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  1. Free, publicly-accessible full text available December 1, 2024
  2. 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.

     
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  3. null (Ed.)
    Convolutional Neural Networks (CNN) are becomin deeper and deeper. It is challenging to deploy the networks directly to embedded devices be- cause they may have different computational capacities. When deploying CNNs, the trade-off between the two objectives: accuracy and inference speed, should be considered. NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm is a multi-objective optimiza- tion algorithm with good performance. The network architecture has a significant influence on the accuracy and inference time. In this paper, we proposed a con- volutional neural network optimization method using a modified NSGA-II algorithm to optimize the network architecture. The NSGA-II algorithm is employed to generate the Pareto front set for a specific convolutional neural network, which can be utilized as a guideline for the deployment of the network in embedded devices. The modified NSGA-II algorithm can help speed up the training process. The experimental results show that the modified NSGA-II algorithm can achieve similar results as the original NSGA-II algorithm with respect to our specific task and saves 46.20% of the original training time. 
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