Page 151 - 《软件学报》2021年第10期

李晟洁 等:基于 Wi-Fi 信道状态信息的行走识别与行走参数估计 3123

4 (National Engineering Research Center of Software Engineering, Peking University, Beijing 100871, China)
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.
Key words: ubiqutous computing; walking recognition; walking parameters estimation; wireless sensing; Wi-Fi; CSI

行走是日常生活中最常见的行为之一,其中,行走的速度、方向、步数和步长是感知室内人体状态的重要
信息.例如:根据行走的方向和速度,我们可以获得人在室内的移动轨迹,为室内定位和追踪提供帮助;通过记录
一天中人行走的时间和步数,可以统计人的日常运动量信息;而行走的速度和步长特征也反映了人的步态特征,
可以用于判断人的身份和健康状态等高级语义.因此在近几年中,对行走行为的识别和重要参数的感知,受到研
究人员的广泛关注.
为了识别行走,估计人的行走参数,研究人员提出了多种感知技术.例如,基于可穿戴设备的方法、基于环境
传感器的方法、基于计算机视觉的方法等.基于可穿戴设备的方法 [1,2] 是最早被用来识别行走行为并获得相关
参数的方法,然而这种方法要求用户时时刻刻穿戴感知设备,同时还需要不定时地更换电池或为设备蓄电,给人
们的生活带来不便.尤其是对于家里的老年人和小孩子,随时携带设备的要求很难被满足.基于周围环境设备的
方法 [3,4] 尝试通过在环境中部署雷达、声音感知和地板震动感知等特殊设备,非侵入地实现行走行为的识别和
行走参数估计,但是由于需要在居住环境中部署这些专用的感知设备,所以成本较高.基于计算机视觉的方
法 [5,6] 通过摄像头获取室内图像或视频信息进行行走识别,并计算行走的相关参数,但是存在隐私担忧和视线要
求等问题.到目前为止,可被用于日常家庭中的低成本、非接触式的行走识别和行走参数估计技术依然缺失.
近年来,随着 Wi-Fi 技术的迅速发展,利用家庭中普遍存在的 Wi-Fi 设备进行行走识别和行走参数估计受到
了研究人员的关注.一方面,复用环境中已有的 Wi-Fi 设备进行感知不仅不需要增加任何感知成本,同时也保护
了用户的隐私;另一方面,由于 Wi-Fi 信号具有穿墙能力,所以相比于声音、光信号等,感知范围较大.由于这些优
[7]
势,越来越多的基于 Wi-Fi 的感知技术涌现出来,其中,对于行走行为的感知也做了大量的工作.E-eyes 、
[8]
CARM 通过观察 Wi-Fi 信号信道状态信息(channel state information,简称 CSI)的振幅变化,提取了振幅直方分
布、振幅变化频率等特征对包括行走在内的几种日常行为进行了识别,但需要事先离线采集大量数据,进行分
[9]
类模型或判定阈值的学习.除此之外,Wi-Fi 信号也被尝试用来识别人的步态信息.WifiU 、WiWho [10] 发现:不同
人在行走时由于步频、步幅的不同,对 Wi-Fi 信号的影响也不相同.他们通过人在行走过程中造成的时域和频域
的信号波动,识别出人行走的每一步,并提取相应的特征,实现了对人的步态识别,但都要求人必须沿一个方向