mD-Track

Xie, Yaxiong; Xiong, Jie; Li, Zhenjiang; Jamieson, Kyle

doi:10.1145/3300061.3300133

Cited by 191 publications

(23 citation statements)

References 45 publications

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“…Table 4 provides a summary of the models between human activity characteristics and critical parameters of wireless signal propagation. [36,69], Coarse-grained estimation [14,18,19,[23][24][25][26]37,40,42,43,46,47,49,50,[52][53][54] Distance (dLoS) Fresnel zone model [106][107][108][109][110] Direction Fresnel zone model [106], AoA with antenna array [11,15,65,[111][112][113][114][115] aw Signal Distance, Direction, Velocity mD-Track [116] 3.1. Phase…”

Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

“…The phase difference can derive the rough speed of the human movement. Fresnel zone model [106][107][108][109][110] Direction Fresnel zone model [106], AoA with antenna array [11,15,65,[111][112][113][114][115] aw Signal Distance, Direction, Velocity mD-Track [116] 3. 1…”

Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

“…If each reflection path affected by human motion can be resolved from the received signals, it will definitely improve performance for passive human localization and motion tracking. mD-Track [116] constructs the path resolving model to jointly estimate the multi-dimensional parameters of each reflection path, including angle-of-arrival (AoA), angle-of-departure (AoD), time-of-flight (ToF), and Doppler shift. mD-Track models the received signal as the superposition of signals along L distinct paths as Equation (22).…”

Section: Raw Signalsmentioning

confidence: 99%

See 2 more Smart Citations

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

Section: Raw Signalsmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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“…While motion tracking techniques obtain a person's displacement by calculating Path-Length-Change-Rate (PLCR) [18] and Doppler-Frequency-Shift (DFS) [19], [20] from WiFi CSI. Sometimes, information of ToF, AoA, and DFS are combined to determine the human location and track them [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, WiDar [18] and IndoTrack [19] use DFS information from two perpendicularly placed WiFi links to construct motion trajectory. WiDar2 [20] and mD-Track [21] use only one WiFi-link but combine AoA, ToF and DFS information. For all these approaches, there is no redundancy in the system to provide robust tracking over the entire motion trajectory.…”

Section: Introductionmentioning

confidence: 99%

WiTraj: Robust Indoor Motion Tracking With WiFi Signals

Zeng

Gao

et al. 2023

IEEE Trans. on Mobile Comput.

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WiFi-based device-free motion tracking systems track persons without requiring them to carry any device. Existing work has explored signal parameters such as timeof-flight (ToF), angle-of-arrival (AoA), and Doppler-frequencyshift (DFS) extracted from WiFi channel state information (CSI) to locate and track people in a room. However, they are not robust due to unreliable estimation of signal parameters. ToF and AoA estimations are not accurate for current standardscompliant WiFi devices that typically have only two antennas and limited channel bandwidth. On the other hand, DFS can be extracted relatively easily on current devices but is susceptible to the high noise level and random phase offset in CSI measurement, which results in a speed-sign-ambiguity problem and renders ambiguous walking speeds.This paper proposes WiTraj, a device-free indoor motion tracking system using commodity WiFi devices. WiTraj improves tracking robustness from three aspects: 1) It significantly improves DFS estimation quality by using the ratio of the CSI from two antennas of each receiver, 2) To better track human walking, it leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and, 3) It differentiates walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors. Experiments show that WiTraj can significantly improve tracking accuracy in typical environments compared to existing DFS-based systems. Evaluations across 9 participants and 3 different environments show that the median tracking error < 2.5% for typical room-sized trajectories.

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