Combining Human Action Sensing of Wheelchair Users and Machine Learning for Autonomous Accessibility Data Collection
Combining Human Action Sensing of Wheelchair Users and Machine Learning for Autonomous Accessibility Data Collection
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DOI：
10.1587/transinf.2015edp7278
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发表时间：
2016-04-01
影响因子：
0.7
通讯作者：
Matsuo, Yutaka
中科院分区：
文献类型：
--
作者：
Iwasawa, Yusuke;Eguchi Yairi, Ikuko;Matsuo, Yutaka
最近智能设备（如智能手机）使用的增加增强了日常人类行为感测与普适计算中有用应用之间的关系。本文提出了一种受个人传感技术启发的新方法，用于以比传统数据收集方法更低的成本收集和可视化道路可达性。为了评估的方法，我们记录了9个轮椅使用者的户外活动，每个约一个小时，通过使用iPod touch和摄像机上的加速度计，收集的监督数据从视频的手，并估计轮椅的行动作为衡量东京的街道水平无障碍。该系统检测到路边攀爬，触觉指示器上移动，在斜坡上移动，并停止，F分数分别为0.63，0.65，0.50和0.91。此外，我们进行了人工有限数量的训练数据的实验，以调查估计目标所需的样本数量。
The recent increase in the use of intelligent devices such as smartphones has enhanced the relationship between daily human behavior sensing and useful applications in ubiquitous computing. This paper proposes a novel method inspired by personal sensing technologies for collecting and visualizing road accessibility at lower cost than traditional data collection methods. To evaluate the methodology, we recorded outdoor activities of nine wheelchair users for approximately one hour each by using an accelerometer on an iPod touch and a camcorder, gathered the supervised data from the video by hand, and estimated the wheelchair actions as a measure of street level accessibility in Tokyo. The system detected curb climbing, moving on tactile indicators, moving on slopes, and stopping, with F-scores of 0.63, 0.65, 0.50, and 0.91, respectively. In addition, we conducted experiments with an artificially limited number of training data to investigate the number of samples required to estimate the target.