Complete Scene Parsing for Autonomous Navigation in Unstructured Environments

Chakravarthy, Anirudh; Rohmetra, Honnesh; Goel, Divyam; Baskar, Harshan; Garg, Prateek; Rout, B.K.

doi:10.1109/icoias49312.2020.9081829

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Hence, this work is focused on discussing ML-based solutions for remote monitoring of vital signs which can be run or deployed using simple cameras available on a smartphone or laptop. Artificial intelligence has come a long way from being effective in limited applications [14,47,48], to having a transformative impact across numerous domains [1,20,26,28,38,49,53,57,58]. Artificial Intelligence (AI) has been revolutionizing multiple fields like Drones [24],VANETs [36], and IoT Security [35].…”

Section: Introductionmentioning

confidence: 99%

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

et al. 2021

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The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.

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