necessitates the need for a prompt, safe and a contactless method for the dispatch of basic items and other essentials in various domains such as hospitals, manufacturing industries and warehouses. Contemporary robot technology can help build robots that can handle objects safely and replace and/or assist humans in such domains. Robots with soft grippers can be used in hospitals where lightweight items like bottles, medicines and tablets can be handed over to patients. They can be used in warehouses to lift objects of varying topology. This paper discusses the design of the gripper arm for a robotic trolley that can be used to pick and place objects. The gripper arm was modelled on Autodesk Fusion360, and the analysis was done on Ansys. The arm and the gripper were manufactured using ABS plastic and a composite material consisting of elastosil silicone rubber, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The fatalities associated with driving while intoxicated (DWI) are on the rise, leading to a staggering twelve thousand people dying from it and nine lakh people getting arrested every year. DWIs are usually confirmed with the use of breathalyzers, which require the subject to blow into the machine. In light of the current pandemic caused by COVID-19, a susceptible individual may deny blowing into the machine. Thus, the need for a contactless method to detect if someone is drunk arises, so that suspects are prevented from taking advantage of the situation. This also assists law enforcement in the detection of DWI cases. The proposed study is the method to detect intoxication in a given suspect through Graph Neural Networks using facial landmarks. We also present a labeled dataset as a complementary dataset for intoxication detection. This dataset is the first graph-based data available for the detection of alcohol intoxication. Extensive experiments were carried out to validate this approach. © 2021 IEEE.
ABSTRACT
BACKGROUND: Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence. METHODS: This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study. RESULTS: By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods. CONCLUSIONS: This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.