This study aimed to develop daily living support robots for patients with hemiplegia and the elderly. To support the daily living activities using robots in ordinary households without imposing physical and mental burdens on users, the system must detect the actions of the user and move appropriately according to their motions. We propose a reaching-position prediction scheme that targets the motion of lifting the upper arm, which is burdensome for patients with hemiplegia and the elderly in daily living activities. For this motion, it is difficult to obtain effective features to create a prediction model in environments where large-scale sensor system installation is not feasible and the motion time is short. We performed motion-collection experiments, revealed the features of the target motion and built a prediction model using the multimodal motion features and deep learning. The proposed model achieved an accuracy of 93 \% macro average and F1-score of 0.69 for a 9-class classification prediction at 35\% of the motion completion.

翻译：本研究旨在为偏瘫患者及老年人开发日常生活辅助机器人。为在不给用户带来身心负担的前提下，在普通家庭环境中利用机器人辅助日常生活活动，系统需检测用户行为并依据其动作进行适应性移动。本文提出一种针对上臂抬起动作的伸手位置预测方案，该动作对偏瘫患者及老年人在日常活动中构成显著负担。在此类动作中，由于大规模传感器系统安装不可行且动作持续时间较短，难以提取有效特征以构建预测模型。我们通过动作采集实验揭示了目标动作的特征，并利用多模态动作特征与深度学习构建了预测模型。所提模型在动作完成度达35%时，对9类分类预测实现了93%的宏平均准确率与0.69的F1分数。