Baymax – The Healthcare Robot

Chaudhary, K. P.; Deshpande, Soham; Baswant, Vrushket; Shintre, Chaitanya; Bandi, Yukti

doi:10.1109/incet51464.2021.9456437

Cited by 2 publications

(1 citation statement)

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“…A similar system, RoNA (Robotic Nursing Assistant), executes the same task, but is instead controlled by the operator through an external GUI (Ding et al, 2014). Chaudhary et al (2021) plan to design a new medical robot, called BAYMAX, which will serve also as personal companion for general healthcare. The robot will be equipped with a head, comprising a camera, microphone and speakers.…”

Section: Related Workmentioning

confidence: 99%

HR1 Robot: An Assistant for Healthcare Applications

et al. 2022

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According to the World Health Organization1,2 the percentage of healthcare dependent population, such as elderly and people with disabilities, among others, will increase over the next years. This trend will put a strain on the health and social systems of most countries. The adoption of robots could assist these health systems in responding to this increased demand, particularly in high intensity and repetitive tasks. In a previous work, we compared a Socially Assistive Robot (SAR) with a Virtual Agent (VA) during the execution of a rehabilitation task. The SAR consisted of a humanoid R1 robot, while the Virtual Agent represented its simulated counter-part. In both cases, the agents evaluated the participants’ motions and provided verbal feedback. Participants reported higher levels of engagement when training with the SAR. Given that the architecture has been proven to be successful for a rehabilitation task, other sets of repetitive tasks could also take advantage of the platform, such as clinical tests. A commonly performed clinical trial is the Timed Up and Go (TUG), where the patient has to stand up, walk 3 m to a goal line and back, and sit down. To handle this test, we extended the architecture to evaluate lower limbs’ motions, follow the participants while continuously interacting with them, and verify that the test is completed successfully. We implemented the scenario in Gazebo, by simulating both participants and the interaction with the robot3. A full interactive report is created when the test is over, providing the extracted information to the specialist. We validate the architecture in three different experiments, each with 1,000 trials, using the Gazebo simulation. These experiments evaluate the ability of this architecture to analyse the patient, verify if they are able to complete the TUG test, and the accuracy of the measurements obtained during the test. This work provides the foundations towards more thorough clinical experiments with a large number of participants with a physical platform in the future. The software is publicly available in the assistive-rehab repository4 and fully documented.

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Section: Related Workmentioning

confidence: 99%