Zero-Calibration Device-Free Localization for the IoT Based on Participatory Sensing

Ibrahim, Osama Talaat; Gomaa, Walid; Youssef, Moustafa

doi:10.1109/glocom.2018.8648111

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…The typical way of collecting the training data in literature is to try all the combinations of the number of people in the area of interest [10], [12], [14]- [16]. This makes the training task labor intensive and limits the systems ability to detect a large number of people [18]. To address the scale and overhead of collecting the training data, CROSSCOUNT introduces a new technique that depends on collecting the training samples using only a single person.…”

Section: System Overviewmentioning

confidence: 99%

“…The typical way of collecting the training data in literature is to try all the combinations of the number of people in the area of interest [10], [12], [14]- [16]. This makes the training task labor intensive and limits the systems ability to detect a large number of people [18] This also enhances the system generalizability and increases its ability to deal with the noisy characteristics of wireless channels.…”

Section: System Overviewmentioning

confidence: 99%

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CrossCount: A Deep Learning System for Device-Free Human Counting Using WiFi

2019

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Counting humans is an essential part of many people-centric applications. In this paper, we propose CROSS-COUNT: an accurate deep-learning-based human count estimator that uses a single WiFi link to estimate the human count in an area of interest. The main idea is to depend on the temporal link-blockage pattern as a discriminant feature that is more robust to wireless channel noise than the signal strength, hence delivering a ubiquitous and accurate human counting system. As part of its design, CROSSCOUNT addresses a number of deep learning challenges such as class imbalance and training data augmentation for enhancing the model generalizability. Implementation and evaluation of CROSSCOUNT in multiple testbeds show that it can achieve a human counting accuracy to within a maximum of 2 persons 100% of the time. This highlights the promise of CROSSCOUNT as a ubiquitous crowd estimator with non-labour-intensive data collection from off-theshelf devices.

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Section: System Overviewmentioning

confidence: 99%

“…The typical way of collecting the training data in literature is to try all the combinations of the number of people in the area of interest [10], [12], [14]- [16]. This makes the training task labor intensive and limits the systems ability to detect a large number of people [18] This also enhances the system generalizability and increases its ability to deal with the noisy characteristics of wireless channels.…”

Section: System Overviewmentioning

confidence: 99%

CrossCount: A Deep Learning System for Device-Free Human Counting Using WiFi

2019

Self Cite

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A perspective on human activity recognition from inertial motion data

Gomaa,

Khamis

2023

Neural Comput & Applic

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Human activity recognition (HAR) using inertial motion data has gained a lot of momentum in recent years both in research and industrial applications. From the abstract perspective, this has been driven by the rapid dynamics for building intelligent, smart environments, and ubiquitous systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc., which necessitate and subsume activity recognition aiming at recognizing the actions, characteristics, and goals of one or more agent(s) from a temporal series of observations streamed from one or more sensors. From a more concrete and seemingly orthogonal perspective, such momentum has been driven by the ubiquity of inertial motion sensors on-board mobile and wearable devices including smartphones, smartwatches, etc. In this paper we give an introductory and a comprehensive survey to the subject from a given perspective. We focus on a subset of topics, that we think are major, that will have significant and influential impacts on the future research and industrial-scale deployment of HAR systems. These include: (1) a comprehensive and detailed description of the inertial motion benchmark datasets that are publicly available and/or accessible, (2) feature selection and extraction techniques and the corresponding learning methods used to build workable HAR systems; we survey classical handcrafted datasets as well as data-oriented automatic representation learning approach to the subject, (3) transfer learning as a way to overcome many hurdles in actual deployments of HAR systems on a large scale, (4) embedded implementations of HAR systems on mobile and/or wearable devices, and finally (5) we touch on adversarial attacks, a topic that is essentially related to the security and privacy of HAR systems. As the field is very huge and diverse, this article is by no means comprehensive; it is though meant to provide a logically and conceptually rather complete picture to advanced practitioners, as well as to present a readable guided introduction to newcomers. Our logical and conceptual perspectives mimic the typical data science pipeline for state-of-the-art AI-based systems.

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