Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1
Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1
复制标题
智能系统与应用 - 2019年智能系统会议（IntelliSys）第一卷论文集
DOI：
10.1007/978-3-030-29516-5_54
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Bausch N
中科院分区：
文献类型：
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作者：
Bausch N
2015年，一个用于检测和避开物体的电动轮椅系统通过Raspberry Pi进行了增强，将感官输入源扩展到系统，以记录有关其环境的信息。轮椅使用者并不总是能够使用简单的控制装置，如操纵杆来驾驶，他们可能不得不使用舌头、头或脚来控制轮椅。这可能会增加学习如何驾驶所需的努力，因此跟踪和观察轮椅使用者的进步变得非常重要。本文描述的研究采用机器学习来使用无线接入点并预测其位置，并通过长期使用来学习房间和建筑物之间的路线。该系统使用位置和加速度计数据来呈现有关驾驶模式和碰撞行为的信息。主要用户界面用于轮椅用户在驾驶时在室内定位自己，次要用户界面向护理人员显示过去的信息，以告知轮椅用户的事件和一般跟踪。
In 2015 a powered wheelchair system to detect and avoid objects was enhanced with a Raspberry Pi to extend the sensory input sources to the system in order to record information about its environment. Wheelchair users are not always able to use simple controls such as joysticks to drive and they may have to control the wheelchair using their tongue, head or feet. This can increase the effort it takes to learn how to drive and therefore it becomes important to track and observe how a wheelchair user is progressing. The research described in this paper employs machine learning to use wireless access points and predict its location, and with prolonged use will learn routes between rooms and buildings. The system uses location and accelerometer data to present information about driving patterns and collisions behaviour. The primary user interface is for the wheelchair user to orientate themselves indoors whilst driving and a secondary user interface is displaying past information to a carer to inform about incidents and general tracking of the wheelchair user.