Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Diraco, Giovanni; Rescio, Gabriele; Siciliano, P.; Leone, Alessandro

doi:10.3390/s23115281

Cited by 28 publications

(10 citation statements)

References 121 publications

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“…As said, ML, DL, and sensing have already received extensive coverage in previous review works. Additionally, Interoperability, Multimodality, Real-Time Processing, Resource-Constrained Processing, and Sensing Technologies have been thoroughly analyzed in the previous review study by the authors [16]. This work aims to explore and analyze the concepts of Context Awareness, Data Availability, Personalization, and Privacy, which have not been given much attention in previous reviews.…”

Section: Review Of Related Work and Rationale For This Comprehensive ...mentioning

confidence: 99%

“…In the previous authors' work [16], the dimensions of Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing have been yet analyzed from the perspective of sensing technologies. Composing the dimensions addressed in this review with those previously analyzed outlines what one can define as the temple of Smart Living.…”

Section: Introductionmentioning

confidence: 99%

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

Diraco¹,

Rescio²,

Caroppo³

et al. 2023

Preprint

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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 literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

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Section: Review Of Related Work and Rationale For This Comprehensive ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Diraco¹,

Rescio²,

Caroppo³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Human behavior recognition is a popular research direction in the field of computer vision. With the continuous reduction in the application cost of digital cameras and video cameras and the extensive use of smartphones, it is no longer challenging to shoot and obtain video image data [1]. Studying video and image content is a core issue in the fields of computer vision and multimedia.…”

Section: Introductionmentioning

confidence: 99%

Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning

Salim,

Celik

2024

Electronics

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Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene.

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“…These sensors capture intricate patterns of movement and orientation, forming the foundational data that supports HAR models. Due to its ability to provide abundant, continuous, and multidimensional knowledge of human movements, sensor data is the most effective option for HAR as it enables precise and comprehensive activity recognition [10].…”

Section: Introductionmentioning

confidence: 99%

Enhanced human activity recognition through deep multi-layer perceptron on the UCI-HAR dataset

Hossain,

Ray,

Islam

et al. 2024

IJAAS

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Using the UCI-HAR dataset, this paper examines human activity recognition (HAR) from the perspectives of data science and artificial intelligence. The primary objective is to present and evaluate the effectiveness of a multi-layer perceptron (MLP) model, concentrating on six different activity categories. We train and assess the MLP model using the UCI-HAR dataset, contrasting its results with those of convolutional neural networks (CNN). The MLP model shows competitive results, attaining an amazing 97% validation and testing accuracy, highlighting its efficiency for smaller datasets. An extensive study is carried out to assess the model's adaptation to a larger Motion Sense dataset using confusion matrices and cross-entropy, the model shows robustness with an accuracy of 89%. The MLP model performs admirably, demonstrating its capacity to pick up complex patterns. Results from comparative analysis with CNN are competitive, especially when dealing with smaller datasets. The suggested MLP model shows up as a practical and efficient way to advance HAR techniques. Its remarkable performance and versatility not only show its usefulness in real-world scenarios but also point to interesting directions for further study in the area of HAR.

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Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Cited by 28 publications

References 121 publications

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

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

Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning

Enhanced human activity recognition through deep multi-layer perceptron on the UCI-HAR dataset

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