Robotic Assistance in Medication Intake: A Complete Pipeline

Social robots counselling in community pharmacies - Helping or harming? A qualitative study of pharmacists' views

Background

Welfare technological solutions such as social robots attempt to meet the growing needs of the healthcare sector. Social robots may be able to respond to the shortage of pharmaceutical personnel at community pharmacies. However, there is a lack of previous studies regarding the use of social robots for medication counselling purposes in a pharmacy setting.

Objectives

The objective of this qualitative study was to explore pharmacists' views on the potential role of social robots in medication counselling.

Methods

Pharmacists, purposively sampled based on having recent experience of counselling customers in community pharmacies in Finland, first acted as customers interacting with the social robot in a simulated setting, before taking part in a focus group where their perspectives were explored. The focus group discussions were conducted in October and November 2022. The qualitative data was transcribed and analysed using reflexive thematic analysis.

Results

The number of participants was eight in total. A main theme of how the robot may either help or harm concerning medication safety within a pharmacy setting was identified. The six sub-themes found, according to pharmacists' views on robot counselling in community pharmacies, are context, digital competence, customer integrity, interaction, pharmacists' professional role and human skills.

Conclusions

According to the study findings, pharmacists experience that the social robot can offer a potential complement to a human pharmacist. The robot is seen as beneficial with respect to certain customer groups and in the light of personnel shortages, and may in the future add to trust, equality, freedom of choice and multilingualism, among other things, in the customer service situation at community pharmacies, thus improving medication safety.

VLP Landmark and SLAM-Assisted Automatic Map Calibration for Robot Navigation with Semantic Information

With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot’s position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively.