A context‐awareness model for activity recognition in robot‐assisted scenarios

Lera, Francisco Javier Rodríguez; Rico, Francisco Martín; Higueras, Ángel Manuel Guerrero; Olivera, Vicente Matellán

doi:10.1111/exsy.12481

Cited by 16 publications

(11 citation statements)

References 38 publications

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“…Note that our simulation-based framework, basically designed for HAR, has versatile applications across various domains, including gesture recognition [14], sports [15], autonomous vehicles [16], social robotics [17], and smart homes [18]. In this research, our validation process involves real experiments covering five human activities, highlighting the effectiveness of our proof-of-concept.…”

Section: B Our Approachmentioning

confidence: 88%

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Waqar,

Pätzold

2024

IEEE Internet Things J.

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Modern human activity recognition (HAR) systems are designed using large amounts of experimental data. So far, real-data-driven or experimental-based HAR systems using Wi-Fi or radar systems have shown considerable results. However, the acquisition of large, clean, and labeled training datasets remains a crucial impediment to the progress of experimentalbased HAR systems. Therefore, in this paper, a paradigm shift from the experimental to a fully simulation-based design of HAR systems is proposed in the context of radar sensing. An endto-end simulation framework is proposed as a proof-of-concept that can simulate realistic millimeter-wave radar signatures for synthesized human motion. We designed a human motion synthesis tool that emulates different types of human activities and generates the spatial trajectories accordingly. These trajectories are processed by a geometric model with respect to user-defined antenna configurations. Considering the long-and short-time stationarity of wireless channels, we synthesize the raw in-phase and quadrature data and process the data to simulate the radar signatures for emulated human activities. Finally, a simulated and a real HAR dataset were used to train and test a simulation-based HAR system, respectively, which gave an average (maximum) classification accuracy of 94% (98.4%). The main advantage of the proposed simulation framework is that the training effort for radar-based classifiers, e.g., gesture recognition systems, can be minimized drastically.

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Section: B Our Approachmentioning

confidence: 88%

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Waqar,

Pätzold

2024

IEEE Internet Things J.

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“…The added value of the proposed approach lies in integrating contextual analysis into semantic values using graph-based techniques, as detailed subsequently. Specifically, leveraging context to manage domain knowledge significantly facilitates a probabilistic analysis to enhance system performance [13,14]. Additionally, adopting selected graph methodologies enhances the explainability of the decision-making process.…”

Section: Introductionmentioning

confidence: 99%

A multilevel graph approach for IoT-based complex scenario management through situation awareness and semantic approaches

Casillo,

Colace,

Lorusso

et al. 2024

J Reliable Intell Environ

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Modern reality and the environments in which we live are characterized by technology components geared toward automatic management through pervasive services. Thanks to the advent of the Internet of Things, such environments can provide information such as pollution levels, public transport conditions, efficiency of energy distribution networks, and identification of suspicious activities by generating complex scenarios. The profitable management of such scenarios can be performed through context modeling and methodologies that can extract and understand environmental information by preventing certain events through artificial intelligence techniques by increasing Situation Awareness. This paper focuses on developing a methodology with predictive capabilities and context adaptability for managing complex scenarios. The use of semantic and graph-based approaches, unlike many approaches used, leads to better integration of knowledge, resulting in improved system performance. In addition, such approaches allow understanding of what is happening in the system at a given time, enabling manipulation and integration of semantic information. Graph-based approaches chosen for this purpose are Ontologies, Context Dimension Trees, and Bayesian Networks, which are able to support the end-user or expert user in handling complex scenarios. The proposed methodology has been validated and applied to real complex scenarios based on the IoT paradigm. The proposed approach validation was conducted using open data from the city of London; a practical scenario case study was conducted in the field of automated management of a Smart Home. In both cases, the system achieved promising results.

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“…The steady interest in HAR is due to its extensive range of applications. Over the years, HAR systems have proven their usefulness in application areas such as social robotics [5], autonomous driving [6], sports [7], [8], home automation [9], healthcare [10], automated video analysis [11], and human-computer interaction [12].…”

Section: Introductionmentioning

confidence: 99%

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Waqar,

Muaaz,

Pätzold

2023

IEEE Sensors J.

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Modern monostatic radar-based human activity recognition (HAR) systems perform very well as long as the direction of human activities is either towards or away from the radar. The monostatic single-input single-output (SISO) and monostatic multipleinput multiple-output (MIMO) radar systems cannot detect motion of an object that moves perpendicularly to the radar's boresight axis. Due to this physical layer limitation, today's radar-based HAR systems fail to classify multi-directional human activities. In this paper, we resolve this typical but critical physical layer problem of contemporary HAR systems. We propose a HAR system underlying a distributed MIMO radar configuration, where multiple antennas of a millimeter wave MIMO radar system (Ancortek SDR-KIT 2400T2R4) are distributed in an indoor environment. In our proposed HAR system, we have two independent and identical monostatic radar subsystems that irradiate and capture the multi-directional human movement from two perspectives, which allows to compute two distinct time-variant radial velocity distributions. A feature extraction network extracts numerous features from the measured time-variant radial velocity distributions, which are then fused by a multiclass classifier to detect five types of human activities. The proposed multi-perspective MIMO-radar-based HAR system achieves a classification accuracy of 98.52%, which surpasses the accuracy of SISO radar-based HAR system by more than 9%. Our approach resolves the physical layer limitations of modern HAR systems that are based on either monostatic SISO or monostatic MIMO radar systems.

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A context‐awareness model for activity recognition in robot‐assisted scenarios

Cited by 16 publications

References 38 publications

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

A multilevel graph approach for IoT-based complex scenario management through situation awareness and semantic approaches

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

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