A Trajectory-Driven 3D Channel Model for Human Activity Recognition

Abdelgawwad, Ahmed; Mallofré, Andreu Català; Pätzold, Matthias

doi:10.1109/access.2021.3098951

Cited by 11 publications

(7 citation statements)

References 46 publications

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“… Wifi round trip audio acceleration Audio related to activity gives another useful feature in recognition. Accuracy is consistent upon 12 activities Limited activities, Maybe not suitable if activities with similar audio are tested, technology used is relatively new and hardware is not ubiquitous Dataset created and model trained again and again for new activities ML [ 7 ] 2021 Gives a mathematical representation for human body as clusters of different points Radio frequency Time variant features and properties explored while representing human as cluster of scattered points Signa based, stationary objects CSI and IMU data were used as collected ML [ 83 ] 2015 online activity recognition system, which explores WiFi ambient signals for received signal strength indicator (RSSI) fingerprint of different activities. Wifi signal data Simple use.…”

Section: Input Methods: Vision-based and Sensor-basedmentioning

confidence: 99%

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

Bhola

Vishwakarma

2023

Multimed Tools Appl

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With the advent of technology, we are getting more comfortable with the use of gadgets, cameras, etc., and find Artificial Intelligence as an integral part of most of the tasks we perform throughout the day. In such a scenario, the use of cameras and vision-based sensors comes as an escape from many real-time problems and challenges. One major application of these vision-based systems is Indoor Human Activity Recognition (HAR) which serves in a variety of scenarios ranging from smart homes, elderly care, assisted living, and human behavior pattern analysis for identifying any abnormal behavior to abnormal activity recognition like falling, slipping, domestic violence, etc. The effect of HAR in real time has made the area of indoor activity recognition a more explored zone by the industrial segment to attract users with their products in multiple domains. Hence, considering these aspects of HAR, this work proposes a detailed survey on indoor HAR. Through this work, we have highlighted the recent methodologies and their performance in the field of indoor activity recognition. We have also discussed- the challenges, detailed study of approaches with real-world applications of indoor-HAR, datasets available for indoor activity, and their technical details in this work. We have proposed a taxonomy for indoor HAR and highlighted the state-of-the-art and future prospects by mentioning the research gaps and the shortcomings of recent surveys with respect to our work.

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Section: Input Methods: Vision-based and Sensor-basedmentioning

confidence: 99%

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

Bhola

Vishwakarma

2023

Multimed Tools Appl

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“…More details can be found in [19]. For the considered problem, we adopted an unbalanced implementation [40] which pre-trains the generator using the parameters W D of the C-VAE decoder (9). This prevents the faster convergence of the discriminator at early epochs which could cause the generator to not converge.…”

Section: Model Configurationmentioning

confidence: 99%

“…The perturbative effects of the radio signals induced by the presence or movements of human bodies can be interpreted using electromagnetic (EM) propagation theory considerations [8]. These have paved the way to several physical and statistical models for passive radio sensing, which exploit full wave approaches, ray tracing, moving point scattering [9] and diffraction theory [10]- [13]. The body-induced perturbations that impair the radio channel, can be thus acquired, measured, and processed using model-based methods to estimate location and track target information.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic-informed generative models for passive RF sensing and perception of body motions

Savazzi,

Fieramosca,

Kianoush

et al. 2024

Preprint

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Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging problems and Bayesian estimation, such as passive localization, RF tomography, and holography. Physics-informed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproducing EM propagation effects, approximated EM fields and learn a physics-informed data distribution, i.e., a Bayesian prior. The paper discusses two popular techniques, namely variational auto-encoders (VAEs) and generative adversarial networks (GANs), and their adaptations to incorporate relevant EM body diffraction concepts. Proposed EM-informed generative models are verified against classical EM tools driven by diffraction theory and validated on real data. Physics-informed generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing and artificial intelligence (AI): the paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in multiple-input multiple-output (MIMO) communication systems. Proposed generative tools are designed, implemented and verified on resource constrained wireless devices being members of a Wireless Local Area Network (WLAN).

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“…The effect of body movements on channel perturbations can be predicted using analytical models [120].…”

Section: Channel Perturbations Modelingmentioning

confidence: 99%

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Shahbazian

Trubitsyna

2023

IEEE Access

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Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decisionmaking that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wirelessbased human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

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A Trajectory-Driven 3D Channel Model for Human Activity Recognition

Cited by 11 publications

References 46 publications

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

Electromagnetic-informed generative models for passive RF sensing and perception of body motions

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

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