From caretaking activities for elderly people to being assistive in healthcare setup, mobile and non-mobile robots have the potential to be highly applicable and serviceable. The ongoing pandemic has shown that human-to-human contact in healthcare institutions and senior homes must be limited. In this scenario, elderlies and immunocompromised individuals must be exclusively protected. Robots are a promising way to overcome this problem in assisted living environments. In addition, the advent of AI and machine learning will pave a way for intelligent robots with cognitive abilities, while enabling them to be more aware of their surroundings. In this paper, we discuss the general perspectives, potential research opportunities, and challenges arising in the area of robots in assisted living environments and present our research work pertaining to certain application scenarios, i.e., robots in rehabilitation and robots in hospital environments and pandemics, which, in turn, exhibits the growing prospects and interdisciplinary nature of the field of robots in assisted living environment.

The recent pandemic outbreak proved social distancing effective in helping curb the spread of SARS-CoV-2 variants along with the wearing of masks and hand gloves in hospitals and assisted living environments. Health delivery personnel having undergone training regarding the handling of patients suffering from Corona infection have been stretched. Administering injections involves unavoidable person to person contact. In this circumstance, the spread of bodily fluids and consequently the Coronavirus become eminent, leading to an upsurge of infection rates among nurses and doctors. This makes enforced home office practices and telepresence through humanoid robots a viable alternative. In providing assistance to further reduce contact with patients during vaccinations, a software module has been designed, developed, and implemented on a Pepper robot that estimates the pose of a patient, identifies an injection spot, and raises an arm to deliver the vaccine dose on a bare shoulder. Implementation was done using the QiSDK in an android integrated development environment with a custom Python wrapper. Tests carried out yielded positive results in under 60 s with an 80% success rate, and exposed some ambient lighting discrepancies. These discrepancies can be solved in the near future, paving a new way for humans to get vaccinated.