Challenges

Youssef, Moustafa; Mah, Matthew; Agrawala, Ashok K.

doi:10.1145/1287853.1287880

Cited by 656 publications

(31 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AoA [15,22,25,41,65,66,[112][113][114][115], distance [15,26,100,105,106,110], ToF [41,64,66,68,113,132] Time-domain features: Most time-domain features directly apply statistics. Calculating timedomain features usually takes the input of amplitude, phase, or phase difference whose computation costs are small.…”

Section: Spatial Domainmentioning

confidence: 99%

“…For tracking applications, this survey mainly includes human motion tracking [41,71,129], human tracking [15,17,22,24,26,28,29,48,53,58,[64][65][66][67]70,100,103,105,110,112,113,128,129,136], in-air finger tracking [11,115], and walking direction tracking [106], and human indoor localization [25,[101][102][103][104]114,124,128,132,137,138]. The difference between tracking application and the above three types of applications is that they calculate spatial information, such as direction, speed, or distance estimation.…”

Section: Tracking Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Spatial Domainmentioning

confidence: 99%

Section: Tracking Applicationsmentioning

confidence: 99%

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The shadowing effects produced by a moving object in the line of sight (LOS) between a wireless transmitter and a receiver in a home wireless network can be used to track object motions in indoor environments (Woyach, Puccinelli, and Haenggi 2006;Youssef and Mah 2007). The received signal strength indicator (RSSI) of a wireless signal fluctuates due to the object movements in indoor environments.…”

Section: Introductionmentioning

confidence: 99%

Device-free human micro-activity recognition method using WiFi signals

Al-qaness

2019

Geo-spatial Information Science

View full text Add to dashboard Cite

Human activity tracking plays a vital role in human-computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro-and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based microactivity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.

show abstract

“…Nowadays, localization techniques have been an active field in pervasive and ubiquitous computing. Generally, the target localization methods can be classified as: device-free [1,2,3] and device-based [4,5,6]. Device-based methods need objects to attach assistant wireless devices for signal transmitting or receiving.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity

Guo

et al. 2019

Sensors

View full text Add to dashboard Cite

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.

show abstract

Challenges

Cited by 656 publications

References 18 publications

Human Activity Sensing with Wireless Signals: A Survey

Human Activity Sensing with Wireless Signals: A Survey

Device-free human micro-activity recognition method using WiFi signals

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity

Contact Info

Product

Resources

About