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Background As the older adult population increases there is a great need of developing smart healthcare technologies to assist older adults. Robot-based homecare systems are a promising solution to achieving this goal. This study aims to summarize the recent research in homecare robots, understand user needs and identify the future research directions. Methods First, we present an overview of the state-of-the-art in homecare robots, including the design and functions of our previously developed ASCC Companion Robot (ASCCBot). Second, we conducted a user study to understand the stakeholders’ opinions and needs regarding homecare robots. Finally, we proposed the future research directions in this research area in response to the existing problems. Results Our user study shows that most of the interviewees emphasized the importance of medication reminder and fall detection functions. The stakeholders also emphasized the functions to enhance the connection between older adults and their families and friends, as well as the functions to improve the efficiency and productivity of the caregivers. We also identified three major future directions in this research area: human-machine interface, learning and adaptation, and privacy protection. Conclusions The user study discovered some new useful functions that the stakeholders want to have and also validated the developed functions of the ASCCBot. The three major future directions in the homecare robot research area were identified. 

In this paper, we aimed to study the energy consumption problem in a collaborative activity monitoring system (CAMS) that consists of a compan- ion robot and a wearable device. First, we tested the energy consumption in different operation modes of the system. Based on that, we analyzed the effect of band- width on the time cost and energy consumption which allowed us to combine WiFi and Bluetooth together for data transmission to improve the performance of the system. Second, we preprocessed the image data on the wearable device to reduce the size of images before sending them to the robot, and analyzed the time and energy consumption cost by local computing and data transmission. Third, based on the bandwidth of WiFi and Bluetooth, the requirement of time and energy consumption, we proposed an optimization problem on image sizes in which the wearable device decides how to send the data to the robot to reduce the energy and time cost. The results showed that the relations between the bandwidth, time cost, image resolutions and energy consumption could be used to improve the performance of CAMS.