Point Transformer-Based Human Activity Recognition Using High-Dimensional Radar Point Clouds

Guo, Zhongyuan; Guendel, Ronny G.; Yarovoy, Alexander; Fioranelli, Francesco

doi:10.1109/radarconf2351548.2023.10149679

Cited by 6 publications

(1 citation statement)

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“…Ref. [76] explores the application of three self-attention models, specifically Point Transformer models, in the classification of Activities of Daily Living (ADL). The experimental dataset, collected at TU Delft, serves as the foundation for investigating the optimal combination of various input features, assessing the impact of the proposed Adaptive Clutter Cancellation (ACC) method, and evaluating the model's robustness within a leave-onesubject-out scenario.…”

Section: Radar Signal Harmentioning

confidence: 99%

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

Kaseris,

Kostavelis,

Malassiotis

2024

MAKE

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Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration.

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Section: Radar Signal Harmentioning

confidence: 99%