Sensor-Based Benchmark Datasets: Comparison and Analysis

Ahad, Md. Atiqur Rahman; Antar, Anindya Das; Ahmed, Masud

doi:10.1007/978-3-030-51379-5_6

Cited by 2 publications

(1 citation statement)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [106] offers an exhaustive survey on datasets tailored for human activity recognition (HAR) through the use of installed sensors. These datasets are pivotal for researchers aiming to develop efficient machine learning models for activity recognition, and they exclude datasets based on RGB or RGB-Depth video actions.…”

Section: Sensor-based Datasetsmentioning

confidence: 99%

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

Kaseris,

Kostavelis,

Malassiotis

2024

MAKE

View full text Add to dashboard Cite

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.

show abstract

Section: Sensor-based Datasetsmentioning

confidence: 99%