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Abstract-In general, the applications of robots have shifted rapidly from industrial uses to social uses. This provides robots with the ability to naturally interact with human beings and socially fit into the human environment. The deployment of social robots in the healthcare system is becoming extensive as a result of the shortage of healthcare professionals, rising costs of healthcare and the exponential growth in the number of vulnerable populations such as the sick, the aged and children with developmental disabilities. Consequently, social robots are used in healthcare for providing health education and entertainment for patients in the hospital and for providing aids for the sick and aged. They are also used for dispensing drugs and providing rehabilitation as well as emotional and aging care. Hence, social robots improve the efficiency and quality of healthcare services. The interaction between social robots and human beings is known as human-robot interaction. Human-robot interaction in healthcare is faced with numerous challenges such as the fear of displacement of caregivers by robots, safety, usefulness, acceptability as well as appropriateness. These challenges ultimately lead to a low rate of acceptance of the robotic technology. Consequently, this paper extensively appraises humanrobot interaction in healthcare, their applications and challenges. Design, ethical and usability issues such as privacy, trust, safety, users' attitude, culture, robot morphology as well as emotions and deception arising from the interaction between humans and robots in healthcare are also reviewed in this paper.

Advances in robotics have paved the way for a novel approach of organizing large numbers of robots, otherwise referred to as multi-robots. Multi-robots can either be homogenous or heterogeneous. Nevertheless, a group of autonomous and relatively homogenous robots that interacts with one another as well as with their environment is referred to as swarm robots. Swarm robots are biologically inspired by natural swarms as found in animal societies such as birds and fishes as well as social insects such as honey bees, wasps, termites and ants. Hence, they exhibit certain properties which are similar to those found in these creatures such as aggregation, self-organization, foraging as well as flocking. Swarm robots work together to achieve a desired goal, which is usually too complex for a single robot to accomplish. They are typically characterized by simplicity of individuals, fault tolerance, autonomy, parallelism, high reliability, scalability as well as robustness. They can be used for mining, military, medical and agricultural activities. They can also be used for search and rescue missions, toxic waste cleanup, and for piling sandbags along coastlines in preparation for floods or hurricane. Nevertheless, swarm robots are plagued with the stigma of widespread, interference, uncertainty, safety and lack of reliable communication. Furthermore, studies in swarm robotics are practically limited to virtual reality simulations. Hence, the principles of swarm robotics are rarely applied to real-life problems.  It is against this background that this study systematically explores swarm robots. This study reviewed eighty literatures relating to swarm robots. These literatures were obtained from journal articles, technical reports, books, and conference proceedings. The selection of these literatures was based on their relevance to the research problem. This study revealed that the application of swarm robots to real life problems would promote the development of systems that are robust, fault tolerant and scalable.

Brain-Computer Interface (BCI) otherwise known as a Brain-Machine Interface (BMI) is an emergent technology whose goal is to create a real-time and direct communication pathway between the brain and external devices such as computers, robots, artificial limbs and wheelchairs. In BCI, cerebral or brain activities control these devices by transmitting and receiving signals from the brain. BCI is applied in healthcare to improve the communication capabilities of people living with disabilities or locked in syndrome such as traumatic brain disorders, Amyotrophic Lateral Sclerosis (ALS), spinal cord injury, brain stem stroke and other severe motor disabilities. BCI also increases the independence of disabled individuals by improving their muscle control. Consequently, BCI improves the quality of life of disabled persons by allowing this group of people to live a normal and comfortable life. In spite of the benefits of BCI, the technology is not widely deployed in healthcare. This is because of the numerous challenges associated with it. One of the basic limitations of BCI is that the signals received from the brain are prone to interference. Furthermore, legal and ethical concerns such as the risk of infection or hemorrhage, psychological

There is a rapid increase in the demand of healthcare resources in Nigeria mainly healthcare personnel and healthcare facilities. This is due to the prevalence of chronic diseases such as endemic malaria, arthritis, hypertension, diabetes; an upsurge in the rate of avoidable deaths as well as an exponential increase in the population. However, healthcare resources in Nigeria are insufficient. For instance, in recent times, the ratio of doctors to the inhabitants is 1: 4,857. Moreover, more than 60% of the Nigerian populace resides in rural areas where there are extreme shortages of healthcare practitioners and healthcare facilities due to geographical isolation and lack of opportunities. Hence, the low ratio of healthcare practitioners to patients causes a heavy workload on the healthcare practitioners. This however leads to medical errors as healthcare providers work under intense pressure to attend to the medical needs of their patients. This in turns leads to considerable loss of lives. In order to ameliorate this situation, this paper proposes an ontology based framework that will enable healthcare providers in Nigeria to continuously monitor their patients" health remotely outside the settings of the hospital. This will reduce the workload of the healthcare providers, assist them in decision making process as well as reduce the long waiting hours of the patients within the hospital environment. This framework is also designed to tackle the challenge of semantic interoperability facing healthcare systems around the globe.