People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. However, it is inconvenient for wheelchair users to input data while on the move. Hence, an automatic crowdsourcing mechanism is needed.

In this contribution we present SmartWheels, a solution to detect urban features by analyzing inertial sensors data produced by wheelchair movements. Activity recognition techniques are used to process the sensors data stream. SmartWheels is evaluated on data collected from 17 real wheelchair users navigating in a controlled environment (10 users) and in-the-wild (7 users). Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier.

行动不便的人在城市环境中移动时具有多种需求和能力，因此需要个性化的导航说明。提供这些说明需要了解城市特征，如路缘坡道，台阶或沿途的其他障碍物。由于无法从地图上获得这些城市特征并随时间变化，因此将最终用户的信息众包是一种可扩展且有希望的解决方案。但是，轮椅使用者在移动中输入数据是不方便的。因此，需要一种自动众包机制。

在此贡献中，我们提出了SmartWheels，这是一种通过分析轮椅运动产生的惯性传感器数据来检测城市特征的解决方案。活动识别技术用于处理传感器数据流。SmartWheels是根据从17位真实轮椅用户（在受控环境（10位用户）和野外（7位用户）中导航）中收集的数据进行评估的。实验结果表明，SmartWheels是检测城市特征的可行解决方案，特别是通过基于分类器分配给预测的置信度应用特定策略。