TinySense: Multi-user respiration detection using Wi-Fi CSI signals

Wang, Pei; Guo, Bin; Xin, Tong; Wang, Zhu; Yu, Zhiwen

doi:10.1109/healthcom.2017.8210837

Cited by 36 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normal respiration plays a crucial part in daily life because abnormal respiration state may endanger a person's life. Currently, plenty of systems based on CSI can detect human breath rate, including PhaseBeat [85], TR-BREATH [87], Liu et al [88], Wang et al [131], TinySense [132], Yang et al [134], Zhang et al [135], FullBreathe [24], BreathTrack [136], FarSense [137], and Khan et al [22]. Different from other behavior recognition that usually applies the pattern-based or deep learning-based methods, most respiration monitoring applications leverage model-based methods.…”

Section: F Discussion On Respiration Monitoringmentioning

confidence: 99%

“…Different from other behavior recognition that usually applies the pattern-based or deep learning-based methods, most respiration monitoring applications leverage model-based methods. The number of users varies from 2 (e.g., Tiny-Sense [132]) to 12 (e.g., TR-BREATH [87]). The patternbased method achieves the best results for the number of users (12 users) and recognition accuracy (above 98% for doze users under LOS and 9 users under NLOS scenarios).…”

Section: F Discussion On Respiration Monitoringmentioning

confidence: 99%

“…The sufficient experimental results show that WiSDAR achieves 96% stable identification precision for eight kinds of activity identification. Zou et al propose a novel device-free human behavior recognition system, called DeepHare [132] in 2018. This system can automatically identify 5 daily behaviors (e.g., sit, stand, walk, run and lying down) in three different rooms (e.g., conference room, office, apartment) by utilizing Autoencoder Long-term Recurrent Convolutional Network (AE-LRCN).…”

Section: ) Coarse-grained Specific Behavior Recognition A: Daily Behmentioning

confidence: 99%

“…WiHear [89] identities 9 vowel pronunciation according to mouth motion profile, and it has 91% precision for one person and 74% precision for 3 persons. TinySense [132] detects a multi-person breathing rate by analyzing biggest stream with the peak-valley difference. Moreover, this system obtains over 95% and 88% precision for respiration rate detection of 1 or 2 volunteers at the same time.…”

Section: B Multi-person Behavior Recognitionmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

View full text Add to dashboard Cite

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

show abstract

Section: F Discussion On Respiration Monitoringmentioning

confidence: 99%

Section: F Discussion On Respiration Monitoringmentioning

confidence: 99%

Section: ) Coarse-grained Specific Behavior Recognition A: Daily Behmentioning

confidence: 99%

Section: B Multi-person Behavior Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

View full text Add to dashboard Cite

show abstract

“…If the indoor passive positioning method is applied in a real environment, noise interference is unavoidable, such as an unrelated human. TinySense [77] solves the challenges of detecting the interaction of multiple people breathing by filtering out noncompliant CSI in human health monitoring. In terms of indoor positioning, radio tomographic imaging (RTI) has received great attention in reducing the limitations of indoor layout and noise.…”

Section: Interferencementioning

confidence: 99%

Survey on WiFi‐based indoor positioning techniques

et al. 2020

View full text Add to dashboard Cite

Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

Zhao

Bhuiyan

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.

show abstract

TinySense: Multi-user respiration detection using Wi-Fi CSI signals

Cited by 36 publications

References 15 publications

A Survey on Human Behavior Recognition Using Channel State Information

A Survey on Human Behavior Recognition Using Channel State Information

Survey on WiFi‐based indoor positioning techniques

Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

Contact Info

Product

Resources

About