Towards a Multi-purpose Robotic Nursing Assistant

Robotic nursing aid is one of the heavily researched areas in robotics nowadays. Several robotic assistants exist that only focus on a specific function related to nurses assistance or functions related to patient aid. There is a need for a unified system that not only performs tasks that would assist nurses and reduce their burden but also perform tasks that help a patient. In recent times, due to the COVID-19 pandemic, there is also an increase in the need for robotic assistants that have teleoperation capabilities to provide better protection against the virus spread. To address these requirements, we propose a novel Multi-purpose Intelligent Nurse Aid (MINA) robotic system that is capable of providing walking assistance to the patients and perform teleoperation tasks with an easy-to-use and intuitive Graphical User Interface (GUI). This paper also presents preliminary results from the walking assistant task that improves upon the current state-of-the-art methods and shows the developed GUI for teleoperation.

Automated system to measure Tandem Gait to assess executive functions in children

As mobile technologies have become ubiquitous in recent years, computer-based cognitive tests have become more popular and efficient. In this work, we focus on assessing motor function in children by analyzing their gait movements. Although there has been a lot of research on designing automated assessment systems for gait analysis, most of these efforts use obtrusive wearable sensors for measuring body movements. We have devised a computer vision-based assessment system that only requires a camera which makes it easier to employ in school or home environments. A dataset has been created with 27 children performing the test. Furthermore in order to improve the accuracy of the system, a deep learning based model was pre-trained on NTU-RGB+D 120 dataset and then it was fine-tuned on our gait dataset. The results highlight the efficacy of proposed work for automating the assessment of children's performances by achieving 76.61% classification accuracy.