Graduate School of Engineering, Chiba University, Nishi-Chiba, Chiba, Japan.
Graduate School of Engineering, Chiba University, Nishi-Chiba, Chiba, Japan; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.
Comput Intell Neurosci. 2016;2016:9845816. doi: 10.1155/2016/9845816. Epub 2016 Apr 24.
Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not been tested in any home living scenarios. In this study we did a series of experiments to investigate the accuracy of activity recognition of the mobile robot in a home living scenario. The daily activities tested in the evaluation experiment include watching TV and sleeping. A dataset recorded by a distributed distance-measuring sensor network was used as a reference to the activity recognition results. It was shown that the accuracy is not consistent for all the activities; that is, mobile robot could achieve a high success rate in some activities but a poor success rate in others. It was found that the observation position of the mobile robot and subject surroundings have high impact on the accuracy of the activity recognition, due to the variability of the home living daily activities and their transitional process. The possibility of improvement of recognition accuracy has been shown too.
人口老龄化要求医疗保健领域提供更多服务。已经表明，移动机器人可以成为老年人家庭生物监测的潜在解决方案。通过我们之前的研究，已经开发出一种能够跟踪对象并识别其日常生活活动的移动机器人系统。然而，该系统尚未在任何家庭生活场景中进行测试。在这项研究中，我们进行了一系列实验，以调查移动机器人在家庭生活场景中进行活动识别的准确性。评估实验中测试的日常活动包括看电视和睡觉。使用分布式测距传感器网络记录的数据集作为活动识别结果的参考。结果表明，并非所有活动的准确性都一致；也就是说，移动机器人在某些活动中可以达到很高的成功率，但在其他活动中成功率却很差。由于家庭日常生活活动及其过渡过程的可变性，发现移动机器人的观察位置和对象周围环境对活动识别的准确性有很大影响。也展示了提高识别准确性的可能性。
