摘要:行走是日常生活中最常见的行为之一，它的特征可以反映人的身份、健康等重要信息.例如，行走的速度、方向、步数、步长等细粒度的参数可以为室内追踪、步态分析、老人看护等情境感知应用提供关键信息.因此，在近几年中，利用环境中已有的Wi-Fi信号对行走进行感知受到了研究人员的广泛关注.为了利用Wi-Fi信号感知行走，当前的方法都需要进行大量的行走数据采集，通过经验观察或者离线学习，提取信号特征来识别行走以及估计行走参数.由于缺乏理论指导，所提取信号特征较为间接且往往包含与环境和感知目标相关的冗余信息，所以当环境和感知目标发生变化时，系统需要重新进行学习，使其难以被应用于无线环境易变的真实场景中.不同于以往工作，首次在不需要任何预训练的情况下，利用环境中已有的Wi-Fi信号实现了在连续活动中对行走行为的精准识别，并且能够同时精确地估计行走的速度、方向、步数、步长等多维信息，为上层情境感知应用提供关键的上下文信息.特别地，通过分析人在行走过程中产生的多普勒效应和Wi-Fi信道状态信息（channel state information）之间的关系，建立基本的多普勒速度运动模型，揭示了行走行为和信道状态信息变化之间的理论关联.同时，基于该模型，通过多重信号分离（multiple signal classification）算法从信道状态信息中提取出了与环境和感知目标均无关、仅与人运动状态相关的信号特征——多普勒速度.最后，通过深入研究多普勒速度和人的行走真实速度之间的映射关系，提出了基于多普勒速度的行走识别与细粒度的行走参数估计方法，且经过在不同环境中、由不同实验者进行的大量实验也表明了行走识别和行走参数估计方法的准确性和鲁棒性.其中，对于行走识别的准确率达到了95.5%，行走速度大小估计的相对中位误差为12.2%，方向估计的中位误差为9°，步数统计的准确率达90%，步长估计的中位误差为0.12m.



Abstract:As one of the common daily behaviors, walking could reveal much important information, such as one's identity and health condition. Fine-grained walking information such as walking velocity, walking direction, the number of steps, and stride length could provide important references for indoor tracking, gait analysis, elder care, and other context-aware situation applications. Thus, the perception of human walking utilizing the environmental Wi-Fi signal has been widely concerned by researchers in recent years. In order to achieve the perception of human walking, current methods usually need to gather a lot of walking data and then extract signal feature from extensive data through empirical observation or off-line training. However, due to the lack of theoretical instruction, the extracted signal feature is indirect and often contains redundant information of environment and sensing target. Therefore, as long as there is a change of the environment or sensing target, these systems have to regather data and relearn the signal feature for new situation. This would cause difficulties when applied in real life with varied wireless environment. Different from these works, this study has achieved the walking recognization in daily continuous activities without any learning requirement. Moreover, the fine-grained parameters such as walking velocity, walking direction, the number of steps, and stride length have been estimated in order to provide crucial context for upper layer context-aware applications. Specially, by analyzing the relationship between channel state information (CSI) and Doppler effect introduced by human movement, a Doppler velocity model is firstly established revealing that the theoretical relationship between human movement and CSI variation. Then by utilizing the MUSIC algorithm, the Doppler velocity could be obtained from Wi-Fi CSI which serves as an effective signal feature in revealing human movement and unrelated to the environment and human target. Finally, by studying the relationship between Doppler velocity and real human walking velocity, walking behavior as well as estimating fine-grained walking parameters could be recognized. Through extensive experiments done by different volunteers in different environments, the results have demonstrated the accuracy and robustness of the system. The system achieves an accuracy of 95.5% in walking recognition, a relative median error of 12.2% in walking velocity estimation, a median error of 9° in walking direction estimation, an accuracy of 90% in step counting and a median error of 0.12m in stride length estimation.



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