###### Abstract  
In facing the outbreak of the pandemic, robots are highly appealing for their non-contact nature. Among them, we have selected the mobile robot manipulator to develop an autonomous system for pandemic prevention, as it possesses both mobility and manipulability. The robot was used as a platform for performing autonomous fever detection, medicine delivery, and environmental disinfection system for the fever station and isolation ward, which are the two primary units that deal with the pandemic in a hospital. The proposed novel algorithms aim to ensure both human safety and comfort by automating fever detection and recognizing medicine taking. Additionally, they address environmental disinfection by effectively covering blind spots. We conducted a series of experiments to evaluate their performance in a hospital-like setting, which was designed specifically for the testing of intelligent medical systems developed in our university. Quantitative assessment was administered to analyze how the introduction of the proposed autonomous system reduced the risk of infection, and feedback was also collected from participants through questionnaires. pandemic prevention; fever detection; medicine delivery; environmental disinfection; mobile robot manipulator +
Footnote †: journal: Medical  
Nakhaeinia et al. [11] developed a compliant wrist to support robotic interaction with real-time proximity and contact feedback to ensure the collaborator's safety. [PERSON] et al. [12] proposed attaching a wearable sensor on a human for the robot to detect and then avoid collision via speed adjustment. We propose regulating the speed of the robot toward a person according to their relative distance [13], as well as providing a voice reminder. In response to the situation of a certain percentage of patients not adhering to the medication regimen, which results in a large number of preventable deaths and a huge waste of healthcare resources [14; 15], the proposed system will further check whether the medicines are indeed taken by the patients by monitoring their motions after medicine delivery. In previous research, [PERSON] et al. proposed applying remote control for robot-assisted medication delivery via healthcare personnel [4]. [PERSON] and [PERSON] proposed utilizing a wearable watch to collect users' medication images, which were then uploaded to a cloud server for analysis [16]. [PERSON] et al. proposed applying the gated recurrent unit-based neural network to classify 3D human skeletons in determining whether the medicines have been taken [17]. Further, [PERSON], [PERSON], and [PERSON] proposed using the Support Vector Machine (SVM) to classify depth images of hand motions during medication intake [18]. To serve as an autonomous system that is free of privacy concerns, the proposed approach aims to recognize the action of medicine taking from the corresponding arm motion sequence. We chose that of the arm over the hand, as it may be less affected by clothes and others. A novel algorithm named the Segmented Dynamic-Time-Warps Recognizer (SDTWR) was then proposed based on the concept of dynamic time warps [19], which is frequently used for measuring similarity between two temporal sequences. Both the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are considered effective for time series recognition and are potential candidates for serving as the learning mechanism. However, during our preliminary study, the CNN demonstrated superior performance. Environmental disinfection is very crucial for the safety and health of medical staff and patients. It needs to be conducted regularly and also under request. In previous research, [PERSON], [PERSON], and [PERSON] proposed installing Ultraviolet-C (UVC) lamps on the robot itself and also on the robotic arm to allow for more comprehensive disinfection, while it required human intervention for remote control [5]. [PERSON] et al.