Challenges Ahead in Healthcare Applications for Vision and Sensors

The trend of todays healthcare demands prognostic, precautionary, customized and participatory care spawning an increase in stipulation for healthcare resources and services because of growing world population with rapid increase of people with special needs, such as the elderly population. Besides, the World Health Organization (WHO) conducted a survey back in 2013 which highlighted the fact that, global health workforce shortage to reach 12.9 million incoming decades; with more chronic diseases to be observed with an increasing rate of 10% each year like current pandemic situation of COVID-19. Owing to such factors, the researchers and healthcare professionals should seamlessly consolidate, coordinate and contrive new technologies to facilitate patients services; moderating healthcare transformation costs and risks at the same time. Following that, a widespread adoption of sensor technology and computer vision have been witnessed due to their superior performance for a variety of healthcare applications comprising not only remote monitoring and tracking of diseases with computer vision based diagnostic health examination methods but also early detection and prediction of stages of various chronics as well and many more. However, there remain several challenges which include the interoperability and expandability issues of technological devices and sensors including the familiarity of users with the usage of those technologies. Another prime obstacle remains the domain specific datasets, which are required for training user oriented models in terms of data driven approaches. This chapter demonstrates study of advances in modern computer vision techniques along with the development of faster and more accurate sensors for healthcare applications highlighting their challenges, open issues, and performance considerations in healthcare research. abstract environment.