Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Diraco, Giovanni; Rescio, Gabriele; Caroppo, Andrea; Manni, Andrea; Leone, Alessandro

doi:10.20944/preprints202306.0672.v1

2023

DOI: 10.20944/preprints202306.0672.v1

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Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Giovanni Diraco¹,

Gabriele Rescio²,

Andrea Caroppo³

et al.

Abstract: Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the… Show more

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2024

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Cited by 4 publications

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Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

Mekruksavanich,

Jitpattanakul

2024

Applied Sciences

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Human activity recognition (HAR) identifies people’s motions and actions in daily life. HAR research has grown with the popularity of internet-connected, wearable sensors that capture human movement data to detect activities. Recent deep learning advances have enabled more HAR research and applications using data from wearable devices. However, prior HAR research often focused on a few sensor locations on the body. Recognizing real-world activities poses challenges when device positioning is uncontrolled or initial user training data are unavailable. This research analyzes the feasibility of deep learning models for both position-dependent and position-independent HAR. We introduce an advanced residual deep learning model called Att-ResBiGRU, which excels at accurate position-dependent HAR and delivers excellent performance for position-independent HAR. We evaluate this model using three public HAR datasets: Opportunity, PAMAP2, and REALWORLD16. Comparisons are made to previously published deep learning architectures for addressing HAR challenges. The proposed Att-ResBiGRU model outperforms existing techniques in accuracy, cross-entropy loss, and F1-score across all three datasets. We assess the model using k-fold cross-validation. The Att-ResBiGRU achieves F1-scores of 86.69%, 96.23%, and 96.44% on the PAMAP2, REALWORLD16, and Opportunity datasets, surpassing state-of-the-art models across all datasets. Our experiments and analysis demonstrate the exceptional performance of the Att-ResBiGRU model for HAR applications.

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Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

Mekruksavanich,

Jitpattanakul

2024

Applied Sciences

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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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A Multi-Agent System for Service Provisioning in an Internet-of-Things Smart Space Based on User Preferences

Mandaric,

Keselj Dilberovic,

Jezic

2024

Sensors

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The integration of the Internet of Things (IoT) and artificial intelligence (AI) is critical to the advancement of ambient intelligence (AmI), as it enables systems to understand contextual information and react accordingly. While many solutions focus on user-centric services that provide enhanced comfort and support, few expand on scenarios in which multiple users are present simultaneously, leaving a significant gap in service provisioning. To address this problem, this paper presents a multi-agent system in which software agents, aware of context, advocate for their users’ preferences and negotiate service settings to achieve solutions that satisfy everyone, taking into account users’ flexibility. The proposed negotiation algorithm is illustrated through a smart lighting use case, and the results are analyzed in terms of the concrete preferences defined by the user and the selected settings resulting from the negotiation in regard to user flexibility.

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Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Cited by 4 publications

References 97 publications

Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

A Multi-Agent System for Service Provisioning in an Internet-of-Things Smart Space Based on User Preferences

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